Assessing Facility Readiness and Spatial Accessibility for the Management of Hypertension in Kilifi County, Kenya: A Cross-Sectional Study

Assessing Facility Readiness and Spatial Accessibility for the Management of Hypertension in Kilifi County, Kenya: A Cross-Sectional Study | 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 Assessing Facility Readiness and Spatial Accessibility for the Management of Hypertension in Kilifi County, Kenya: A Cross-Sectional Study Robinson Oyando, Eda Mumo, Ruth Lucinde, Aurelia Brazeal, Clement Mwagwabi, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003184/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Non-communicable diseases (NCDs) currently cause 43 million deaths globally. Health systems in low-and middle-income countries, including Kenya, are struggling to respond to the growing NCD burden and respond to population health needs in an equitable and accessible manner. The aim of this study was to examine health facility readiness, health workers’ knowledge, and spatial accessibility to hypertension management in Kilifi County, Kenya. Methods We conducted a cross-sectional survey of 34 facilities in Kilifi North and Kilifi South sub-counties. Readiness was assessed across five domains: basic infrastructure, equipment, diagnostics, medicines, and training/guidelines. Facilities with readiness index ≥ 70% for all the assessed domains were classified as ready to provide hypertension services. Fisher’s exact test was used to examine factors associated with facility readiness. Health worker knowledge in managing hypertension was evaluated using self-administered questionnaires. Spatial accessibility to geocoded health facilities was modelled in AccessMod using high spatial resolution raster datasets of the elevation, land cover, and population combined with vector datasets of a detailed road network and travel barriers. Four travel scenarios were adopted: walking only, motorcycle only, walking followed by motorcycle, and walking followed by motorcycle and then vehicle. Results The overall mean hypertension service readiness index was 42.9% (95% CI: 37.1–48.8). We found strong evidence that readiness varied by facility type, facility location and supervisory practices ( p < 0.05). The weakest readiness domains were in the availability of anti-hypertensive medicines (21.4%; 95% CI: 12.2–30.6) and staff training/guidelines (25%; 95% CI: 11.6–38.1). Whereas the mean overall knowledge score was 11.9 out of 13 (91.4%; 95% CI: 89.3–93.4), only 14% of health workers were familiar with the latest cardiovascular treatment guidelines. Spatial accessibility analysis using the most pragmatic travel scenario for the Coast region indicated that over 80% of the population in the two sub-counties (~ 530,000 people) resided within 30 minutes travel time to a health facility. Conclusion Health facilities were geographically accessible, but they lacked the readiness to deliver hypertension care. To improve health facility readiness, measures to ensure the availability of anti-hypertensive medicines, healthcare worker training and dissemination of treatment guidelines should be prioritised. Non-communicable diseases Hypertension Health facility readiness Geospatial access Universal Health Coverage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The rapidly increasing burden of non-communicable diseases (NCDs) globally threatens health systems, particularly in low-and middle-income countries (LMICs), in achieving Sustainable Development Goal (SDG) targets 3.4, on reducing premature NCD mortality by one-third by 2030, and 3.8 on universal health coverage (UHC) ( 1 , 2 ). In sub-Saharan Africa (SSA), NCDs account for 37% of all deaths and are projected to surpass communicable, maternal, neonatal and nutritional diseases combined by 2030 ( 3 ). In Kenya, NCDs account for 50% of hospitalisations and 41% of all deaths ( 4 , 5 ). Health systems in many LMICs are poorly prepared to meet the needs of people living with NCDs ( 6 ). A continued focus on acute conditions and maternal and child health has meant that less – albeit slowly growing – emphasis has been placed on creating more coordinated services across primary and specialist care that are required to improve outcomes for chronic NCDs ( 7 , 8 ). This lack of preparedness has been linked to about half of NCD deaths in LMICs ( 6 ). There has been increasing interest in understanding health facility readiness for managing NCDs in LMICs ( 9 – 11 ). Studies have typically examined multiple NCDs ( 12 – 14 ), using adaptations of the World Health Organization (WHO) Service Availability and Readiness Assessment (SARA) tool ( 15 – 17 ) and including public and private sector facilities ( 16 , 18 , 19 ). These studies consistently highlight substantial gaps across key health system components such as: limited availability of essential NCD medications ( 15 , 20 ); lack of diagnostic equipment ( 15 , 21 ) and absence of treatment protocols or guidelines ( 16 , 18 , 22 ); along with insufficient infrastructure ( 11 , 21 ) and inadequate number of trained front-line health workers (FLHWs) ( 15 , 16 , 18 ). Other additional challenges include inadequate health information systems, patient education materials, referral and follow-up mechanisms, financing, and governance structures for NCD care ( 21 – 24 ). Facility-level characteristics such as ownership, level of care, location, supervision practices, quality assurance and review of patients’ feedback were found to be significantly associated with the level of readiness for management of NCDs ( 13 , 16 , 18 , 25 ). Despite limited longitudinal evidence, one study from Ethiopia reported persistently low, and a declining trend in facility readiness over time, highlighting systemic challenges in improving and sustaining NCD service capacity ( 14 ). Two areas that have received less attention regarding facility readiness are (i) spatial accessibility of health facilities across different levels of care for NCDs and (ii) FLHWs’ knowledge of NCDs. Spatial accessibility refers to the ease with which individuals are able to reach a healthcare facility from their homes, typically measured by distance, travel time or cost ( 26 ). Regarding FLHWs’ knowledge, research from India, Tanzania, and Uganda suggests that higher-cadre staff, such as doctors, are more knowledgeable in NCD management compared to nurses and other clinical cadres, but the overall evidence remains sparse. Both spatial accessibility and FLHW knowledge shape access and utilization of care: few health facilities, resulting in long travel distance or high transport costs, can deter access ( 27 – 30 ). In contrast, inadequate provider knowledge or perceived competency may drive patients to bypass local primary healthcare facilities in favour of hospitals or private providers ( 28 , 31 – 33 ). Understanding service availability and facility readiness is critical to guide policy reforms that re-orient services toward integrated NCD care and re-design delivery models for efficiency, equity and sustainability. But these issues are not well understood in the context of Kenya. This study seeks to contribute to bridging this evidence gap. Using hypertension, which is a is a prevalent (33%) condition in Kenya ( 34 ), as a lens through which to examine NCD management, we investigated facility readiness, FLHW knowledge, and spatial accessibility of facilities, along with the factors associated with facility readiness, in two rural sub-counties of Kilifi County. Methods This study was embedded within a larger project on Improving Hypertension Control in Rural sub-Saharan Africa (IHCoR-Africa), which aimed to co-develop and evaluate a community-centred approach to improve the management of hypertension in two rural sites (Kilifi North and Kilifi South) in Kilifi, Kenya, and Kiang West, the Gambia ( 35 , 36 ). In this paper, we report the facility readiness assessment conducted for the Kenyan component of the study. The selection of the two rural sites in Kilifi was informed by discussions and consultations between the study team and the Kilifi County Department of Health Management Team. Study setting Between September and October 2024, we conducted a cross-sectional survey of all public health facilities in Kilifi North and Kilifi South sub-counties (see Table A1 and Figure A1 in Appendix 1) of Kilifi County, located in the coastal region of Kenya. Kilifi North and Kilifi South sub-counties are among the highly populated sub-counties in Kilifi County with a population of over 201,300 and 232,739, respectively ( 37 ). The county’s primary economic activities include subsistence agriculture, fishing, and tourism ( 38 ). The area has also been reported to experience a high burden of stroke and heart failure ( 39 ). Along with 46 semi-autonomous counties, the Kilifi government shares responsibilities for the health system with the national government ( 40 ). Counties provide most health services ( 41 ). Healthcare is organised into four tiers: community health services, primary healthcare facilities (PHC) (dispensaries and health centres), secondary hospitals (sub-county and county referral hospitals), and tertiary referral hospitals ( 41 ), with the latter overseen by the national government. Services are delivered by public and private providers, with private provision slightly dominating. Hypertension services should be provided across all the four tiers ( 5 ), but, in practice, hypertension screening and follow-up at the community level have remained ad hoc, with some primary healthcare facilities not offering hypertension management services at all ( 18 , 42 ). Service delivery in Kilifi County mirrors the tiered system documented in the national health policy guidelines ( 43 ). Access to PHC has improved with the building of new dispensaries, but NCD management at the community level remains ad hoc, and many PHC facilities lack the staffing, essential medicines, and equipment for hypertension management ( 28 , 43 , 44 ). Secondary care (Level 4) is limited, with only three of nine designated hospitals fully operational, requiring patients to travel long distances to access specialised NCD services ( 28 , 42 ). Human resources are heavily skewed towards higher-level hospitals, while weaknesses in the supply chain cause frequent stock-outs of essential medicines. Inadequate availability of antihypertensive medicines is in part explained by restrictions in stocking certain antihypertensive classes at PHC facilities (especially dispensaries) compare to higher level hospitals ( 44 – 47 ). As a result, and again mirroring the rest of the country, hypertension service delivery remains inconsistent, poorly coordinated, and inaccessible to the population in need ( 42 ). Study design We conducted a cross-sectional survey of all public health facilities in Kilifi North and South sub-counties of Kilifi County. Health facility and health care worker selection All 34 public health facilities located in both sub-counties were included in the study (Figure A1 in Appendix 1). These included one county referral hospital and two sub-county hospitals (Level 4), four health centres (Level 3), and 27 dispensaries (Level 2). The list of 34 health facilities was obtained from the Kenya master facility list ( 48 ) and was validated by the sub-county health management teams in Kilifi North and South sub-counties. FLHWs providing outpatient care to people living with hypertension were eligible for enrolment in the study. Lists of eligible FLHWs were provided by facility managers and included consultant physicians, medical doctors, clinical officers (non-medical doctor clinicians) ( 49 ) and nurses. All FLHWs offering hypertension care at respective health facilities were approached on the day of facility assessment, briefed about the study and invited to participate in the study by responding to the self-administered questionnaires at a time convenient to them. All potential FLHWs consented to participate in the study except two. Data collection Data were collected using an adapted version of the WHO SARA questionnaire ( 50 ). The adapted structured facility questionnaire consisted of seven modules (Appendix 2), which included questions informed by Kenya’s 2024 cardiovascular treatment guidelines ( 5 ) and the 2023 essential medicines list ( 47 ). In the adapted SARA questionnaire, we added questions on (i) equipment required for the management of hypertension and (ii) the availability of essential antihypertensive medicines by level of care, respectively. Validation and standardisation of the data collection tool was undertaken in three steps. First, the tool was aligned with questionnaires from previous studies that have conducted facility readiness assessments for hypertension by including related and relevant questions ( 15 , 18 , 51 ). Second, we consulted with four medical experts (one consultant physician, three medical officers and one pharmacist) to review the tool and assess its relevance and applicability to Kenya’s health system. Third, the tool was pilot tested at six health facilities, and adaptations were made as appropriate. At each facility, trained research assistants collected data using direct observation, facility record review, and structured interviews with facility managers or other competent FLWHs who were familiar with facility operations. To minimise social desirability bias in respondent-reported information, wherever possible, we prioritised objective verification through direct observation of infrastructure, equipment, and service readiness indicators rather than relying solely on self-report. Information provided during interviews was cross-checked against observed conditions and facility records to enhance accuracy and reduce over- or under-reporting. To assess hypertension management knowledge, as part of the adapted SARA tool, we administered a structured 13-item questionnaire covering clinical evaluation, treatment initiation, and follow-up principles aligned with Kenya's 2024 cardiovascular disease management guidelines. Knowledge items included true/false statements and case-based scenarios (Appendix 2). Data from health facilities were collected electronically on tablets using RedCap (version 13.1.5) software. All study participants provided written informed consent prior to participating in study activities. Service availability and readiness indicator variables Hypertension-specific service availability was assessed by asking respondents from sampled facilities whether they offered diagnosis and/or management services for hypertension. Facility readiness was then evaluated using predefined tracer items across five domains: basic infrastructure, equipment, diagnostic capacity, trained staff and guidelines, and medicines (Table 1 ). For each domain, a mean availability score was computed, and an overall readiness score was derived as the average across all domains, expressed as a percentage. Following previous studies ( 16 , 18 , 52 ), facilities scoring ≥ 70% were classified as “ready” to deliver hypertension interventions and vice versa if the score was below the threshold. Table 1 Study outcomes assessed across health facilities Service Provision Instrument for data collection Derivation Number and proportion of outpatient visits related to hypertension, diabetes, comorbid (hypertension and diabetes) and other NCDs (mental disorders, cancer, chronic obstructive pulmonary diseases, epilepsy) Service statistics From July 2023 to June 2024, and types of facilities Readiness domain Basic infrastructure SARA Questionnaire Availability of 1) laboratory services, and functional 2) facility-owned phone, 3) computer, and 4) internet Basic equipment SARA