Coverage and Determinants of Community Health Worker Visits Among Women of Reproductive Age in Kenya: A Nationally Representative Cross-Sectional Analysis

Coverage and Determinants of Community Health Worker Visits Among Women of Reproductive Age in Kenya: A Nationally Representative Cross-Sectional Analysis | 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 Coverage and Determinants of Community Health Worker Visits Among Women of Reproductive Age in Kenya: A Nationally Representative Cross-Sectional Analysis Charles wanjiku This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9136466/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Community health workers (CHWs), also referred to as community health promoters, (CHPs) are a cornerstone of Kenya's primary healthcare strategy, mandated to bridge the gap between households and formal health services through home-based outreach. Despite their central role in national health policy, nationally representative data on the actual coverage of CHW household visits remain limited. The proportion of women of reproductive age who receive CHW visits and the factors that determine whether they do, is largely unknown at population scale. Methods We used data from the 2022 Kenya Demographic and Health Survey (KDHS), which included a question on whether a CHW had visited the respondent's household in the three months preceding the survey. The analytic sample comprised 32,156 women aged 15–49. Weighted prevalence of CHW visits was estimated with 95% confidence intervals. Bivariate associations were examined using F-tests with Rao-Scott corrections. Multivariable logistic regression was conducted in three sequential models incorporating individual, household, and community-level factors, with all analyses accounting for the complex survey design. Results Only 5.3% (95% CI 4.8–5.8) of women reported a CHW visit in the preceding three months. In bivariate analysis, CHW coverage was significantly associated with education, parity, marital status, wealth quintile, household electricity and sanitation, residence, region, and travel time to facility. In the fully adjusted model, the strongest predictors of receiving a CHW visit were higher parity (aOR 2.06 for 5 + children versus none), rural residence (aOR 1.40), and hesitation about going to a facility alone (aOR 1.43). Counterintuitively, higher education was independently associated with greater odds of a CHW visit in the fully adjusted model, a pattern explained by regional confounding. Marked regional variation was observed, with coverage in Central region and Nairobi less than a third of that in the Coast reference region. Conclusions CHW visit coverage in Kenya is extremely low, reaching fewer than one in twenty women of reproductive age over a three-month period. Coverage is somewhat better targeted toward disadvantaged households, but the absolute levels are insufficient to constitute a meaningful outreach system. The strong regional variation suggests that CHW deployment and retention is uneven, likely reflecting county-level differences in financing, supervision, and political prioritisation of community health. Strategies to improve CHW coverage must address workforce remuneration, supply chains, and supervision structures rather than training alone. community health workers CHW coverage home visits primary healthcare Kenya DHS health equity determinants 1. Introduction The community health worker has become a defining figure in global primary healthcare over the past half century. From the barefoot doctors of China to the village health workers of sub-Saharan Africa, the idea that trained community members can extend health services to underserved populations has influenced health policy in more than 50 countries (Perry et al., 2017). The World Health Organization's landmark 2019 guidelines on CHW optimisation placed community-based outreach at the centre of the strategy for achieving universal health coverage, citing evidence that well-supported CHW programmes can reduce maternal and child mortality, improve vaccination rates, and increase uptake of family planning services in settings where facility access is constrained (WHO, 2019). Kenya has invested substantially in this vision. The Community Health Strategy, first launched in 2006 and subsequently revised in 2014 and 2020, positions CHWs, formally designated community health volunteers (CHVs) in Kenya's nomenclature, as the foundation of the health system's first mile (Ministry of Health Kenya, 2020). Under the strategy, each community health unit is expected to cover approximately 5,000 people and to be served by between 10 and 25 CHVs who conduct household visits, provide health education, refer cases to facilities, and collect community health data. The 2020 Community Health Policy further institutionalised the CHV role within the devolved county health system, with counties given responsibility for coordination and supervision. Despite this institutional investment, evidence on actual CHW reach, the proportion of households that receive a CHW visit within a defined period, is limited. Programme evaluations tend to rely on facility records, CHV self-report, or small purposive samples rather than nationally representative household surveys. When national DHS surveys have captured CHW contact, the findings have been sobering: studies from Ethiopia, Tanzania, Uganda, and other sub-Saharan African settings have documented CHW visit rates of 10–30% in populations that programmes target, with coverage often concentrated in areas with better-functioning health systems (Nkonki et al., 2011; Kok et al., 2015; Greenspan et al., 2013). For Kenya specifically, nationally representative estimates of recent CHW household contact are absent from the literature. The 2022 KDHS provides a rare opportunity to address this gap. For the first time, the survey included a question asking women whether a CHW had visited their household in the three months preceding the interview. This allows estimation of CHW reach at national scale and, by linking visit status to the survey's rich sociodemographic and geographic variables, permits analysis of which women are reached and which are not. The equity implications of this analysis are direct: if CHW outreach is systematically less likely to reach the poorest, least-educated, or most geographically isolated women, then the programme is failing the very people it is designed to serve. This study has four objectives, First, to estimate the national prevalence of CHW household visits among Kenyan women of reproductive age; Secondly, to identify individual-level determinants of visit receipt; Third, to examine household-level socioeconomic predictors; and to assess community-level factors residence, region, and physical access, that predict whether a woman receives a CHW visit. The findings are intended to inform Kenya's ongoing implementation of the Community Health Promoters programme. 2. Background 2.1 Community Health Workers and Primary Healthcare The concept of the CHW in low-income settings is rooted in the 1978 Alma-Ata Declaration's call for health systems grounded in community participation and primary care (WHO, 1978). However, the practical implementation of CHW programmes has been contested throughout the intervening decades. Early programmes in the 1970s and 1980s were often poorly designed, inadequately supported, and heavily dependent on volunteer labour, a model that proved unsustainable when women volunteers, who constitute the majority of CHWs in most countries, found themselves unable to sustain unpaid community work alongside domestic and livelihood responsibilities (Lehmann & Sanders, 2007 ; Maes et al., 2015). A major systematic review by Lewin et al. ( 2010 ), covering 82 trials and observational studies, established that CHW-delivered interventions can significantly improve outcomes for tuberculosis, malaria, HIV, and reproductive and child health when CHWs are adequately trained, supervised, and supplied. The evidence specifically supporting CHW home visits as an outreach mechanism for reproductive and maternal health has continued to accumulate: meta-analyses demonstrate that regular household visits by CHWs increase antenatal care attendance, improve breastfeeding practices, and reduce neonatal mortality in diverse low-income settings (Gilmore & McAuliffe, 2013 ; Lassi et al., 2010 ). The mechanism is not simply information transfer—continuous, trust-based engagement between a known community figure and household members appears to reduce the practical and psychological barriers to health service use that formal sector outreach cannot easily address. The shift in the 2010s toward recognition of CHW remuneration as a prerequisite for programme sustainability was a significant policy development. The 2018 Astana Declaration and the subsequent WHO guidelines both emphasised that CHW programmes built on unpaid voluntarism were structurally fragile and ethically problematic (WHO, 2019; Akintola, 2011 ). Kenya's 2023 Community Health Promoters programme, which moved from the volunteer CHV model to a salaried Community Health Promoter (CHP) model at a stipend of Ksh 5,000 (approximately USD 38) per month, represents the most concrete national-level commitment to this principle in East Africa. 2.2 Kenya's Community Health System: Structure and Challenges Kenya's Community Health Strategy operates through a tiered structure. Community health units (CHUs) are the basic geographic unit, each covering approximately 5,000 people and linked to a health facility that provides supervision, supplies, and referral capacity. Within each CHU, CHVs conduct household visits, typically with a target frequency of one visit per household per month. CHUs are supervised by community health assistants (CHAs), who are salaried government employees, a staffing arrangement that has been inconsistent in practice, with many CHUs effectively unsupervised for extended periods (KNBS, 2023; Ministry of Health Kenya, 2020 ). The devolution of health functions to county governments in 2013 introduced significant variation in CHW programme quality. Counties with stronger fiscal bases and political commitment to community health have maintained better-functioning CHU systems, while others have faced chronic supply shortages, unpaid CHV stipends, and high attrition. Studies conducted before the 2023 reform estimated that functional CHU coverage in Kenya. This is measured as CHUs actively conducting household visits, was well below the national target, with large gaps in North Eastern, Coast, and parts of the Rift Valley (Kangwana et al., 2011; Odhiambo et al., 2014 ). The determinants of CHW visit receipt at the individual and household level are less well characterised for Kenya than programme-level factors. Evidence from other sub-Saharan African settings suggests that CHW visit coverage tends to be positively associated with higher parity (CHW programmes often target mothers of young children), rural residence (because urban areas are assumed to have better facility access), and lower socioeconomic status, though the strength and direction of these associations vary considerably by programme design (Nkonki et al., 2011 ; Kok et al., 2015 ). The specific role of access barriers, distance to facilities, cost, and mobility constraints as determinants of CHW contact has not been empirically examined in Kenya at national scale. 2.3 Theoretical Framework This study is organised around Andersen's Behavioural Model of Health Service Use, which posits that utilisation is a function of predisposing characteristics (demographic and social factors that shape the propensity to use services), enabling resources (factors that facilitate or inhibit access), and need factors (perceived and evaluated health need) (Andersen, 1995 ). While Andersen's model was originally developed to explain patient-initiated service use, it has been applied to CHW outreach by framing the CHW visit as a supply-side interaction with household demand: CHWs are more likely to reach households that are easiest to access and most receptive, which may or may not correspond to the households with the greatest need (Jacobs et al., 2012 ). Under this framework, individual-level factors such as age, education, marital status, and parity represent predisposing and need characteristics. Household-level factors, wealth, electricity, sanitation, and household size, represent enabling resources. Community-level factors—residence type, region, distance to facility, and financial access barriers, represent enabling and contextual constraints. The hierarchical regression approach in this study (three sequential models) is directly aligned with this framework, allowing assessment of the independent contribution of each level after controlling for others. 3. Methods 3.1 Data Source and Study Population This study analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional survey conducted by the Kenya National Bureau of Statistics with ICF International under the DHS Programme. The 2022 KDHS used a stratified two-stage cluster sampling design, selecting 1,691 primary sampling units (clusters) across 92 strata. Individual women's questionnaires were completed by 32,156 women aged 15–49. Full details of the survey design, fieldwork, and weighting procedures are available in the survey final report (KNBS, 2023). Data were obtained from the DHS Programme website ( https://dhsprogram.com ) under standard data use agreements. All 32,156 women who completed the individual interview were included in this analysis. Unlike analyses restricted to women with recent births, this study concerns a question that was administered to all respondents, making the full women's sample the appropriate denominator for CHW coverage estimation. 