Questionnaire Availability of functional: 1) blood pressure machine, 2) weight machine, 3) height meter, 4) measuring tape, 5) stethoscope, 6) ophthalmoscope Diagnostics SARA Questionnaire Availability of 1) Electrocardiogram (ECG) machine, 2) ECG thermal paper, 3) Strips for urinalysis, 4) Glucometer, 5) Glucometer strips, 6) Biochemistry equipment, 7) Haematology equipment, 8) X-ray machine Trained staff and treatment guidelines SARA Questionnaire and Self-completed questionnaire Reporting hypertension training in past year and familiarity with cardiovascular treatment guidelines Essential antihypertensive medicines SARA Questionnaire Availability of unexpired 1) Angiotensin converting enzyme Inhibitors (ACEIs) [enalapril], 2) Angiotensin Receptor Blockers (ARBs) [Losartan, Telmisartan], 3) Beta Blockers (BBs) [Bisoprolol, Labetalol, Metoprolol, Nebivolol], 4) Calcium channel Blockers (CCBs) [Amlodipine, Nifedipine], 5) Thiazide & Thiazide-like Diuretics (TTD) [Chlorthalidone, Hydrochlorothiazide, Indapamide], 6) Combination antihypertensive medicines [Amlodipine + Hydrochlorothiazide, Amlodipine + Indapamide, Losartan + Hydrochlorothiazide, Lisinopril + Hydrochlorothiazide, Perindopril + Amlodipine, Perindopril + Amlodipine + Indapamide, Telmisartan + Amlodipine, Telmisartan + Amlodipine + Hydrochlorothiazide, Telmisartan + Hydrochlorothiazide], 7) Other anti-hypertensive agents [ Methyldopa, Spironolactone, Hydralazine, Doxazosin, Prazosin, Phenoxybenzamine, Bosentan, Sildenafil, Tadalafil] Healthcare worker knowledge Comfort in managing hypertension Self-completed questionnaire Reporting "poor", “satisfactory” or "good" when asked whether he/she feel comfortable with managing hypertension Fair knowledge in hypertension management Self-completed questionnaire Assessed via case scenario questionnaires for hypertension and defined as scoring at least 10/13 Health worker knowledge assessment A 13-item knowledge assessment covering clinical evaluation, treatment initiation, and follow-up was administered to any FLHW who managed people with hypertension at respective health facilities (Appendix 2). Scores were categorised as having at least fair knowledge (10/13) ( 51 ). Spatial accessibility analysis Spatial datasets assembled for the accessibility modelling included a geocoded list of health facilities, detailed road network, land cover, a digital elevation model (DEM), travel barriers (national parks, community nature reserves, forest reserves, wetland areas) and a gridded population dataset. Spatial accessibility to the 34 public health facilities was modelled as travel time using the accessibility analysis module in WHO AccessMod Tool (version 5.8) ( 53 ). The analysis considered four travelling scenarios based on the area’s local context with defined travel speeds for each land cover and road class as outlined in Table 2 . In the combined travel scenarios, it was assumed that rural areas are characterised by poor road networks, and that the individual seeking care would have to walk across various land cover types before getting a motorised transport (motorcycle) on the minor roads and finally a vehicle on the major roads ( 54 ) In each scenario, travel time was modelled for all facilities collectively and the mean travel time for each sub-county per scenario obtained. Population residing within 30 minutes, 60 minutes, and over 1 hour travel time for each subcounty were also delineated. Table 2 Defined travel speeds above each road class and land cover type used to model travel time to the 34 public health facilities in Kilifi North and Kilifi South for four selected travel scenarios. Road and Land cover type Defined Speed (Km/hr) for the travel scenario Road Class 1 (Walking) 2 (Motorcycle) 3 (Combined: 1 & 2) 4 (Combined: 1, 2 & Vehicle) National 5 40 40 65 Primary 5 35 35 50 Secondary 5 30 30 30 Minor 5 25 25 25 Government 5 20 10 20 Settlement 5 10 5 10 Rural 5 10 5 10 Unclassified 5 10 5 10 Land Cover Type Waterbody 0 0 0 0 Tree cover 1 5 1 1 Wetland 0 0 0 0 Cropland 3 7..5 3 3 Built-up Area 5 10 5 5 Bare ground 5 10 5 5 Shrubland 4 7.5 4 4 Rangeland 4 7.5 4 4 Statistical analysis Facility readiness for hypertension management was visualised using graphical methods. Continuous variables were summarised as mean (95% confidence intervals) or median (interquartile range), depending on distribution. Descriptive analyses were conducted for all indicators and results were presented as frequencies and percentages. Comparisons of outcomes by facility level were performed using Fisher's exact test for 2×c contingency tables (55). For domain-based indicators, mean readiness scores with 95% confidence intervals were calculated. To evaluate the robustness of facility readiness score estimates, we computed composite scores using three approaches: 1) an unweighted average of domain scores (primary analysis), 2) an item-weighted average, proportional to the number of indicators within each domain, and 3) ‘an expert-weighted average’ (medicines = 30%, equipment = 25%, diagnostics = 25%, infrastructure = 10%, and guidelines = 10%) informed by empirical literature ( 15 , 18 ) and policy guidelines ( 5 , 56 , 57 ). The unweighted approach was prioritized for comparability with existing literature. Health worker knowledge scores were analysed using one-way ANOVA with post-hoc Tukey's Honestly Significant Difference (HSD) tests to examine differences by facility level and cadre ( 58 ). We used Welch's ANOVA and robust standard errors to account for unequal variances ( 59 ). Multivariable linear regression examined predictors of knowledge scores, controlling for facility type and cadre. Mixed-effects models accounted for clustering within facilities. T-tests compared knowledge scores between ready and non-ready facilities. All analyses accounted for clustering within facilities using robust standard errors where appropriate. Statistical significance was set at p < 0.05. All analyses were conducted using Stata Statistical Software: Release 15 (StataCorp LLC, College Station, TX, USA). Results Characteristics of health facilities Of the 34 surveyed facilities, Kilifi North sub-county had more facilities 20 (59%) compared with Kilifi South 14 (41%). All surveyed facilities provided hypertension services. In the financial year (FY) 2023/24, the workload across the health facilities was highest in hospitals and least in dispensaries (Table 3 ). Only nine (26%) health facilities operated 24-hour service, and only two (7%) dispensaries did so. The majority (28; 82%) of health facilities were contracted by the social health insurance fund (National Health Insurance Fund) (Table 3 ). Table 3 Characteristics of surveyed health facilities in Kilifi North and South sub-counties Characteristic Dispensary n (%) Health centre n (%) Hospital n (%) Total n (%) Sub-county Kilifi North 17 ( 63 ) 1 ( 25 ) 2 (67) 20 ( 59 ) Kilifi South 10 ( 37 ) 3 (75) 1 ( 33 ) 14 ( 41 ) Location Rural 26 (96) 4 (100) 1 ( 33 ) 31 (91) Urban 1 ( 4 ) 0 (0) 2 (67) 3 ( 9 ) Operate 24 hours No 25 (93) 0 (0) 0 (0) 25 (74) Yes 2 ( 7 ) 4 (100) 3 (100) 9 ( 26 ) Contracted by NHIF No 6 ( 22 ) 0 (0) 0 (0) 6 ( 18 ) Yes 21 (88) 4 (100) 3 (100) 28 (82) Provides hypertension services Yes 27 (100) 4 (100) 3 (100) 34 (100) Workload statistics (FY 2023/24) Catchment population Median (IQR) 8510 (6825–12195) 23876 (15727–36388) 67749 (24790–71151) 10318 (7280–18565) Outpatient visits Median (IQR) 10213 (7097–17803) 32532 (24729–41547) 44584 (8911-185357) 12439 (8756–21714) Total hypertension cases Median (IQR) 135 (56–617) 438 (188–1513) 2322 (1211–12985) 213 (63–877) Total NCD cases* Median (IQR) 300 (139–1285) 869 (427–4496) 2769 (2144–34393) 429 (145–2144) NHIF – National Health Insurance Fund; FY – Financial Year; NCD – Non-Communicable Diseases; IQR - Interquartile Range *Includes hypertension, diabetes, asthma, epilepsy, and mental