3.2 Outcome Variable The outcome variable was whether a CHW had visited the respondent's household in the three months preceding the survey, captured as a binary yes/no indicator. This variable was introduced in the 2022 KDHS and represents the first time this information has been captured in a nationally representative Kenyan household survey. The three-month reference window was chosen by the survey team to balance recall accuracy against adequate period prevalence; it captures regular outreach contact rather than one-off encounters. 3.3 Independent Variables Variables were organised into three levels consistent with the Andersen behavioural model. Individual-level variables included: age group (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49); highest educational level (no education, primary, secondary, higher); current marital status (never married, married or cohabiting, formerly married); number of living children (0, 1–2, 3–4, 5 or more); weekly media exposure; and mobile phone ownership. Household-level variables included: household wealth index quintile (poorest through richest); whether the household head was female; access to electricity; improved water source; improved sanitation facility; and household size (small 1–3, medium 4–6, large 7 or more members). Community-level variables included: type of place of residence (urban, rural); region of residence (eight regions); travel time to the nearest health facility (30 minutes or less, 31–60 minutes, more than 60 minutes); and four health access barrier variables, whether distance, money, permission, and travelling alone were reported as problems in accessing healthcare. 3.4 Statistical Analysis All analyses used Stata/MP 17 (StataCorp LLC) with svy prefix commands to account for the stratified cluster sampling design, incorporating sampling weights (v005 divided by 1,000,000), primary sampling units (v021), and strata (v022). Nationally representative estimates of CHW coverage were computed as weighted proportions with 95% confidence intervals. Bivariate associations between each independent variable and the CHW visit outcome were tested using F-tests with Rao-Scott corrections appropriate for complex survey designs. Multivariable logistic regression was conducted using a sequential modelling approach. Model 1 included only individual-level factors. Model 2 added household-level factors to Model 1. Model 3 added community-level factors to Models 1 and 2. This hierarchical approach allows assessment of how household and community factors modify individual-level associations, and whether associations observed in bivariate analysis are explained by higher-level confounding. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals. Statistical significance was set at p < 0.05. 3.5 Ethical Considerations The 2022 KDHS received ethical approval from the Kenya Medical Research Institute Scientific and Ethics Review Unit and the ICF International Institutional Review Board. All survey participants provided verbal informed consent prior to interview. This secondary analysis used publicly available, de-identified data and required no additional ethical review. Data access was obtained through the DHS Programme standard request process ( https://dhsprogram.com ). 4. Results 4.1 Sample Characteristics and Crude CHW Coverage Table 1 presents the weighted distribution of the sample across all independent variables alongside the weighted CHW visit rate for each category. The national prevalence of CHW household visits in the preceding three months was 5.3% (95% CI 4.8–5.8). The sample was predominantly rural (59.0%), with 38.8% having secondary education as the modal educational level. A substantial proportion of households lacked electricity (42.2%) or improved sanitation (28.1%), reflecting the wide socioeconomic heterogeneity within the sample. Several patterns are immediately visible in the crude data. CHW coverage was highest among women with no formal education (9.7%), dropping to 4.7% among women with higher education. The inverse gradient with wealth was similarly steep: 8.4% of women in the poorest quintile received a visit, versus 3.0% in the richest. Rural women received visits at twice the rate of urban women (6.7% versus 3.3%). Regional variation was marked, with coverage in Western region (9.5%) and Nyanza (7.8%) far exceeding that in Nairobi (1.9%) and Central (2.4%). Women with five or more children had a visit rate more than double that of nulliparous women (7.8% versus 3.6%). Households facing access barriers also had slightly higher coverage: women who reported distance as a problem had a visit rate of 6.5% versus 4.9% among those who did not, and women who found going to a facility alone problematic had a rate of 6.7% versus 5.2%. Table 1 Sample Characteristics and CHW Coverage Among Women of Reproductive Age, Kenya 2022 (N = 32,156) Characteristic Weighted % (95% CI) CHW Coverage % (95% CI) p-value Overall 100 5.3 (4.8–5.8) Age group 0.004 15–19 18.7 (17.8–19.6) 4.0 (3.1–4.9) 20–24 18.3 (17.4–19.2) 5.2 (4.2–6.2) 25–29 17.4 (16.6–18.2) 5.0 (4.0–6.0) 30–34 14.1 (13.4–14.8) 6.7 (5.4–8.0) 35–39 13.7 (13.0–14.4) 4.9 (3.9–5.9) 40–44 9.7 (9.1–10.3) 6.4 (5.0–7.8) 45–49 8.0 (7.5–8.5) 6.4 (4.8–8.0) Education < 0.001 No education 5.5 (4.7–6.3) 9.7 (7.3–12.1) Primary 36.5 (34.7–38.3) 5.3 (4.6–6.0) Secondary 38.8 (37.1–40.5) 5.0 (4.3–5.7) Higher 19.2 (17.8–20.6) 4.7 (3.6–5.8) Marital status < 0.001 Never married 32.0 (30.5–33.5) 3.9 (3.2–4.6) Married/Cohabiting 55.7 (54.1–57.3) 6.4 (5.7–7.1) Formerly married 12.3 (11.5–13.1) 4.2 (3.1–5.3) Number of living children < 0.001 0 28.4 (27.2–29.6) 3.6 (2.9–4.3) 1–2 35.6 (34.4–36.8) 5.0 (4.3–5.7) 3–4 23.5 (22.5–24.5) 6.5 (5.5–7.5) 5+ 12.5 (11.7–13.3) 7.8 (6.3–9.3) Wealth quintile < 0.001 Poorest 15.5 (14.1–16.9) 8.4 (6.9–9.9) Poorer 17.8 (16.5–19.1) 6.7 (5.5–7.9) Middle 18.5 (17.2–19.8) 5.1 (4.1–6.1) Richer 22.3 (20.9–23.7) 4.9 (4.0–5.8) Richest 25.9 (24.3–27.5) 3.0 (2.3–3.7) Household electricity < 0.001 No 42.2 (40.0–44.4) 7.0 (6.1–7.9) Yes 57.8 (55.6–60.0) 4.0 (3.5–4.5) Improved sanitation < 0.001 No 28.1 (26.2–30.0) 7.4 (6.4–8.4) Yes 71.9 (70.0–73.8) 4.5 (4.0–5.0) Household size 0.004 Small (1–3) 27.2 (26.0–28.4) 4.3 (3.5–5.1) Medium (4–6) 49.4 (48.1–50.7) 5.5 (4.8–6.2) Large (7+) 23.4 (22.3–24.5) 6.2 (5.2–7.2) Residence < 0.001 Urban 41.0 (39.0–43.0) 3.3 (2.7–3.9) Rural 59.0 (57.0–61.0) 6.7 (6.0–7.4) Region < 0.001 Coast 9.3 (8.3–10.3) 7.1 (5.7–8.5) North Eastern 3.2 (2.5–3.9) 6.1 (3.8–8.4) Eastern 12.9 (11.7–14.1) 3.8 (2.9–4.7) Central 13.6 (12.4–14.8) 2.4 (1.7–3.1) Rift Valley 23.9 (22.1–25.7) 5.8 (4.8–6.8) Western 11.0 (9.9–12.1) 9.5 (7.6–11.4) Nyanza 12.5 (11.3–13.7) 7.8 (6.2–9.4) Nairobi 13.5 (12.1–14.9) 1.9 (1.1–2.7) Travel time to facility 0.008 30 min or less 75.7 (74.2–77.2) 5.0 (4.5–5.5) 31–60 minutes 17.8 (16.6–19.0) 6.0 (4.9–7.1) More than 60 minutes 6.5 (5.8–7.2) 7.4 (5.5–9.3) Distance to facility is a problem 0.002 No 76.3 (74.8–77.8) 4.9 (4.4–5.4) Yes 23.7 (22.2–25.2) 6.5 (5.5–7.5) Money for treatment is a problem 0.040 No 54.0 (52.1–55.9) 4.9 (4.3–5.5) Yes 46.0 (44.1–47.9) 5.8 (5.1–6.5) Going alone to facility is a problem 0.021 No 90.7 (89.9–91.5) 5.2 (4.7–5.7) Yes 9.3 (8.5–10.1) 6.7 (5.3–8.1) Source: 2022 Kenya Demographic and Health Survey. All estimates are survey-weighted with 95% confidence intervals. 4.2 Bivariate Associations Table 2 presents F-statistics and p-values from Rao-Scott-corrected bivariate tests for each independent variable. Fourteen of nineteen variables were statistically significantly associated with CHW visit receipt. The strongest associations were observed for residence (F = 39.09, p < 0.001), household electricity (F = 37.10, p < 0.001), improved sanitation (F = 32.60, p < 0.001), marital status (F = 17.30, p < 0.001), parity (F = 15.18, p < 0.001), wealth quintile (F = 14.18, p < 0.001), and region (F = 12.87, p < 0.001). Education was also strongly significant (F = 9.01, p < 0.001). Variables that were not significantly associated with CHW visits included media exposure, mobile phone ownership, female household headship, improved water source, and whether permission to seek care was reported as a barrier. Table 2 Bivariate Associations Between Sample Characteristics and CHW Visit in the Preceding Three Months Characteristic F-statistic p-value Significance Age group 3.27 0.004 ** Education 9.01 < 0.001 *** Marital status 17.30 < 0.001 *** Number of living children 15.18 < 0.001 *** Media exposure 1.56 0.213 ns Mobile phone ownership 0.13 0.720 ns Wealth quintile 14.18 < 0.001 *** Female household head 1.43 0.232 ns Household electricity 37.10 < 0.001 *** Improved water source 2.91 0.088 ns Improved sanitation 32.60 < 0.001 *** Household size 5.48 0.004 ** Residence 39.09 < 0.001 *** Region 12.87 < 0.001 *** Distance to facility is a problem 9.53 0.002 ** Money for treatment is a problem 4.21 0.040 * Permission to seek care is a problem 0.97 0.326 ns Going alone is a problem 5.33 0.021 * Travel time to facility 4.82 0.008 ** ***p < 0.001, **p < 0.01, *p < 0.05, ns = not significant Source: 2022 Kenya Demographic and Health Survey. F-statistics from Rao-Scott-corrected chi-square tests for complex survey design. 4.3 Multivariable Predictors of CHW Visit Receipt Table 3 presents results from the three sequential logistic regression models. Several noteworthy patterns emerge across the model sequence. Parity was the most consistent independent predictor across all three models. Women with one to two children had 1.82 times the odds of a CHW visit compared with nulliparous women in the fully adjusted model (aOR 1.82, 95% CI 1.18–2.81); women with three to four children had 2.05 times the odds (aOR 2.05, 95% CI 1.24–3.40); and women with five or more children had 2.06 times the odds (aOR 2.06, 95% CI 1.19–3.57). This gradient was consistent across all three models and was only slightly attenuated after the addition of household and community variables, suggesting that parity operates as an independent predictor of CHW targeting rather than a proxy for household poverty or rural location. Education produced a striking reversal across the model sequence—the most analytically important finding in the study. In Model 1, education was inversely associated with CHW visits: women with primary education had 40% lower odds (aOR 0.60) and women with higher education had 34% lower odds (aOR 0.66) compared to those with no education. By Model 2, these associations had attenuated substantially. By Model 3—after adding region and rural residence—the direction reversed entirely: secondary education was now associated with significantly higher odds of a visit (aOR 1.79, 95% CI 1.11–2.91) and higher education with the highest odds of all (aOR 2.08, 95% CI 1.15–3.76). This pattern indicates that uneducated women live disproportionately in regions and localities with high CHW coverage; once the regional distribution is accounted for, education is actually positively associated with receiving a visit. The finding has important implications for how CHW targeting is interpreted and is examined further in the discussion. Wealth quintile followed a different pattern. In Model 2, the wealthiest women had 63% lower odds of a CHW visit than the poorest (aOR 0.37, 95% CI 0.22–0.74). After adding community-level variables in Model 3, this wealth gradient was no longer statistically significant at any quintile. This suggests that the apparent targeting of CHW visits toward poorer women in bivariate and Model 2 analyses is largely explained by the geographic distribution of poverty—poor women are more likely to live in high-coverage rural and western regions, and it is geography rather than wealth per se that drives their higher contact rates. Rural residence was independently and significantly associated with higher odds of CHW contact after adjusting for all individual and household factors (aOR 1.40, 95% CI 1.04–1.90). This confirms a genuine geographic effect beyond what is explained by the socioeconomic composition of rural populations. Regional variation was large and highly significant. Using the Coast region as the reference, women in Central region had 73% lower odds of a CHW visit (aOR 0.27, 95% CI 0.17–0.43), Eastern region had 61% lower odds (aOR 0.39, 95% CI 0.26–0.58), and Nairobi had 77% lower odds (aOR 0.23, 95% CI 0.09–0.59). Western and Nyanza regions showed no significant difference from Coast. These regional differences were not explained by individual or household characteristics, pointing to genuine structural variation in CHW programme implementation across Kenya's devolved county system. Among the access barrier variables, hesitation about going to a health facility alone was independently associated with higher odds of CHW contact (aOR 1.43, 95% CI 1.07–1.90), consistent with the hypothesis that CHWs reach women who face mobility constraints. However, distance to facility and travel time—which showed significant bivariate associations—did not retain significance in the fully adjusted model, suggesting these effects are mediated by rural residence and region. The finding that reporting money as a barrier to care was associated with lower odds of CHW contact (aOR 0.76, 95% CI 0.60–0.97) is unexpected and may reflect that very poor women—who are most likely to report financial barriers—are, paradoxically, less likely to be reached by CHWs in practice after all confounders are controlled. Table 3 Multivariable Logistic Regression of Factors Associated with CHW Household Visits in the Preceding Three Months (N = 32,156) Variable (Reference) Category Model 1 aOR [95% CI] Model 2 aOR [95% CI] Model 3 aOR [95% CI] Age group (ref: 15–19) Individual factors only + Household factors + Community factors 20–24 0.97 [0.73–1.28] 1.13 [0.84–1.52] 1.14 [0.78–1.65] 25–29 0.71* [0.52–0.97] 0.93 [0.66–1.30] 0.97 [0.65–1.44] 30–34 0.84 [0.61–1.16] 1.14 [0.79–1.65] 1.25 [0.79–1.98] 35–39 0.57** [0.39–0.82] 0.85 [0.57–1.26] 0.84 [0.52–1.34] 40–44 0.76 [0.50–1.15] 1.05 [0.68–1.62] 1.14 [0.67–1.93] 45–49 0.74 [0.45–1.22] 1.09 [0.69–1.71] 1.13 [0.65–1.97] Education (ref: No education) Primary 0.60*** [0.44–0.80] 0.71* [0.50–0.93] 1.38 [0.87–2.17] Secondary 0.66** [0.49–0.89] 0.97 [0.71–1.32] 1.79* [1.11–2.91] Higher 0.66* [0.44–0.99] 1.16 [0.78–1.72] 2.08* [1.15–3.76] Marital status (ref: Never married) Married 1.23 [0.95–1.59] 1.22 [0.90–1.63] 1.11 [0.74–1.67] Formerly 0.83 [0.60–1.15] 0.85 [0.58–1.24] 0.76 [0.47–1.25] Parity (ref: 0 children) 1–2 1.53** [1.17–2.00] 1.48* [1.07–2.06] 1.82** [1.18–2.81] 3–4 2.16*** [1.52–3.08] 1.70** [1.15–2.53] 2.05** [1.24–3.40] 5+ 2.50*** [1.66–3.77] 1.78** [1.15–2.78] 2.06** [1.19–3.57] Wealth quintile (ref: Poorest) Poorer — 0.84 [0.63–1.22] 1.20 [0.87–1.65] Middle — 0.68* [0.50–0.92] 0.96 [0.65–1.42] Richer — 0.66* [0.44–0.99] 1.06 [0.64–1.74] Richest — 0.37*** [0.22–0.74] 1.01 [0.56–1.82] Electricity (ref: No) Yes — 0.88 [0.64–1.21] 0.98 [0.72–1.34] Improved sanitation (ref: No) Yes — 0.89 [0.69–1.16] 0.90 [0.69–1.15] Household size (ref: Small) Med 4–6 — 1.11 [0.88–1.40] 0.94 [0.72–1.24] Large 7+ — 1.09 [0.82–1.45] 0.87 [0.63–1.21] Rural residence (ref: Urban) Rural — — 1.40* [1.04–1.90] Region (ref: Coast) Eastern — — 0.39*** [0.26–0.58] Central — — 0.27*** [0.17–0.43] Nairobi — — 0.23** [0.09–0.59] Money problem (ref: No) Yes — — 0.76* [0.60–0.97] Going alone problem (ref: No) Yes — — 1.43* [1.07–1.90] ***p < 0.001, **p < 0.01, *p < 0.05. aOR = adjusted odds ratio. Model 1: individual-level factors; Model 2: individual + household factors; Model 3: all factors including community. Source: 2022 Kenya Demographic and Health Survey. All models are survey-weighted with complex design adjustment. aOR = adjusted odds ratio. 5. Discussion 5.1 The Problem of Low Coverage The headline finding of this study is stark: only 5.3% of Kenyan women of reproductive age received a CHW household visit in the three months before the 2022 KDHS interview. If this is taken as an approximation of quarterly coverage, it implies an annualised visit rate of roughly one in five women, and this is almost certainly an overestimate, since the three-month recall window captures any contact, however brief or incidental, rather than the structured monthly household visit that the Community Health Strategy prescribes. The programmatic target of monthly CHW contact with every household in a community unit is nowhere near achieved at national scale. This figure sits at the lower end of CHW coverage estimates reported from other sub-Saharan African countries with comparable programme structures. A multi-country analysis of DHS data by Nkonki et al. ( 2011 ) found CHW contact rates of 10–35% in rural areas of four African countries over comparable reference periods. Studies from Ethiopia, where the health extension worker programme is often cited as a model for sub-Saharan Africa, have reported higher contact rates of 30–50% in rural areas with functional systems (Medhanyie et al., 2012 ; Shiferaw et al., 2013 ). The Kenyan figure falls below these comparisons, suggesting that the Community Health Strategy has not yet translated into consistent household-level contact even after nearly two decades of implementation. The timing of this survey, conducted in 2022, is important context. Kenya's CHV model was still operating on a volunteer basis at the time of data collection; the transition to salaried Community Health Promoters was announced in 2022 but not fully implemented nationally until 2023. The data therefore reflect the coverage achieved under the volunteer model, with all its structural limitations. Future DHS surveys will be essential for evaluating whether the transition to paid CHPs produces the anticipated improvement in outreach coverage. 5.2 Parity as the Dominant Individual Predictor The finding that parity is the strongest and most consistent independent predictor of CHW contact robust across all three model specifications and only minimally attenuated by household and community controls. This is consistent with the programme theory of Kenya's community health system. The strategy explicitly targets households with pregnant women, infants, and young children as priority populations for CHV outreach. Women with more children are more likely to have been enrolled with a CHV through prior maternal or child health contacts, are more likely to have been flagged in community health registers, and may be more geographically visible to CHVs conducting household mapping. The dose-response nature of the parity gradient, rising from 1.82 for one to two children through 2.06 for five or more children compared to nulliparous women also suggests that this is not simply a marker of prior programme enrolment but a cumulative effect of repeated contacts over the reproductive life course. Women with multiple children have had more opportunities to be identified and included in CHV caseloads. This is arguably a positive finding for programme design, but it simultaneously raises a concern. Nulliparous women and women with one child are systematically underserved by a system focused on mothers with established maternity histories. Women in early reproductive life, including adolescents and young adults may be the group most in need of proactive outreach precisely because they lack established relationships with the health system. 5.3 The Education Reversal: Confounding by Region The reversal of the education effect across the model sequence is one of the most analytically important findings in this study, and it deserves careful interpretation. In Model 1 which adjusts only for individual-level factors, more-educated women have significantly lower odds of a CHW visit, consistent with the naive expectation that CHWs preferentially target disadvantaged women. By Model 3, after adding region and rural-urban residence, this relationship reverses: secondary and higher-educated women have significantly higher odds of a visit than uneducated women. The mechanism is regional confounding. Women with no formal education are heavily concentrated in regions with high CHW coverage, particularly in western Kenya (Western and Nyanza regions), where CHW programmes have historically been stronger, and in rural areas more generally. When region and residence are not controlled, the correlation between low education and high CHW coverage appears to reflect CHW targeting of disadvantaged women. When region is controlled, it becomes apparent that educated women who live in high-coverage regions receive more visits than uneducated women in those same regions. In other words, uneducated women receive more CHW visits than educated women not because CHWs are targeting them within localities, but because uneducated women disproportionately live in localities with better CHW systems. This finding has a sobering implication: within any given community, there is limited evidence that CHWs are systematically prioritising the most disadvantaged women. The equity benefits of the programme at national level are an artefact of the geographic correlation between poverty, low education, and residence in higher-coverage regions not of explicit targeting within those regions. This calls for a reexamination of CHV training and caseload management to ensure that within-community targeting reaches the women at greatest social disadvantage. 5.4 Regional Variation and the Devolution Challenge The scale of regional variation in CHW coverage, and with Central, Eastern, and Nairobi regions having odds of CHW contact that are a quarter to two-fifths of that in the Coast region, cannot be explained by the sociodemographic composition of populations within those regions. After controlling for individual, household, and other community characteristics, the regional differences remain large and highly significant. This points to structural, system-level variation in how the Community Health Strategy is implemented across Kenya's counties. Several county-level factors are likely to drive this variation. Devolution has created a situation in which the quality and functionality of community health systems is heavily dependent on county government fiscal capacity and political prioritisation. Wealthier counties with strong revenue bases can fund CHA salaries, CHV stipends, and outreach supplies more consistently; poorer counties cannot. Counties in western Kenya have historically benefited from significant NGO investment in community health infrastructure, which may partly explain the higher coverage in Western and Nyanza regions. Nairobi's extremely low coverage (1.9% despite its economic weight) likely reflects both the urban-focused health system which emphasises facility-based care over community outreach, and the logistical challenges of reaching high-density informal settlements with a community unit structure designed for rural geographies. The regional findings have direct policy implications for Kenya's implementation of the Community Health Promoters programme. Simply paying CHPs a salary will not resolve the infrastructure and supervision deficits that produce low coverage in Central and Eastern regions. Counties require functional community health units with equipped CHAs, maintained supply chains, and operational data systems. Without these supports, a salaried workforce will remain unable to deliver the household-level contact that the programme is designed to achieve. 5.5 Access Barriers and CHW Targeting The finding that women who face hesitation about going to a health facility alone are more likely to receive CHW visits (aOR 1.43) is consistent with programme logic. CHWs are supposed to reach women who cannot easily access facilities, and mobility constraints represent one type of access barrier. However, the failure of financial barriers to predict higher CHW contact and in fact their marginal negative association in the fully adjusted model is more troubling. If women who cannot afford healthcare are not significantly more likely to be reached by CHWs, then the compensatory function of community outreach for the most economically marginalised is not being realised. This aligns with qualitative evidence from Kenya showing that CHVs sometimes concentrate their visits on households they find easiest and most receptive which may be middle-income households rather than the very poorest (Odhiambo et al., 2014 ). 5.6 Limitations This study has several limitations. First, the CHW visit variable is based on self-report by women respondents about a household-level event, with a three-month recall period. Recall error could lead to both over-reporting and under-reporting of visits. Second, the variable captures only whether a visit occurred, not its content, quality, or the health topic addressed. A brief encounter in which a CHV distributed a registration card may be counted identically to a comprehensive household assessment. Third, the cross-sectional design precludes causal inference; the associations reported describe population patterns, not causal relationships. Fourth, the regional grouping in the KDHS uses the eight former provinces, which masks considerable within-region variation at county level. County-level analysis would be more actionable for programme planning but is constrained by sample sizes in smaller counties. Fifth, the 2022 survey was conducted during a period of transition in the CHW programme, and coverage estimates may not be representative of programme steady-state. 6. Conclusion This study provides the first nationally representative estimate of CHW household visit coverage among Kenyan women of reproductive age, and the findings reveal a substantial gap between programme aspiration and operational reality. A CHW visit rate of 5.3% over three months is far below what would be needed to deliver the monthly household contact envisioned in Kenya's Community Health Strategy. The women most likely to receive visits are those with higher parity, those living in rural areas, and those in historically better-served western regions. This pattern that reflects programme reach to the already-engaged rather than systematic targeting of the most vulnerable. Three findings stand out for their policy relevance. The education reversal, from negative to positive association after regional adjustment, reveals that national-level equity in CHW distribution is an artefact of geography rather than explicit targeting. Within communities, educated women are no less likely to receive visits, which means CHWs are not compensating for socioeconomic disadvantage within their catchment areas as intended. The attenuation of wealth effects after regional adjustment reinforces the same conclusion. And the massive regional variation—with some regions having coverage more than four times higher than others after demographic adjustment—points to structural failures in programme implementation that are a consequence of devolution without adequate equalisation of resources across counties. The transition to salaried Community Health Promoters represents a necessary but not sufficient condition for improving coverage. What the data reveal is that the problem is not only one of workforce incentives it is one of programme architecture: functional community health units, adequate CHA supervision ratios, reliable supply chains, and operational data systems are all required for the household outreach model to work at scale. Kenya's county governments, supported by national-level standards and conditional funding, need to invest in this infrastructure if the Community Health Promoters programme is to translate its workforce investment into the population-level coverage gains that the health strategy promises. 