health conditions Facility readiness for hypertension management by facility level The overall mean readiness index was 42.9% (95% CI: 37.1–48.8), which is lower than the 70% readiness threshold defined in our study. Hospitals had the highest readiness index (79.7%; 95% CI: 54.2-105.4), surpassing the readiness threshold, while the opposite was the case for health centres and dispensaries (Table 4 ). Domain-specific strength was observed in equipment (mean 80.3%; 95% CI: 77.2–83.3), with all health facilities scoring above 70% for this domain. Conversely, availability of essential antihypertensive medicines scored lowest (mean 21.4%; 95% CI: 12.2–30.6), followed by training/guidelines (mean 25%; 95% CI: 11.6–38.1). Table 4 Facility readiness for hypertension management Facility type n Overall readiness index mean (95% CI) Infrastructure index mean (95% CI) Equipment index mean (95% CI) Diagnostics index mean (95% CI) Medicines index mean (95% CI) Guidelines & Training index mean (95% CI) Dispensary 27 37.4 (32.9–41.9) 35.2 (20.1–50.3) 81.0 (77.8–84.1) 27.8 (23.8–31.7) 12.7 (5.0-20.4) 14.8 (2.8–26.9) Health Centre 4 52.7 (32.3–73.1) 56.3 (6.2-106.3) 71.4 (52.9–90.0) 46.9 (27.8–65.9) 39.3 (17.5–61.0) 50.0 (-15.0-115.0) Hospital 3 79.7 (54.2-105.4) 91.7 (55.8-127.5) 85.7 (85.7–85.7) 70.8 (23.4-118.3) 76.2 (55.7–96.7) 83.3 (11.6–155.0) Overall 34 43.0 (37.1–48.8) 42.7 (29.0-56.3) 80.3 (77.2–83.3) 33.8 (27.9–39.8) 21.4 (12.2–30.6) 25.0 (11.6–38.1) Figure 1 shows individual facilities ordered by level of readiness to manage hypertension at the 70% readiness threshold. Although dispensaries were below the readiness threshold, five of them had achieved a higher readiness index than health centres. One health centre achieved a higher readiness index than one of the hospitals, also surpassing the readiness threshold (Fig. 1 ). Facility readiness for hypertension management by sub-county Overall, the mean readiness index was 46.4% (95% CI: 36.5–56.4) and 40.4% (95% CI: 32.8–48.2) in Kilifi South and Kilifi North, respectively. For the antihypertensive medicines domain, the mean index in Kilifi South was 26.5% (95% CI: 12.1–40.9) compared to 17.1% (95% CI: 5.0–30.7) in Kilifi North. The mean readiness index for the equipment domain was similar in the two sub-counties (Fig. 2). Facility readiness by domains of readiness Figure 3 shows facility readiness across five domains. Calcium channel blockers (mean 35.3%; 95% CI: 18.4–52.2) were the most available class of antihypertensives across the health facilities. Overall, 11.8% (95% CI: 0.4–23.2%) of facilities had at least one combination therapy (CT) available. Availability of CT varied markedly by facility level, with hospitals having the highest proportion (66.7%; 95% CI: −76.8–210.1%), followed by health centres (25.0%; 95% CI: −54.6–104.6%) and dispensaries (3.7%, 95% CI: −3.9–11.3%) (results not shown). Only four of the nine combination therapies were available, all in hospitals: losartan+ hydrochlorothiazide (HCTZ), telmisartan + amlodipine, telmisartan + amlodipine + HCTZ, and telmisartan + HCTZ (each 33.3%, 95% CI: −110.1–176.8%). At primary healthcare level, only losartan + HCTZ was found in 3.7% of dispensaries and 25.0% of health centres (results not shown). Ophthalmoscope and electrocardiogram (ECG) machine, and ECG paper were the least available basic equipment and diagnostics, respectively (Fig. 3, panels c and d). The mean overall availability of guidelines and training was only 25% (95% CI: 11.9–38.1) while the mean overall availability of infrastructure was only 42.6% (95% CI: 29.0-56.3), with laboratory infrastructure being the most available (mean 50%; 95% CI: 32.2–67.7) and internet being the least available (mean 29.4%; 95% CI: 13.3–45.5) for this domain (Fig. 3, panels e and f). Health worker knowledge 3.1. Participant characteristics and knowledge A total of 73 (out of 75 approached) health workers from all 34 surveyed facilities completed the hypertension knowledge assessment. The sample consisted predominantly of nursing officers followed by clinical officers, with the majority of health workers coming from dispensaries (Table 5 ). The mean overall knowledge score was 11.9 out of 13 (91.4%; 95% CI 89.3–93.4). Most healthcare workers (97.3%, 71/73) had fair/adequate knowledge, indicating generally strong theoretical knowledge of hypertension management principles. At the same time, over half of FLHWs (52.7%) reported not being familiar with any cardiovascular treatment guidelines, with only 14% (n = 10) reporting familiarity with the 2024 cardiovascular treatment guidelines. 3.2. Knowledge variation by facility level Knowledge scores showed a clear hierarchical pattern across facility levels (ANOVA, F = 7.62, p = 0.001). Hospital-based staff had near-perfect knowledge scores (mean: 12.7/13, 97.6% (95% CI: 95.1–100.0) compared to health centre staff (mean: 12.2/13, 93.8% (95% CI: 88.8–98.9)) and dispensary staff (mean: 11.5/13, 88.7% (95% CI: 86.0-91.4)). However, only the difference between hospital and dispensary staff was statistically significant (mean difference: 1.16 points, 95% CI: 0.42–1.89, p < 0.001) (Table A2 Appendix 2). Table 5 Participant characteristics and knowledge scores Characteristic n (%) Mean score (95% CI) %Score (95% CI) ≥ 10/13 (%) Total 73 (100) 11.9 (11.6–12.1) 91.4 (89.3–93.4) 97.3 Cadre Nursing officer 48 (65.8) 11.4 (11.1–11.7) 87.8 (85.3–90.3) 95.8 Clinical officer 16 (21.9) 12.6 (12.2–13.0) 97.1 (94.1–100.0) 100 Medical officer 5 (6.8) 13.0 (13.0–13.0) 100.0 (100.0-100.0) 100 Consultant Physician 4 (5.5) 13.0 (13.0–13.0) 100.0 (100.0-100.0) 100 Facility Type Dispensary 47 (64.4) 11.5 (11.2–11.9) 88.7 (86.0-91.4) 95.7 Health Centre 10 (13.7) 12.2 (11.5–12.9) 93.8 (88.8–98.9) 100 Hospital 16 (21.9) 12.7 (12.4–13.0) 97.6 (95.1–100.0) 100 3.3. Association of health worker knowledge and facility readiness status Healthcare workers in facilities meeting the hypertension service readiness threshold (≥ 70%) had significantly higher knowledge scores than those in facilities not meeting that threshold (12.6 vs. 11.7 out of 13, mean difference: 0.96 points, t=-3.11, p = 0.003) (Table A3 Appendix 1). This represents a moderate effect size (Cohen's d = 0.79), with staff working in facilities with a high readiness index scoring approximately 7.4 percentage points higher (97.1% vs. 89.7%). 3.4. Cadre-based knowledge differences Knowledge varied substantially by professional cadre (ANOVA, F = 10.71, p < 0.001). Medical officers and consultant physicians had perfect knowledge (13/13, 100%), followed by clinical officers (12.6/13, 97.1% (95% CI: 94.1–100.0)), and nursing officers (11.4/13, 87.8% (95%: 85.3–90.3)) (Table 5 ). In the adjusted multivariable analysis controlling for facility type, clinical officers scored 9.0 percentage points higher than nursing officers (95% CI: 4.0–14.0, p = 0.001), while medical officers scored 9.3 percentage points higher (95% CI: 0.3–18.4, p = 0.044) (Table A4 Appendix 1). 3.5. Remaining knowledge gaps Despite generally strong performance, critical gaps in hypertension management knowledge were identified. Only 69.9% (51/73) of healthcare workers correctly identified calcium channel blocker (CCB)-based combination therapy as the preferred first-line treatment-the most common knowledge gap across all cadres and facility types. Furthermore, 21.9% (16/73) indicated they would (inappropriately) switch a stable hypertensive patient to diazepam and furosemide for isolated headaches, representing a potential patient safety concern. When examining critical errors (defined as mistakes in four high-stakes clinical domains: hypertension definition, medication management, treatment continuity, and follow-up frequency), a facility gradient emerged: dispensary staff averaged 0.45 errors per worker, health centre staff 0.40 errors, and hospital staff only 0.06 errors ( p = 0.03). 3.6. Multivariable analysis of predictors of knowledge scores In the mixed-effects models accounting for facility clustering, cadre remained a strong independent predictor of knowledge after controlling for facility type. Clinical officers scored 8.7 percentage points higher than nursing officers (95% CI: 4.1–13.3, p < 0.001), while facility type differences were attenuated in adjusted models (Table A5 Appendix 1). 3.7 Factors associated with facility readiness for hypertension management We found strong evidence that geographic location, facility type and supervisory support were associated with hypertension service readiness (Table 6 ). A strong urban-rural disparity was observed. While 66.7% (2/3) of urban facilities were classified as ready, only 3.2% (1/31) of rural facilities met the readiness threshold ( p = 0.016). There was strong evidence that facilities that had received a supervisory visit in the past three months were more likely to be classified as ready, at 66.7% (2/3) compared to 3.2% (1/31) of those without a visit ( p = 0.016). Table 6 Facility characteristics and bivariate associations with hypertension service readiness (N = 34) Characteristic Total n (%) Not ready (< 70% index) n (%) Ready (≥ 70% index) n (%) p-value* Total 34 (100.0) 31 (91.2) 3 (8.8) Geographical location Rural 31 (91.2) 30 (96.8) 1 (3.2) 0.016 Urban 3 (8.8) 1 (33.3) 2 (66.7) Facility level Dispensary 7 (79.4) 27 (100.0) 0 (0.0) 0.003 Health Centre 4 (11.8) 3 (75.0) 1 (25.0) Hospital 3 (8.8) 1 (33.3) 2 (66.7) Supervisory visit (past 3 months) No 31 (91.2) 30 (96.8) 1 (3.2) 0.016 Yes 3 (8.8) 1 (33.3) 2 (66.7) * p -values derived from Fisher’s exact test. 3.8 Robustness of readiness scores The overall hypertension service readiness score was calculated using three methodologies (Figure A2 in Appendix 1). Both the unweighted (primary) and item-weighted approaches yielded a score of 42.9%, indicating strong internal consistency within the measurement tool. The expert-weighted score was slightly lower at 41.6%, suggesting that facilities less frequently possessed items designated as high-priority by clinical and operational experts. 3.9 Spatial accessibility The average travel time from any population location in each sub-county to the 34 public health facilities using scenario 4 (walking + motorcycle + vehicle, which was the most practical scenario) was 25 minutes in Kilifi North and 30 minutes in Kilifi South. Scenario 3, which combined walking and use of motorcycle-also a highly used travel scenario in the region - had an average travel time of 27 minutes in Kilifi North and 31 minutes in Kilifi South. In the two sub-counties, over 80 per cent of the total population resided within 30 minutes travel time of a public health facility for the most pragmatic scenario as shown in Fig. 4 . Scenario 1 (walking only) and scenario 2 (motorcycle only) were also analysed to give a broader perspective of the different transport options in the two sub-counties based on the population’s socio-economic class (Fig. 5 ). In scenario 1, the average travel time was 44 minutes in Kilifi North and 47 minutes in Kilifi South while in scenario 2, 15 minutes in Kilifi North and 16 minutes in Kilifi South. Discussion This study presents a comprehensive assessment of hypertension service readiness and spatial accessibility of health facilities in two sub-counties in Kilifi County, Kenya, by integrating structural, human resource, and spatial dimensions. Our findings reveal substantial systemic gaps that threaten equitable access to quality hypertension care in this predominantly rural setting. The overall mean readiness index of 42.9% falls considerably below the 70% threshold defined in our study for effective service delivery. Importantly, whereas the overall mean readiness index reported in our study was lower than that reported in a national study in Kenya for cardiovascular diseases (CVDs), at 69%, it is very similar to the one reported in Nepal (38.1%) for CVDs ( 16 ), in Bangladesh (45.1%) for CVDs ( 19 ) and in Ethiopia (29%) for hypertension ( 25 ). The difference in readiness index scores between our study and the national readiness study in Kenya likely stems from methodological differences in domains of readiness assessed and the sampling approach for health facilities. The strong gradient where no dispensaries met the readiness threshold compared to 25% of health centres and 67% of hospitals is noteworthy. Dispensaries, which constitute the majority (88%) of facilities and form the backbone of primary care delivery in rural Kilifi, demonstrated critical shortfalls in essential antihypertensive medicines (mean availability 12.7%) and staff training (14.8%) required for basic hypertension management. In part, these results can be attributed to national policies that restrict certain commodity and equipment stocking in dispensaries and health centres ( 5 , 47 ). Nevertheless, in a bid to promote access to hypertension care and decongest secondary hospitals, the County Department of Health procures antihypertensive medicines for dispensaries, and facilities engage in adaptive practices such as borrowing medicines from one another to reduce stock-outs ( 46 ). While these coping strategies are important for maintaining continuity of care, as evidenced by our findings, they remain insufficient to ensure consistent availability of essential antihypertensive medicines, especially at lower-level facilities. These contextual realities notwithstanding, the marked disparities in readiness across facility tiers creates a fundamental misalignment with the principle of primary healthcare and forces a de facto rationing of quality care by facility tier and geography. In other words, patients who depend on public primary healthcare do not consistently access the essential service delivery inputs required for the management of hypertension, a finding consistent with facility readiness assessments for NCDs across SSA ( 13 – 15 , 20 , 52 ). The concentration of readiness at higher-level facilities exacerbates inequitable access to quality hypertension care and creates a fundamental mismatch with population health needs as most rural residents depend on lower-level facilities for routine chronic care. Moreover, this gradient in facility readiness for hypertension is potentially a powerful structural determinant of health-seeking behaviour, likely influenced by the well-documented phenomenon of bypassing, a finding documented in a recent study ( 46 ). Three facility-level factors emerged as significant predictors of readiness: urban location, facility level, and recent supervision. The urban-rural disparity was pronounced, with 67% of urban facilities ready versus only 3% of rural facilities. It is important to note that geographic location has been documented as one of the critical factors explaining socioeconomic inequalities in the utilization of screening and treatment interventions for hypertension care in Kenya ( 60 ). The strong association with recent supervision suggests that regular supportive oversight may improve the level of facility readiness for hypertension. Previous studies on health facility readiness for NCDs in Tanzania, Ethiopia and Nepal have also found that urban location, existence of supportive supervision and facility tier as some of the determinants of facility readiness ( 13 , 16 , 25 ). The weakest readiness domains, medicines (21.4%) and training/guidelines (25%), represent critical service delivery bottlenecks. The limited availability of combination therapies (11.8%) documented in our study is particularly concerning as their limited availability alongside other essential antihypertensive classes risks weakening treatment adherence and blood pressure control. Although acceptable in reducing pill burden and improve medication adherence ( 61 ), challenges at multiple health