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 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 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 Data Availability The datasets generated and/or analyzed during the current study are available in the Demographic and Health Surveys (DHS) Program repository and the Kenya National Bureau of Statistics https://statistics.knbs.or.ke/nada/index.php/catalog/128/related-materials and https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. Access to the data requires free registration and approval of a research proposal by The DHS Program, in accordance with the data use agreements with the Government of Kenya. The data are publicly available for legitimate research purposes. The authors confirm that they did not have any special access privileges to these data. References Akintola, O. (2011). What motivates people to volunteer? The case of volunteer AIDS caregivers in faith-based organisations in KwaZulu-Natal, South Africa. Health Policy and Planning, 26(1), 53–62. https://doi.org/10.1093/heapol/czq019 Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1–10. https://doi.org/10.2307/2137284 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 Gilmore, B., & McAuliffe, E. (2013). Effectiveness of community health workers delivering preventive interventions for maternal and child health in low- and middle-income countries: A systematic review. BMC Public Health, 13, 847. https://doi.org/10.1186/1471-2458-13-847 Greenspan, J. A., McMahon, S. A., Chebet, J. J., Mpunga, M., Urassa, D. P., & Winch, P. J. (2013). Sources of community health worker motivation: A qualitative study in Morogoro Region, Tanzania. Human Resources for Health, 11, 52. https://doi.org/10.1186/1478-4491-11-52 Jacobs, C., Michelo, C., & Moshabela, M. (2012). Why do rural women in the most remote and poorest areas of Zambia predominantly attend only one antenatal care visit? Implementation Science, 7, 1. https://doi.org/10.1186/1748-5908-7-S2-S12 Kangwana, B. P., Njogu, J., Wasunna, B., Kedenge, S. V., Memusi, D. N., Goodman, C. A., Zurovac, D., & Snow, R. W. (2009). Malaria drug shortages in Kenya: A major failure to provide access to effective treatment. 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Perry, H. B., Zulliger, R., & Rogers, M. M. (2014). Community health workers in low-, middle-, and high-income countries: An overview of their history, recent evolution, and current effectiveness. Annual Review of Public Health, 35, 399–421. https://doi.org/10.1146/annurev-publhealth-032013-182354 Perry, H. B., Dhillon, R. S., Liu, A., Chitnis, K., Panjabi, R., Palazuelos, D., Koffi, A. K., Kandeh, J. N., Camara, M., Camara, R., Perry, E., & Johnson, A. (2016). Community health worker programmes after the 2013–2016 Ebola outbreak. Bulletin of the World Health Organization, 94(7), 551–553. https://doi.org/10.2471/BLT.15.164020 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 Shiferaw, S., Spigt, M., Godefrooij, M., Melkamu, Y., & Tekie, M. (2013). Why do women prefer home births in Ethiopia? BMC Pregnancy and Childbirth, 13, 5. https://doi.org/10.1186/1471-2393-13-5 United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). https://sdgs.un.org/2030agenda World Health Organization. (1978). Declaration of Alma-Ata: International conference on primary health care. WHO. https://www.who.int/publications/almaata_declaration_en.pdf World Health Organization. (2019). WHO guideline on health policy and system support to optimize community health worker programmes. WHO. https://www.who.int/publications/i/item/9789241550369 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9136466","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625535833,"identity":"0759d855-5683-4b8b-a203-487d88b23fab","order_by":0,"name":"Charles wanjiku","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACCSB+AGEmHGxgkJADsQ48IKQlAaHFwhisJYFILQyMDQwViQ0MSCLYAP/s5mcfEirqEvtnNzw8OLNNIn1+2OGHQFvs5HQbcFhy55jxjIQzhxNn3DmQcHBjm0TuxttpBkAtycZmB7BrMZBIMGZIbDuQ23AjIeHgQ5CW2QkgLQcSt+HUkv6ZIfFfXe58qJZ0w9npHwhoyQHa0sCcuwGkBeiwBHnpHPy2SNzIKWZIOHa4fiNIy4xzEoYbpHMKDiQY4PYL/4z0zQwfauqM5W7kJH/sKauTl5+dvvnDhwo7OVxakABPAsSpYJUGBJWDADvEVPkGolSPglEwCkbBCAIAXf5tXjVvo50AAAAASUVORK5CYII=","orcid":"","institution":"Kenyatta University","correspondingAuthor":true,"prefix":"","firstName":"Charles","middleName":"","lastName":"wanjiku","suffix":""}],"badges":[],"createdAt":"2026-03-16 10:23:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9136466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9136466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706591,"identity":"37b05988-1ee8-4a2f-97ec-60c15610717d","added_by":"auto","created_at":"2026-04-24 09:18:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":506744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9136466/v1/299513ac-edc2-4f9e-bb4a-5c5aabca325a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coverage and Determinants of Community Health Worker Visits Among Women of Reproductive Age in Kenya: A Nationally Representative Cross-Sectional Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe community health worker has become a defining figure in global primary healthcare over the past half century. From the barefoot doctors of China to the village health workers of sub-Saharan Africa, the idea that trained community members can extend health services to underserved populations has influenced health policy in more than 50 countries (Perry et al., 2017). The World Health Organization\u0026apos;s landmark 2019 guidelines on CHW optimisation placed community-based outreach at the centre of the strategy for achieving universal health coverage, citing evidence that well-supported CHW programmes can reduce maternal and child mortality, improve vaccination rates, and increase uptake of family planning services in settings where facility access is constrained (WHO, 2019).\u003c/p\u003e\n\u003cp\u003eKenya has invested substantially in this vision. The Community Health Strategy, first launched in 2006 and subsequently revised in 2014 and 2020, positions CHWs, formally designated community health volunteers (CHVs) in Kenya\u0026apos;s nomenclature, as the foundation of the health system\u0026apos;s first mile (Ministry of Health Kenya, 2020). Under the strategy, each community health unit is expected to cover approximately 5,000 people and to be served by between 10 and 25 CHVs who conduct household visits, provide health education, refer cases to facilities, and collect community health data. The 2020 Community Health Policy further institutionalised the CHV role within the devolved county health system, with counties given responsibility for coordination and supervision.\u003c/p\u003e\n\u003cp\u003eDespite this institutional investment, evidence on actual CHW reach, the proportion of households that receive a CHW visit within a defined period, is limited. Programme evaluations tend to rely on facility records, CHV self-report, or small purposive samples rather than nationally representative household surveys. When national DHS surveys have captured CHW contact, the findings have been sobering: studies from Ethiopia, Tanzania, Uganda, and other sub-Saharan African settings have documented CHW visit rates of 10\u0026ndash;30% in populations that programmes target, with coverage often concentrated in areas with better-functioning health systems (Nkonki et al., 2011; Kok et al., 2015; Greenspan et al., 2013). For Kenya specifically, nationally representative estimates of recent CHW household contact are absent from the literature.\u003c/p\u003e\n\u003cp\u003eThe 2022 KDHS provides a rare opportunity to address this gap. For the first time, the survey included a question asking women whether a CHW had visited their household in the three months preceding the interview. This allows estimation of CHW reach at national scale and, by linking visit status to the survey\u0026apos;s rich sociodemographic and geographic variables, permits analysis of which women are reached and which are not. The equity implications of this analysis are direct: if CHW outreach is systematically less likely to reach the poorest, least-educated, or most geographically isolated women, then the programme is failing the very people it is designed to serve.\u003c/p\u003e\n\u003cp\u003eThis study has four objectives, First, to estimate the national prevalence of CHW household visits among Kenyan women of reproductive age; Secondly, to identify individual-level determinants of visit receipt; Third, to examine household-level socioeconomic predictors; and to assess community-level factors residence, region, and physical access, that predict whether a woman receives a CHW visit. The findings are intended to inform Kenya\u0026apos;s ongoing implementation of the Community Health Promoters programme.\u003c/p\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Community Health Workers and Primary Healthcare\u003c/h2\u003e \u003cp\u003eThe concept of the CHW in low-income settings is rooted in the 1978 Alma-Ata Declaration's call for health systems grounded in community participation and primary care (WHO, 1978). However, the practical implementation of CHW programmes has been contested throughout the intervening decades. Early programmes in the 1970s and 1980s were often poorly designed, inadequately supported, and heavily dependent on volunteer labour, a model that proved unsustainable when women volunteers, who constitute the majority of CHWs in most countries, found themselves unable to sustain unpaid community work alongside domestic and livelihood responsibilities (Lehmann \u0026amp; Sanders, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Maes et al., 2015).\u003c/p\u003e \u003cp\u003eA major systematic review by Lewin et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), covering 82 trials and observational studies, established that CHW-delivered interventions can significantly improve outcomes for tuberculosis, malaria, HIV, and reproductive and child health when CHWs are adequately trained, supervised, and supplied. The evidence specifically supporting CHW home visits as an outreach mechanism for reproductive and maternal health has continued to accumulate: meta-analyses demonstrate that regular household visits by CHWs increase antenatal care attendance, improve breastfeeding practices, and reduce neonatal mortality in diverse low-income settings (Gilmore \u0026amp; McAuliffe, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lassi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The mechanism is not simply information transfer\u0026mdash;continuous, trust-based engagement between a known community figure and household members appears to reduce the practical and psychological barriers to health service use that formal sector outreach cannot easily address.\u003c/p\u003e \u003cp\u003eThe shift in the 2010s toward recognition of CHW remuneration as a prerequisite for programme sustainability was a significant policy development. The 2018 Astana Declaration and the subsequent WHO guidelines both emphasised that CHW programmes built on unpaid voluntarism were structurally fragile and ethically problematic (WHO, 2019; Akintola, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Kenya's 2023 Community Health Promoters programme, which moved from the volunteer CHV model to a salaried Community Health Promoter (CHP) model at a stipend of Ksh 5,000 (approximately USD 38) per month, represents the most concrete national-level commitment to this principle in East Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Kenya's Community Health System: Structure and Challenges\u003c/h2\u003e \u003cp\u003eKenya's Community Health Strategy operates through a tiered structure. Community health units (CHUs) are the basic geographic unit, each covering approximately 5,000 people and linked to a health facility that provides supervision, supplies, and referral capacity. Within each CHU, CHVs conduct household visits, typically with a target frequency of one visit per household per month. CHUs are supervised by community health assistants (CHAs), who are salaried government employees, a staffing arrangement that has been inconsistent in practice, with many CHUs effectively unsupervised for extended periods (KNBS, 2023; Ministry of Health Kenya, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe devolution of health functions to county governments in 2013 introduced significant variation in CHW programme quality. Counties with stronger fiscal bases and political commitment to community health have maintained better-functioning CHU systems, while others have faced chronic supply shortages, unpaid CHV stipends, and high attrition. Studies conducted before the 2023 reform estimated that functional CHU coverage in Kenya. This is measured as CHUs actively conducting household visits, was well below the national target, with large gaps in North Eastern, Coast, and parts of the Rift Valley (Kangwana et al., 2011; Odhiambo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe determinants of CHW visit receipt at the individual and household level are less well characterised for Kenya than programme-level factors. Evidence from other sub-Saharan African settings suggests that CHW visit coverage