systems levels continue to undermine the implementation of combination therapies in the Kenyan context ( 62 ). Inefficient procurement systems, including restrictive commodity order forms for lower-level facilities, and budgetary constraints have been reported to be among the main causes for the chronic stock out of antihypertensive medication in Kilifi county ( 46 ). Other critical implications of the chronic stock-out of antihypertensives on patients include catastrophic healthcare expenditure, defaulting from care or seeking alternative forms of treatment that may worsen patients’ health conditions ( 28 , 29 ). Health worker knowledge presented a paradoxical picture. While overall knowledge scores were high, critical gaps persisted in first-line treatment selection and medication safety. Only 70% correctly identified calcium channel blocker-based combination therapy as preferred first-line treatment, and 22% indicated they would inappropriately switch stable patients to diazepam and furosemide for isolated headaches, a concerning patient safety issue. This discordance between high overall scores and specific clinical knowledge gaps may reflect the challenge of translating theoretical knowledge into clinical practice, likely because of inadequate dissemination of treatment guidelines. We found that majority of FLHWs (71%) were not familiar with any cardiovascular treatment guidelines and only 14% reported familiarity with the 2024 cardiovascular treatment guidelines. These findings suggests the need for concerted efforts to disseminate the 2024 cardiovascular treatment guidelines and institute measure for routine training of FLWHs on hypertension management, a finding that has been reported elsewhere ( 46 ), as has the knowledge gradient across cadres and facility types ( 22 , 51 , 63 ). Higher-level cadres (medical officers, consultants) who achieved perfect scores work exclusively in hospitals, while nursing officers (who constitute majority of primary healthcare providers in Kilifi and predominantly staff dispensaries) had comparatively lower scores (88%) ( 43 ). This distribution creates a dual burden: the providers with the greatest knowledge gaps work in facilities with the fewest resources. This confounded relationship suggests that both FLHW knowledge and facility-level enabling environments are needed for quality hypertension care. A recent study in Kilifi ( 28 ) has shown patient-safety concerns around administration of antihypertensive medicines, particularly at lower-level facilities and from private pharmacies. This evidence reinforces the urgent needs to train FLHWs and improve regulatory oversight of private pharmacies to mitigate risks arising from inappropriate prescribing or medication handling. Spatial accessibility analysis revealed that 81% of the population could reach a health facility within 30 minutes by a combined travel scenario and when considering a walking scenario, only 9% of the population resided in the marginalised areas, where facilities were least likely to be ready to offer hypertension care. The rural border areas of Kilifi South sub-county exhibited significant areas of marginalisation that could exacerbate existing health disparities despite having good access metrics across the study area. These findings align with studies from Malawi, which found that physical access barriers disproportionately affect rural populations seeking chronic care ( 27 ). It is worthwhile noting that previous studies conducted in Kilifi reported that transport costs incurred during routine clinic appointments were a major patient cost driver when seeking hypertension care ( 28 , 29 ). Therefore, to minimise the transport cost burden on patients and enhance access to care, the county government of Kilifi should prioritise improving road infrastructure that could lower transport costs and prioritize investing in the capacity of the existing lower-level facilities. Hospitals generally had higher readiness index scores, in part, because they are better resourced as they generate their own revenues from user fees, and do not solely rely on county allocations ( 46 ). Nevertheless, this could lead to potential over-reliance on higher-level facilities for routine chronic care, a pattern observed in Tanzania and Uganda ( 13 , 63 ), and contributes to health system inefficiency. This highlights the need to reorient higher-level facilities toward supporting primary care through mentorship, supply chain support, and clear referral protocols as envisioned in the establishment of primary care networks ( 64 ). 4.1 Strengths and limitations The key strengths of this study include its rigorous adaptation of WHO SARA tool, with multiple weighting approaches to test robustness of facility readiness index, integration of high-resolution geospatial analysis, and simultaneous assessment of structural readiness and health worker knowledge. The mixed-methods approach provides a comprehensive health system assessment. Several limitations should be acknowledged. First, the study only focused on public facilities in two sub-counties, which limits the generatability of its findings to Kenya as a whole. Second, the cross-sectional design does not allow for drawing causal inference about factors associated with readiness. Finally, while we assessed spatial accessibility, we did not measure actual utilisation. Future research should consider a longitudinal design to assess readiness trends over time and expand coverage of health facilities to include the private sector and a wider geographic scope. 4.2 Policy implications and recommendations To ensure equitable and accessible hypertension care, we recommend prioritised, multifaceted interventions. First, there is an urgent need to strengthen measures to ensure the availability of essential antihypertensive medicines at dispensary and health centre levels through, for example, increased commodity budgetary allocation and expanding the essential medicines list to allow dispensaries to stock essential commodities. Investing in the capacity of PHC facilities (i.e., through adequate staffing) to ensure they can stock essential antihypertensives. Second, there is a need for hypertension workforce capacity building tailored to nursing officers and clinical officers working in PHC facilities, complemented by targeted treatment guidelines dissemination, regular supportive supervision and mentorship from higher-level facilities. Third, it will be important to develop outreach strategies targeting underserved rural areas of Kilifi South, including transportation subsidies for patients. The Kilifi County Health Department and facility managers could use the readiness findings from this study as a baseline to track and improve readiness in these facilities. Finally, our findings suggest that integrated care redesign where higher-level facilities (hospitals) support primary healthcare facilities through clear referral protocols, mentorship programs, as envisioned in the primary care networks, could enhance access to hypertension care. Priorities should include strengthening medicine supply chains, institutionalising guideline availability and supportive supervision at primary care level. Conclusion Primary healthcare facilities in Kilifi North and South sub-counties of Kilifi County exhibit substantial readiness gaps for hypertension management, characterized by critical shortages of essential medicines, inadequate dissemination of treatment guideline, and pronounced urban-rural and facility-tier disparities. While health worker knowledge is generally adequate, specific clinical knowledge gaps persist, particularly among primary healthcare providers. Spatial access barriers exist in Kilifi South, especially for rural populations dependent on walking. Addressing these interconnected challenges requires coordinated interventions targeting