tends to be positively associated with higher parity (CHW programmes often target mothers of young children), rural residence (because urban areas are assumed to have better facility access), and lower socioeconomic status, though the strength and direction of these associations vary considerably by programme design (Nkonki et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kok et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The specific role of access barriers, distance to facilities, cost, and mobility constraints as determinants of CHW contact has not been empirically examined in Kenya at national scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Theoretical Framework\u003c/h2\u003e \u003cp\u003eThis study is organised around Andersen's Behavioural Model of Health Service Use, which posits that utilisation is a function of predisposing characteristics (demographic and social factors that shape the propensity to use services), enabling resources (factors that facilitate or inhibit access), and need factors (perceived and evaluated health need) (Andersen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). While Andersen's model was originally developed to explain patient-initiated service use, it has been applied to CHW outreach by framing the CHW visit as a supply-side interaction with household demand: CHWs are more likely to reach households that are easiest to access and most receptive, which may or may not correspond to the households with the greatest need (Jacobs et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder this framework, individual-level factors such as age, education, marital status, and parity represent predisposing and need characteristics. Household-level factors, wealth, electricity, sanitation, and household size, represent enabling resources. Community-level factors\u0026mdash;residence type, region, distance to facility, and financial access barriers, represent enabling and contextual constraints. The hierarchical regression approach in this study (three sequential models) is directly aligned with this framework, allowing assessment of the independent contribution of each level after controlling for others.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Source and Study Population\u003c/h2\u003e \u003cp\u003eThis study analysed data from the 2022 Kenya Demographic and Health Survey (KDHS), a nationally representative cross-sectional survey conducted by the Kenya National Bureau of Statistics with ICF International under the DHS Programme. The 2022 KDHS used a stratified two-stage cluster sampling design, selecting 1,691 primary sampling units (clusters) across 92 strata. Individual women's questionnaires were completed by 32,156 women aged 15\u0026ndash;49. Full details of the survey design, fieldwork, and weighting procedures are available in the survey final report (KNBS, 2023). Data were obtained from the DHS Programme website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under standard data use agreements.\u003c/p\u003e \u003cp\u003eAll 32,156 women who completed the individual interview were included in this analysis. Unlike analyses restricted to women with recent births, this study concerns a question that was administered to all respondents, making the full women's sample the appropriate denominator for CHW coverage estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Outcome Variable\u003c/h2\u003e \u003cp\u003eThe outcome variable was whether a CHW had visited the respondent's household in the three months preceding the survey, captured as a binary yes/no indicator. This variable was introduced in the 2022 KDHS and represents the first time this information has been captured in a nationally representative Kenyan household survey. The three-month reference window was chosen by the survey team to balance recall accuracy against adequate period prevalence; it captures regular outreach contact rather than one-off encounters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Independent Variables\u003c/h2\u003e \u003cp\u003eVariables were organised into three levels consistent with the Andersen behavioural model. Individual-level variables included: age group (15\u0026ndash;19, 20\u0026ndash;24, 25\u0026ndash;29, 30\u0026ndash;34, 35\u0026ndash;39, 40\u0026ndash;44, 45\u0026ndash;49); highest educational level (no education, primary, secondary, higher); current marital status (never married, married or cohabiting, formerly married); number of living children (0, 1\u0026ndash;2, 3\u0026ndash;4, 5 or more); weekly media exposure; and mobile phone ownership. Household-level variables included: household wealth index quintile (poorest through richest); whether the household head was female; access to electricity; improved water source; improved sanitation facility; and household size (small 1\u0026ndash;3, medium 4\u0026ndash;6, large 7 or more members). Community-level variables included: type of place of residence (urban, rural); region of residence (eight regions); travel time to the nearest health facility (30 minutes or less, 31\u0026ndash;60 minutes, more than 60 minutes); and four health access barrier variables, whether distance, money, permission, and travelling alone were reported as problems in accessing healthcare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses used Stata/MP 17 (StataCorp LLC) with svy prefix commands to account for the stratified cluster sampling design, incorporating sampling weights (v005 divided by 1,000,000), primary sampling units (v021), and strata (v022). Nationally representative estimates of CHW coverage were computed as weighted proportions with 95% confidence intervals. Bivariate associations between each independent variable and the CHW visit outcome were tested using F-tests with Rao-Scott corrections appropriate for complex survey designs.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was conducted using a sequential modelling approach. Model 1 included only individual-level factors. Model 2 added household-level factors to Model 1. Model 3 added community-level factors to Models 1 and 2. This hierarchical approach allows assessment of how household and community factors modify individual-level associations, and whether associations observed in bivariate analysis are explained by higher-level confounding. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003e The 2022 KDHS received ethical approval from the Kenya Medical Research Institute Scientific and Ethics Review Unit and the ICF International Institutional Review Board. All survey participants provided verbal informed consent prior to interview. This secondary analysis used publicly available, de-identified data and required no additional ethical review. Data access was obtained through the DHS Programme standard request process (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dhsprogram.com\u003c/span\u003e\u003cspan address=\"https://dhsprogram.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample Characteristics and Crude CHW Coverage\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the weighted distribution of the sample across all independent variables alongside the weighted CHW visit rate for each category. The national prevalence of CHW household visits in the preceding three months was 5.3% (95% CI 4.8\u0026ndash;5.8). The sample was predominantly rural (59.0%), with 38.8% having secondary education as the modal educational level. A substantial proportion of households lacked electricity (42.2%) or improved sanitation (28.1%), reflecting the wide socioeconomic heterogeneity within the sample.\u003c/p\u003e \u003cp\u003eSeveral patterns are immediately visible in the crude data. CHW coverage was highest among women with no formal education (9.7%), dropping to 4.7% among women with higher education. The inverse gradient with wealth was similarly steep: 8.4% of women in the poorest quintile received a visit, versus 3.0% in the richest. Rural women received visits at twice the rate of urban women (6.7% versus 3.3%). Regional variation was marked, with coverage in Western region (9.5%) and Nyanza (7.8%) far exceeding that in Nairobi (1.9%) and Central (2.4%). Women with five or more children had a visit rate more than double that of nulliparous women (7.8% versus 3.6%). Households facing access barriers also had slightly higher coverage: women who reported distance as a problem had a visit rate of 6.5% versus 4.9% among those who did not, and women who found going to a facility alone problematic had a rate of 6.7% versus 5.2%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Characteristics and CHW Coverage Among Women of Reproductive Age, Kenya 2022 (N\u0026thinsp;=\u0026thinsp;32,156)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighted % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHW Coverage % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3 (4.8\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.7 (17.8\u0026ndash;19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.1\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.3 (17.4\u0026ndash;19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.2\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4 (16.6\u0026ndash;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1 (13.4\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (5.4\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.7 (13.0\u0026ndash;14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9 (3.9\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (9.1\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4 (5.0\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (7.5\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4 (4.8\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 (4.7\u0026ndash;6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7 (7.3\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.5 (34.7\u0026ndash;38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.3 (4.6\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.8 (37.1\u0026ndash;40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.3\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2 (17.8\u0026ndash;20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7 (3.6\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.0 (30.5\u0026ndash;33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9 (3.2\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.7 (54.1\u0026ndash;57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4 (5.7\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormerly married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3 (11.5\u0026ndash;13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.2 (3.1\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of living children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.4 (27.2\u0026ndash;29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6 (2.9\u0026ndash;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.6 (34.4\u0026ndash;36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.3\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5 (22.5\u0026ndash;24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.5\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5 (11.7\u0026ndash;13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8 (6.3\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5 (14.1\u0026ndash;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4 (6.9\u0026ndash;9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.8 (16.5\u0026ndash;19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (5.5\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.5 (17.2\u0026ndash;19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1 (4.1\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.3 (20.9\u0026ndash;23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9 (4.0\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9 (24.3\u0026ndash;27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.3\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold electricity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2 (40.0\u0026ndash;44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0 (6.1\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.8 (55.6\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.5\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImproved sanitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.1 (26.2\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4 (6.4\u0026ndash;8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.9 (70.0\u0026ndash;73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5 (4.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.2 (26.0\u0026ndash;28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3 (3.5\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium (4\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.4 (48.1\u0026ndash;50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.5 (4.8\u0026ndash;6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge (7+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.4 (22.3\u0026ndash;24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2 (5.2\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0 (39.0\u0026ndash;43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.3 (2.7\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.0 (57.0\u0026ndash;61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (6.0\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3 (8.3\u0026ndash;10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.1 (5.7\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Eastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (2.5\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1 (3.8\u0026ndash;8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9 (11.7\u0026ndash;14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8 (2.9\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.6 (12.4\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4 (1.7\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRift Valley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.9 (22.1\u0026ndash;25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8 (4.8\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (9.9\u0026ndash;12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5 (7.6\u0026ndash;11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNyanza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.5 (11.3\u0026ndash;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8 (6.2\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNairobi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5 (12.1\u0026ndash;14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.1\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTravel time to facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30 min or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.7 (74.2\u0026ndash;77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.5\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;60 minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.8 (16.6\u0026ndash;19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0 (4.9\u0026ndash;7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 60 minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (5.8\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4 (5.5\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to facility is a problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.3 (74.8\u0026ndash;77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9 (4.4\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.7 (22.2\u0026ndash;25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.5\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMoney for treatment is a problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.0 (52.1\u0026ndash;55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9 (4.3\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.0 (44.1\u0026ndash;47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8 (5.1\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoing alone to facility is a problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.7 (89.9\u0026ndash;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.7\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3 (8.5\u0026ndash;10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7 (5.3\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. All estimates are survey-weighted with 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Bivariate Associations\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents F-statistics and p-values from Rao-Scott-corrected bivariate tests for each independent variable. Fourteen of nineteen variables were statistically significantly associated with CHW visit receipt. The strongest associations were observed for residence (F\u0026thinsp;=\u0026thinsp;39.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), household electricity (F\u0026thinsp;=\u0026thinsp;37.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), improved sanitation (F\u0026thinsp;=\u0026thinsp;32.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (F\u0026thinsp;=\u0026thinsp;17.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), parity (F\u0026thinsp;=\u0026thinsp;15.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), wealth quintile (F\u0026thinsp;=\u0026thinsp;14.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and region (F\u0026thinsp;=\u0026thinsp;12.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Education was also strongly significant (F\u0026thinsp;=\u0026thinsp;9.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Variables that were not significantly associated with CHW visits included media exposure, mobile phone ownership, female household headship, improved water source, and whether permission to seek care was reported as a barrier.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate Associations Between Sample Characteristics and CHW Visit in the Preceding Three Months\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of living children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile phone ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold electricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved water source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved sanitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to facility is a problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoney for treatment is a problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePermission to seek care is a problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoing alone is a problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time to facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ns\u0026thinsp;=\u0026thinsp;not significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. F-statistics from Rao-Scott-corrected chi-square tests for complex survey design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Multivariable Predictors of CHW Visit Receipt\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents results from the three sequential logistic regression models. Several noteworthy patterns emerge across the model sequence.\u003c/p\u003e \u003cp\u003eParity was the most consistent independent predictor across all three models. Women with one to two children had 1.82 times the odds of a CHW visit compared with nulliparous women in the fully adjusted model (aOR 1.82, 95% CI 1.18\u0026ndash;2.81); women with three to four children had 2.05 times the odds (aOR 2.05, 95% CI 1.24\u0026ndash;3.40); and women with five or more children had 2.06 times the odds (aOR 2.06, 95% CI 1.19\u0026ndash;3.57). This gradient was consistent across all three models and was only slightly attenuated after the addition of household and community variables, suggesting that parity operates as an independent predictor of CHW targeting rather than a proxy for household poverty or rural location.\u003c/p\u003e \u003cp\u003eEducation produced a striking reversal across the model sequence\u0026mdash;the most analytically important finding in the study. In Model 1, education was inversely associated with CHW visits: women with primary education had 40% lower odds (aOR 0.60) and women with higher education had 34% lower odds (aOR 0.66) compared to those with no education. By Model 2, these associations had attenuated substantially. By Model 3\u0026mdash;after adding region and rural residence\u0026mdash;the direction reversed entirely: secondary education was now associated with significantly higher odds of a visit (aOR 1.79, 95% CI 1.11\u0026ndash;2.91) and higher education with the highest odds of all (aOR 2.08, 95% CI 1.15\u0026ndash;3.76). This pattern indicates that uneducated women live disproportionately in regions and localities with high CHW coverage; once the regional distribution is accounted for, education is actually positively associated with receiving a visit. The finding has important implications for how CHW targeting is interpreted and is examined further in the discussion.\u003c/p\u003e \u003cp\u003eWealth quintile followed a different pattern. In Model 2, the wealthiest women had 63% lower odds of a CHW visit than the poorest (aOR 0.37, 95% CI 0.22\u0026ndash;0.74). After adding community-level variables in Model 3, this wealth gradient was no longer statistically significant at any quintile. This suggests that the apparent targeting of CHW visits toward poorer women in bivariate and Model 2 analyses is largely explained by the geographic distribution of poverty\u0026mdash;poor women are more likely to live in high-coverage rural and western regions, and it is geography rather than wealth per se that drives their higher contact rates.\u003c/p\u003e \u003cp\u003eRural residence was independently and significantly associated with higher odds of CHW contact after adjusting for all individual and household factors (aOR 1.40, 95% CI 1.04\u0026ndash;1.90). This confirms a genuine geographic effect beyond what is explained by the socioeconomic composition of rural populations.\u003c/p\u003e \u003cp\u003eRegional variation was large and highly significant. Using the Coast region as the reference, women in Central region had 73% lower odds of a CHW visit (aOR 0.27, 95% CI 0.17\u0026ndash;0.43), Eastern region had 61% lower odds (aOR 0.39, 95% CI 0.26\u0026ndash;0.58), and Nairobi had 77% lower odds (aOR 0.23, 95% CI 0.09\u0026ndash;0.59). Western and Nyanza regions showed no significant difference from Coast. These regional differences were not explained by individual or household characteristics, pointing to genuine structural variation in CHW programme implementation across Kenya's devolved county system.\u003c/p\u003e \u003cp\u003eAmong the access barrier variables, hesitation about going to a health facility alone was independently associated with higher odds of CHW contact (aOR 1.43, 95% CI 1.07\u0026ndash;1.90), consistent with the hypothesis that CHWs reach women who face mobility constraints. However, distance to facility and travel time\u0026mdash;which showed significant bivariate associations\u0026mdash;did not retain significance in the fully adjusted model, suggesting these effects are mediated by rural residence and region. The finding that reporting money as a barrier to care was associated with lower odds of CHW contact (aOR 0.76, 95% CI 0.60\u0026ndash;0.97) is unexpected and may reflect that very poor women\u0026mdash;who are most likely to report financial barriers\u0026mdash;are, paradoxically, less likely to be reached by CHWs in practice after all confounders are controlled.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression of Factors Associated with CHW Household Visits in the Preceding Three Months (N\u0026thinsp;=\u0026thinsp;32,156)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable (Reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1 aOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 aOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3 aOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group (ref: 15\u0026ndash;19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndividual factors only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+ Household factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+ Community factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 [0.73\u0026ndash;1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 [0.84\u0026ndash;1.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 [0.78\u0026ndash;1.65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71* [0.52\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 [0.66\u0026ndash;1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 [0.65\u0026ndash;1.44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 [0.61\u0026ndash;1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 [0.79\u0026ndash;1.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 [0.79\u0026ndash;1.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57** [0.39\u0026ndash;0.82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.57\u0026ndash;1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84 [0.52\u0026ndash;1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 [0.50\u0026ndash;1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 [0.68\u0026ndash;1.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 [0.67\u0026ndash;1.93]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 [0.45\u0026ndash;1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 [0.69\u0026ndash;1.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 [0.65\u0026ndash;1.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (ref: No education)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60*** [0.44\u0026ndash;0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71* [0.50\u0026ndash;0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 [0.87\u0026ndash;2.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66** [0.49\u0026ndash;0.89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 [0.71\u0026ndash;1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79* [1.11\u0026ndash;2.91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66* [0.44\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 [0.78\u0026ndash;1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08* [1.15\u0026ndash;3.76]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status (ref: Never married)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 [0.95\u0026ndash;1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 [0.90\u0026ndash;1.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 [0.74\u0026ndash;1.67]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormerly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 [0.60\u0026ndash;1.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 [0.58\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 [0.47\u0026ndash;1.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity (ref: 0 children)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53** [1.17\u0026ndash;2.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48* [1.07\u0026ndash;2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82** [1.18\u0026ndash;2.81]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16*** [1.52\u0026ndash;3.