supply chains, workforce capacity, geographic equity, and supportive supervision. As Kenya advances toward universal health coverage, prioritising hypertension care readiness at the primary healthcare level will be essential for equitable, effective prevention and control of this condition. Abbreviations ACEIs – Angiotensin converting enzyme Inhibitors, ARBs – Angiotensin Receptor Blockers, BBs – Beta Blockers, BP – Blood Pressure, CCBs – Calcium channel Blockers, CI – Confidence Interval, CT(s) – Combination therapy / Combination therapies, CVD(s) – Cardiovascular disease(s), DEM – Digital elevation model, ECG – Electrocardiogram, FLHW(s) – Front-line health worker(s), FY – Financial Year, HSD – Honestly Significant Difference (Tukey’s), IHCoR-Africa – Improving Hypertension Control in Rural sub-Saharan Africa, IQR – Interquartile Range, KEMRI – Kenya Medical Research Institute (from institutional names), KHDSS – Kilifi Health and Demographic Surveillance System, LMICs – Low- and middle-income countries, LSHTM – London School of Hygiene and Tropical Medicine, NACOSTI – National Commission for Science, Technology and Innovation, NCD(s) – Non-communicable disease(s), NHIF – National Health Insurance Fund, NIHR – National Institute for Health and Care Research, PEN – Package of essential noncommunicable (disease interventions), PHC – Primary healthcare, REDCap – Research Electronic Data Capture (software), SARA – Service Availability and Readiness Assessment, SDG – Sustainable Development Goal, SSA – Sub-Saharan Africa, TTD(s) – Thiazide & Thiazide-like Diuretics, UHC – Universal health coverage, WHO – World Health Organization Declarations Ethical considerations: All study procedures were carried out in accordance with the Declaration of Helsinki . Ethical approval was obtained from the KEMRI Scientific and Ethics Review Unit (Ref. 4631), LSHTM Ethics Committee (Ref. 28313), and NACOSTI (Ref. NACOSTI/P/23/24745). Kilifi County Health Department gave administrative permission. In addition, permission was sought from sub-county health management teams and from facility managers of all participating facilities prior to data collection. During the informed consent process, all potential participants were clearly informed that participation in the study was entirely voluntary. They were assured that they had the right to decline to answer any question or withdraw from the study at any time without any penalty or effect on their access to services, and that no harm would come to them or their families as a result of their participation decisions. All members of the research team completed accredited human subjects’ protection training prior to engaging in any study‑related procedures. Data collection commenced only after participants had provided written informed consent. Any sensitive information obtained during the study was de‑identified, and all study data were stored securely with password protection and access limited strictly to authorized research personnel for legitimate research purposes. Consent for publication: This manuscript was written with the permission of Director KEMRI CGMRC. Availability of data and materials: The data underlying this article cannot be shared publicly due to confidentiality of some of the data. Data are, however, available from the authors upon reasonable request and with permission of KEMRI Wellcome Trust Data Governance Committee. Competing Interests: None declared. Funding: This project is funded by the National Institute for Health and Care Research (NIHR) under its ‘Global Health Research Units and Groups Programme’ (Grant Reference Number NIHR134544). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors’ contributions: Conception or design of the work: R.O., J.A., E.B., B.T., E.N., P.P., and A.E. Data collection: R.O., E.M., R.L., A.B., C.M., E.S., C.K., and J.B. Data analysis and interpretation: R.O., N.K., E.M., N.A., A.E., P.P., E.B., E.N., and B.T. Drafting the article: R.O. Critical revision of the article: R.O., E.M., N.A., B.T., E.N., P.P., and A.E. All authors have read and approved the manuscript. Acknowledgments: We thank the Kilifi County Health Department, facility staff, and research assistants for their cooperation. References World Health Organization. Noncommunicable diseases progress monitor 2025. World Health Organization; 2025. Watkins DA, Danforth K, Ahmed S, Chisholm D, Cieza A, Iunes R, et al. 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Supplementary Files Appendix1StudySettingCharacteristicsandAdditionalFacilityReadinessResults.docx Appendix2HFATool17Sep2024finalclean.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor invited by journal 10 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 01 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Oyando","email":"data:image/png;base64,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","orcid":"","institution":"Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":true,"prefix":"","firstName":"Robinson","middleName":"","lastName":"Oyando","suffix":""},{"id":605059989,"identity":"295b7d9b-ea92-480c-88a6-d82a62eeac3e","order_by":1,"name":"Eda Mumo","email":"","orcid":"","institution":"Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Eda","middleName":"","lastName":"Mumo","suffix":""},{"id":605059994,"identity":"dd2c1559-cffe-4143-9e89-df45e322f78d","order_by":2,"name":"Ruth Lucinde","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Lucinde","suffix":""},{"id":605059999,"identity":"55d3286d-c1ae-4bdb-ab35-4ab44d63cb99","order_by":3,"name":"Aurelia Brazeal","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Aurelia","middleName":"","lastName":"Brazeal","suffix":""},{"id":605060003,"identity":"528f907c-d47c-4cf2-bdfe-40ae89e1d3d5","order_by":4,"name":"Clement Mwagwabi","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Clement","middleName":"","lastName":"Mwagwabi","suffix":""},{"id":605060007,"identity":"cbc17189-d3ac-4f10-ba97-ee5c47b6514d","order_by":5,"name":"Elminah Saru","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Elminah","middleName":"","lastName":"Saru","suffix":""},{"id":605060017,"identity":"56a14de0-bac7-421a-aa6d-7ae998a4ee9d","order_by":6,"name":"Nancy Kagwanja","email":"","orcid":"","institution":"Health Systems and Research Ethics Department, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"","lastName":"Kagwanja","suffix":""},{"id":605060022,"identity":"90c5be21-1025-4192-b841-291612fb42ad","order_by":7,"name":"Catherine Kalu","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Kalu","suffix":""},{"id":605060027,"identity":"7270a22f-0aa9-482a-92e3-319e950af55e","order_by":8,"name":"Juliet Awori","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Juliet","middleName":"","lastName":"Awori","suffix":""},{"id":605060030,"identity":"ce29b56b-195b-45ea-a635-dec5f56c84fb","order_by":9,"name":"James Bukosia","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Bukosia","suffix":""},{"id":605060033,"identity":"59578214-fb78-4c5b-8704-ee607e00ff55","order_by":10,"name":"Nadia Aliyan","email":"","orcid":"","institution":"Kilifi County Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Aliyan","suffix":""},{"id":605060035,"identity":"0f6fb5c0-0e64-4b24-8c70-03aab3232db6","order_by":11,"name":"Pablo Perel","email":"","orcid":"","institution":"Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Perel","suffix":""},{"id":605060037,"identity":"ebd47bf9-3c29-426d-9f7a-05060f302152","order_by":12,"name":"Ellen Nolte","email":"","orcid":"","institution":"Department of Health Service Research and Policy, London School of Hygiene and Tropical Medicine Faculty of Public Health and Policy","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Nolte","suffix":""},{"id":605060040,"identity":"85d2a69c-9db5-4779-8ae1-e723948484ee","order_by":13,"name":"Anthony