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70** [1.15\u0026ndash;2.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05** [1.24\u0026ndash;3.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50*** [1.66\u0026ndash;3.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78** [1.15\u0026ndash;2.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06** [1.19\u0026ndash;3.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth quintile (ref: Poorest)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 [0.63\u0026ndash;1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 [0.87\u0026ndash;1.65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68* [0.50\u0026ndash;0.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 [0.65\u0026ndash;1.42]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66* [0.44\u0026ndash;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 [0.64\u0026ndash;1.74]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37*** [0.22\u0026ndash;0.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 [0.56\u0026ndash;1.82]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElectricity (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 [0.64\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98 [0.72\u0026ndash;1.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImproved sanitation (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 [0.69\u0026ndash;1.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 [0.69\u0026ndash;1.15]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size (ref: Small)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMed 4\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 [0.88\u0026ndash;1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94 [0.72\u0026ndash;1.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge 7+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 [0.82\u0026ndash;1.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87 [0.63\u0026ndash;1.21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural residence (ref: Urban)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40* [1.04\u0026ndash;1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion (ref: Coast)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39*** [0.26\u0026ndash;0.58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27*** [0.17\u0026ndash;0.43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNairobi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23** [0.09\u0026ndash;0.59]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMoney problem (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76* [0.60\u0026ndash;0.97]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGoing alone problem (ref: No)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43* [1.07\u0026ndash;1.90]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. aOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio. Model 1: individual-level factors; Model 2: individual\u0026thinsp;+\u0026thinsp;household factors; Model 3: all factors including community.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: 2022 Kenya Demographic and Health Survey. All models are survey-weighted with complex design adjustment. aOR\u0026thinsp;=\u0026thinsp;adjusted odds ratio.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The Problem of Low Coverage\u003c/h2\u003e \u003cp\u003eThe headline finding of this study is stark: only 5.3% of Kenyan women of reproductive age received a CHW household visit in the three months before the 2022 KDHS interview. If this is taken as an approximation of quarterly coverage, it implies an annualised visit rate of roughly one in five women, and this is almost certainly an overestimate, since the three-month recall window captures any contact, however brief or incidental, rather than the structured monthly household visit that the Community Health Strategy prescribes. The programmatic target of monthly CHW contact with every household in a community unit is nowhere near achieved at national scale.\u003c/p\u003e \u003cp\u003eThis figure sits at the lower end of CHW coverage estimates reported from other sub-Saharan African countries with comparable programme structures. A multi-country analysis of DHS data by Nkonki et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found CHW contact rates of 10\u0026ndash;35% in rural areas of four African countries over comparable reference periods. Studies from Ethiopia, where the health extension worker programme is often cited as a model for sub-Saharan Africa, have reported higher contact rates of 30\u0026ndash;50% in rural areas with functional systems (Medhanyie et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shiferaw et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Kenyan figure falls below these comparisons, suggesting that the Community Health Strategy has not yet translated into consistent household-level contact even after nearly two decades of implementation.\u003c/p\u003e \u003cp\u003eThe timing of this survey, conducted in 2022, is important context. Kenya's CHV model was still operating on a volunteer basis at the time of data collection; the transition to salaried Community Health Promoters was announced in 2022 but not fully implemented nationally until 2023. The data therefore reflect the coverage achieved under the volunteer model, with all its structural limitations. Future DHS surveys will be essential for evaluating whether the transition to paid CHPs produces the anticipated improvement in outreach coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Parity as the Dominant Individual Predictor\u003c/h2\u003e \u003cp\u003eThe finding that parity is the strongest and most consistent independent predictor of CHW contact robust across all three model specifications and only minimally attenuated by household and community controls. This is consistent with the programme theory of Kenya's community health system. The strategy explicitly targets households with pregnant women, infants, and young children as priority populations for CHV outreach. Women with more children are more likely to have been enrolled with a CHV through prior maternal or child health contacts, are more likely to have been flagged in community health registers, and may be more geographically visible to CHVs conducting household mapping.\u003c/p\u003e \u003cp\u003eThe dose-response nature of the parity gradient, rising from 1.82 for one to two children through 2.06 for five or more children compared to nulliparous women also suggests that this is not simply a marker of prior programme enrolment but a cumulative effect of repeated contacts over the reproductive life course. Women with multiple children have had more opportunities to be identified and included in CHV caseloads. This is arguably a positive finding for programme design, but it simultaneously raises a concern. Nulliparous women and women with one child are systematically underserved by a system focused on mothers with established maternity histories. Women in early reproductive life, including adolescents and young adults may be the group most in need of proactive outreach precisely because they lack established relationships with the health system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.3 The Education Reversal: Confounding by Region\u003c/h2\u003e \u003cp\u003eThe reversal of the education effect across the model sequence is one of the most analytically important findings in this study, and it deserves careful interpretation. In Model 1 which adjusts only for individual-level factors, more-educated women have significantly lower odds of a CHW visit, consistent with the naive expectation that CHWs preferentially target disadvantaged women. By Model 3, after adding region and rural-urban residence, this relationship reverses: secondary and higher-educated women have significantly higher odds of a visit than uneducated women.\u003c/p\u003e \u003cp\u003eThe mechanism is regional confounding. Women with no formal education are heavily concentrated in regions with high CHW coverage, particularly in western Kenya (Western and Nyanza regions), where CHW programmes have historically been stronger, and in rural areas more generally. When region and residence are not controlled, the correlation between low education and high CHW coverage appears to reflect CHW targeting of disadvantaged women. When region is controlled, it becomes apparent that educated women who live in high-coverage regions receive more visits than uneducated women in those same regions. In other words, uneducated women receive more CHW visits than educated women not because CHWs are targeting them within localities, but because uneducated women disproportionately live in localities with better CHW systems.\u003c/p\u003e \u003cp\u003eThis finding has a sobering implication: within any given community, there is limited evidence that CHWs are systematically prioritising the most disadvantaged women. The equity benefits of the programme at national level are an artefact of the geographic correlation between poverty, low education, and residence in higher-coverage regions not of explicit targeting within those regions. This calls for a reexamination of CHV training and caseload management to ensure that within-community targeting reaches the women at greatest social disadvantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Regional Variation and the Devolution Challenge\u003c/h2\u003e \u003cp\u003eThe scale of regional variation in CHW coverage, and with Central, Eastern, and Nairobi regions having odds of CHW contact that are a quarter to two-fifths of that in the Coast region, cannot be explained by the sociodemographic composition of populations within those regions. After controlling for individual, household, and other community characteristics, the regional differences remain large and highly significant. This points to structural, system-level variation in how the Community Health Strategy is implemented across Kenya's counties.\u003c/p\u003e \u003cp\u003eSeveral county-level factors are likely to drive this variation. Devolution has created a situation in which the quality and functionality of community health systems is heavily dependent on county government fiscal capacity and political prioritisation. Wealthier counties with strong revenue bases can fund CHA salaries, CHV stipends, and outreach supplies more consistently; poorer counties cannot. Counties in western Kenya have historically benefited from significant NGO investment in community health infrastructure, which may partly explain the higher coverage in Western and Nyanza regions. Nairobi's extremely low coverage (1.9% despite its economic weight) likely reflects both the urban-focused health system which emphasises facility-based care over community outreach, and the logistical challenges of reaching high-density informal settlements with a community unit structure designed for rural geographies.\u003c/p\u003e \u003cp\u003eThe regional findings have direct policy implications for Kenya's implementation of the Community Health Promoters programme. Simply paying CHPs a salary will not resolve the infrastructure and supervision deficits that produce low coverage in Central and Eastern regions. Counties require functional community health units with equipped CHAs, maintained supply chains, and operational data systems. Without these supports, a salaried workforce will remain unable to deliver the household-level contact that the programme is designed to achieve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Access Barriers and CHW Targeting\u003c/h2\u003e \u003cp\u003eThe finding that women who face hesitation about going to a health facility alone are more likely to receive CHW visits (aOR 1.43) is consistent with programme logic. CHWs are supposed to reach women who cannot easily access facilities, and mobility constraints represent one type of access barrier. However, the failure of financial barriers to predict higher CHW contact and in fact their marginal negative association in the fully adjusted model is more troubling. If women who cannot afford healthcare are not significantly more likely to be reached by CHWs, then the compensatory function of community outreach for the most economically marginalised is not being realised. This aligns with qualitative evidence from Kenya showing that CHVs sometimes concentrate their visits on households they find easiest and most receptive which may be middle-income households rather than the very poorest (Odhiambo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the CHW visit variable is based on self-report by women respondents about a household-level event, with a three-month recall period. Recall error could lead to both over-reporting and under-reporting of visits. Second, the variable captures only whether a visit occurred, not its content, quality, or the health topic addressed. A brief encounter in which a CHV distributed a registration card may be counted identically to a comprehensive household assessment. Third, the cross-sectional design precludes causal inference; the associations reported describe population patterns, not causal relationships. Fourth, the regional grouping in the KDHS uses the eight former provinces, which masks considerable within-region variation at county level. County-level analysis would be more actionable for programme planning but is constrained by sample sizes in smaller counties. Fifth, the 2022 survey was conducted during a period of transition in the CHW programme, and coverage estimates may not be representative of programme steady-state.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study provides the first nationally representative estimate of CHW household visit coverage among Kenyan women of reproductive age, and the findings reveal a substantial gap between programme aspiration and operational reality. A CHW visit rate of 5.3% over three months is far below what would be needed to deliver the monthly household contact envisioned in Kenya\u0026apos;s Community Health Strategy. The women most likely to receive visits are those with higher parity, those living in rural areas, and those in historically better-served western regions. This pattern that reflects programme reach to the already-engaged rather than systematic targeting of the most vulnerable.\u003c/p\u003e\n\u003cp\u003eThree findings stand out for their policy relevance. The education reversal, from negative to positive association after regional adjustment, reveals that national-level equity in CHW distribution is an artefact of geography rather than explicit targeting. Within communities, educated women are no less likely to receive visits, which means CHWs are not compensating for socioeconomic disadvantage within their catchment areas as intended. The attenuation of wealth effects after regional adjustment reinforces the same conclusion. And the massive regional variation\u0026mdash;with some regions having coverage more than four times higher than others after demographic adjustment\u0026mdash;points to structural failures in programme implementation that are a consequence of devolution without adequate equalisation of resources across counties.\u003c/p\u003e\n\u003cp\u003eThe transition to salaried Community Health Promoters represents a necessary but not sufficient condition for improving coverage. What the data reveal is that the problem is not only one of workforce incentives it is one of programme architecture: functional community health units, adequate CHA supervision ratios, reliable supply chains, and operational data systems are all required for the household outreach model to work at scale. Kenya\u0026apos;s county governments, supported by national-level standards and conditional funding, need to invest in this infrastructure if the Community Health Promoters programme is to translate its workforce investment into the population-level coverage gains that the health strategy promises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eThe ethics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was performed in accordance with the principles of the Declaration of Helsinki. The study used secondary data from the 2022 Kenya Demographic and Health Survey (KDHS), which is publicly available through the DHS Program website (https://dhsprogram.com). Ethical approval for the original KDHS data collection was obtained from the ICF Institutional Review Board (Project Number: 132989) and the Kenya Medical Research Institute (KEMRI) Scientific and Ethics Review Unit (Protocol Number: KEMRI/RES/7/3/1). All survey respondents provided written informed consent before participation, including consent for anonymized data to be used in future research. Since this analysis involved de-identified, publicly available data, it did not require further ethical clearance\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article. This study was conducted using publicly available data from the Demographic and Health Surveys (DHS) Program, and all work was performed as part of the authors\u0026apos; academic affiliations without external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in the original surveys provided written informed consent before participation, including consent for anonymized data to be used in future research. As this study involved secondary analysis of fully anonymized, publicly available data, it was exempt from additional ethical review. Human Ethics and Consent to Participate declarations: not applicable for this secondary analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable. This manuscript does not contain any 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\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. No financial or non-financial interests that could be construed as influencing the research or interpretation of the findings exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharles, John: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing \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\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the Demographic and Health Surveys (DHS) Program repository and the Kenya National Bureau of Statistics https://statistics.knbs.or.ke/nada/index.php/catalog/128/related-materials and https://dhsprogram.com/data/dataset/Kenya_Standard-DHS_2022.cfm?flag=1. Access to the data requires free registration and approval of a research proposal by The DHS Program, in accordance with the data use agreements with the Government of Kenya. The data are publicly available for legitimate research purposes. The authors confirm that they did not have any special access privileges to these data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkintola, O. (2011). What motivates people to volunteer? The case of volunteer AIDS caregivers in faith-based organisations in KwaZulu-Natal, South Africa. Health Policy and Planning, 26(1), 53\u0026ndash;62. https://doi.org/10.1093/heapol/czq019\u003c/li\u003e\n\u003cli\u003eAndersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1\u0026ndash;10. https://doi.org/10.2307/2137284\u003c/li\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\u003eGilmore, B., \u0026amp; McAuliffe, E. (2013). Effectiveness of community health workers delivering preventive interventions for maternal and child health in low- and middle-income countries: A systematic review. BMC Public Health, 13, 847. https://doi.org/10.1186/1471-2458-13-847\u003c/li\u003e\n\u003cli\u003eGreenspan, J. A., McMahon, S. A., Chebet, J. J., Mpunga, M., Urassa, D. P., \u0026amp; Winch, P. J. (2013). Sources of community health worker motivation: A qualitative study in Morogoro Region, Tanzania. Human Resources for Health, 11, 52. https://doi.org/10.1186/1478-4491-11-52\u003c/li\u003e\n\u003cli\u003eJacobs, C., Michelo, C., \u0026amp; Moshabela, M. (2012). Why do rural women in the most remote and poorest areas of Zambia predominantly attend only one antenatal care visit? 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Health Policy and Planning, 30(9), 1207\u0026ndash;1227. https://doi.org/10.1093/heapol/czu126\u003c/li\u003e\n\u003cli\u003eLassi, Z. S., Haider, B. A., \u0026amp; Bhutta, Z. A. (2010). Community-based intervention packages for reducing maternal and neonatal morbidity and mortality and improving neonatal outcomes. Cochrane Database of Systematic Reviews, 11, CD007754. https://doi.org/10.1002/14651858.CD007754.pub2\u003c/li\u003e\n\u003cli\u003eLehmann, U., \u0026amp; Sanders, D. (2007). Community health workers: What do we know about them? The state of the evidence on programmes, activities, costs and impact on health outcomes of using community health workers. WHO Evidence and Information for Policy. https://www.who.int/hrh/documents/community_health_workers.pdf\u003c/li\u003e\n\u003cli\u003eLewin, S., Munabi-Babigumira, S., Glenton, C., Daniels, K., Bosch-Capblanch, X., van Wyk, B. E., Odgaard-Jensen, J., Johansen, M., Aja, G. N., Zwarenstein, M., \u0026amp; Scheel, I. B. (2010). 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BMC Health Services Research, 12, 352. https://doi.org/10.1186/1472-6963-12-352\u003c/li\u003e\n\u003cli\u003eMinistry of Health Kenya. (2020). Kenya Community Health Policy 2020\u0026ndash;2025. Ministry of Health. https://www.health.go.ke/wp-content/uploads/2021/06/Kenya-Community-Health-Policy-2020-2025.pdf\u003c/li\u003e\n\u003cli\u003eNkonki, L., Cliff, J., \u0026amp; Sanders, D. (2011). Lay health worker attrition: Important but often ignored. Bulletin of the World Health Organization, 89(12), 919\u0026ndash;923. https://doi.org/10.2471/BLT.11.087825\u003c/li\u003e\n\u003cli\u003eOdhiambo, J., Kuria, M., \u0026amp; Azila-Chaudhuri, A. (2014). Community health strategy implementation in Kenya: Lessons and recommendations. East African Medical Journal, 91(6), 190\u0026ndash;197.\u003c/li\u003e\n\u003cli\u003ePerry, H. B., Zulliger, R., \u0026amp; Rogers, M. M. (2014). 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Why do women prefer home births in Ethiopia? BMC Pregnancy and Childbirth, 13, 5. https://doi.org/10.1186/1471-2393-13-5\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. (1978). Declaration of Alma-Ata: International conference on primary health care. WHO. https://www.who.int/publications/almaata_declaration_en.pdf\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2019). WHO guideline on health policy and system support to optimize community health worker programmes. WHO. https://www.who.int/publications/i/item/9789241550369\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"community health workers, CHW coverage, home visits, primary healthcare, Kenya, DHS, health equity, determinants","lastPublishedDoi":"10.21203/rs.3.rs-9136466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9136466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCommunity health workers (CHWs), also referred to as community health promoters, (CHPs) are a cornerstone of Kenya's primary healthcare strategy, mandated to bridge the gap between households and formal health services through home-based outreach. Despite their central role in national health policy, nationally representative data on the actual coverage of CHW household visits remain limited. The proportion of women of reproductive age who receive CHW visits and the factors that determine whether they do, is largely unknown at population scale.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used data from the 2022 Kenya Demographic and Health Survey (KDHS), which included a question on whether a CHW had visited the respondent's household in the three months preceding the survey. The analytic sample comprised 32,156 women aged 15\u0026ndash;49. Weighted prevalence of CHW visits was estimated with 95% confidence intervals. Bivariate associations were examined using F-tests with Rao-Scott corrections. Multivariable logistic regression was conducted in three sequential models incorporating individual, household, and community-level factors, with all analyses accounting for the complex survey design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOnly 5.3% (95% CI 4.8\u0026ndash;5.8) of women reported a CHW visit in the preceding three months. In bivariate analysis, CHW coverage was significantly associated with education, parity, marital status, wealth quintile, household electricity and sanitation, residence, region, and travel time to facility. In the fully adjusted model, the strongest predictors of receiving a CHW visit were higher parity (aOR 2.06 for 5\u0026thinsp;+\u0026thinsp;children versus none), rural residence (aOR 1.40), and hesitation about going to a facility alone (aOR 1.43). Counterintuitively, higher education was independently associated with greater odds of a CHW visit in the fully adjusted model, a pattern explained by regional confounding. Marked regional variation was observed, with coverage in Central region and Nairobi less than a third of that in the Coast reference region.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCHW visit coverage in Kenya is extremely low, reaching fewer than one in twenty women of reproductive age over a three-month period. Coverage is somewhat better targeted toward disadvantaged households, but the absolute levels are insufficient to constitute a meaningful outreach system. The strong regional variation suggests that CHW deployment and retention is uneven, likely reflecting county-level differences in financing, supervision, and political prioritisation of community health. Strategies to improve CHW coverage must address workforce remuneration, supply chains, and supervision structures rather than training alone.\u003c/p\u003e","manuscriptTitle":"Coverage and Determinants of Community Health Worker Visits Among Women of Reproductive Age in Kenya: A Nationally Representative Cross-Sectional Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 18:36:09","doi":"10.21203/rs.3.rs-9136466/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T05:53:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T15:49:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256607021761665176248942510944843762208","date":"2026-04-22T23:59:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T13:16:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269902677046609580147484300049974506647","date":"2026-04-18T19:57:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265279905504784707470660473674477881705","date":"2026-04-18T16:31:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T08:52:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T17:10:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T08:57:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T08:57:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-16T10:10:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a48aae2-1eb8-4687-a5c6-9cffe3c08953","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T05:53:48+00:00","index":64,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T15:49:53+00:00","index":63,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T18:36:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 18:36:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9136466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9136466","identity":"rs-9136466","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}