Etyang","email":"","orcid":"","institution":"Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Etyang","suffix":""},{"id":605060043,"identity":"1453ec20-06dd-4e38-bc61-6ae1959431fe","order_by":14,"name":"Edwine Barasa","email":"","orcid":"","institution":"Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Edwine","middleName":"","lastName":"Barasa","suffix":""},{"id":605060053,"identity":"bb5fb841-7297-4626-bc5b-10c5b6caa6ef","order_by":15,"name":"Benjamin Tsofa","email":"","orcid":"","institution":"Health Systems and Research Ethics Department, KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Tsofa","suffix":""}],"badges":[],"createdAt":"2026-03-01 17:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781467,"identity":"a3816cdd-87d0-4c35-910a-6e56478b8802","added_by":"auto","created_at":"2026-03-17 07:55:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11591,"visible":true,"origin":"","legend":"\u003cp\u003eFacilities ordered by readiness for hypertension management\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/1dd1418bacf38470542b0147.png"},{"id":104573401,"identity":"83d33621-979f-4800-9a57-0890616d89c7","added_by":"auto","created_at":"2026-03-13 13:22:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26006,"visible":true,"origin":"","legend":"\u003cp\u003eDomain readiness by sub-county\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/3bed98fd8628c6281a4950c4.png"},{"id":104573395,"identity":"a6759050-17a6-4fae-b6e7-f65433b6ec5c","added_by":"auto","created_at":"2026-03-13 13:22:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103124,"visible":true,"origin":"","legend":"\u003cp\u003eReadiness for hypertension management by domain\u003c/p\u003e\n\u003cp\u003eACEIs - Angiotensin converting enzyme inhibitors; ARBs - Angiotensin receptor blockers; BBs – Beta Blockers; CCBs - Calcium channel blockers; CTs - Combination therapies; TTDs - Thiazide \u0026amp; Thiazide-like Diuretics; ECG – Electrocardiogram; BP – Blood Pressure\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/81a04c0a0f47469009e2eed0.png"},{"id":104781027,"identity":"36400437-b0c5-4512-95d0-7d0f96dc6f58","added_by":"auto","created_at":"2026-03-17 07:54:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":562719,"visible":true,"origin":"","legend":"\u003cp\u003ePublic health facility geographic accessibility based on travel time modelled using two travel scenarios i) scenario 4 (walking + motorcycle + vehicle: most pragmatic) and ii) scenario 3 (walking + motorcycle).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/ae48382b020e268bc409151a.png"},{"id":104573396,"identity":"10955368-0380-4920-9a4b-9d98ee20ba6a","added_by":"auto","created_at":"2026-03-13 13:22:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":531432,"visible":true,"origin":"","legend":"\u003cp\u003ePublic health facility geographic accessibility based on travel time modelled using two travel scenarios i) scenario 2 (motorcycle only) and ii) scenario 1 (walking only).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/cf7ef30d9ddedd54023e1034.png"},{"id":104834918,"identity":"cfa7b5ab-73d5-4d25-bdb1-b61442fecef7","added_by":"auto","created_at":"2026-03-17 17:35:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3034857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/eb681134-f41a-48b4-99bd-36661a60da6d.pdf"},{"id":104573399,"identity":"58b90c56-940f-4d6f-8159-c3301fa1bbf8","added_by":"auto","created_at":"2026-03-13 13:22:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":497769,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1StudySettingCharacteristicsandAdditionalFacilityReadinessResults.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/f3e505b8ec0748408ddcf52f.docx"},{"id":104573398,"identity":"fb531a11-3199-454b-8dcd-c93459e2087a","added_by":"auto","created_at":"2026-03-13 13:22:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90046,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2HFATool17Sep2024finalclean.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003184/v1/bf14714eca2dccf7c0fb461d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Facility Readiness and Spatial Accessibility for the Management of Hypertension in Kilifi County, Kenya: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe rapidly increasing burden of non-communicable diseases (NCDs) globally threatens health systems, particularly in low-and middle-income countries (LMICs), in achieving Sustainable Development Goal (SDG) targets 3.4, on reducing premature NCD mortality by one-third by 2030, and 3.8 on universal health coverage (UHC) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In sub-Saharan Africa (SSA), NCDs account for 37% of all deaths and are projected to surpass communicable, maternal, neonatal and nutritional diseases combined by 2030 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Kenya, NCDs account for 50% of hospitalisations and 41% of all deaths (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHealth systems in many LMICs are poorly prepared to meet the needs of people living with NCDs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A continued focus on acute conditions and maternal and child health has meant that less \u0026ndash; albeit slowly growing \u0026ndash; emphasis has been placed on creating more coordinated services across primary and specialist care that are required to improve outcomes for chronic NCDs (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This lack of preparedness has been linked to about half of NCD deaths in LMICs (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere has been increasing interest in understanding health facility readiness for managing NCDs in LMICs (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Studies have typically examined multiple NCDs (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), using adaptations of the World Health Organization (WHO) Service Availability and Readiness Assessment (SARA) tool (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and including public and private sector facilities (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These studies consistently highlight substantial gaps across key health system components such as: limited availability of essential NCD medications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e); lack of diagnostic equipment (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and absence of treatment protocols or guidelines (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e); along with insufficient infrastructure (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and inadequate number of trained front-line health workers (FLHWs) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Other additional challenges include inadequate health information systems, patient education materials, referral and follow-up mechanisms, financing, and governance structures for NCD care (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Facility-level characteristics such as ownership, level of care, location, supervision practices, quality assurance and review of patients\u0026rsquo; feedback were found to be significantly associated with the level of readiness for management of NCDs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Despite limited longitudinal evidence, one study from Ethiopia reported persistently low, and a declining trend in facility readiness over time, highlighting systemic challenges in improving and sustaining NCD service capacity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo areas that have received less attention regarding facility readiness are (i) spatial accessibility of health facilities across different levels of care for NCDs and (ii) FLHWs\u0026rsquo; knowledge of NCDs. Spatial accessibility refers to the ease with which individuals are able to reach a healthcare facility from their homes, typically measured by distance, travel time or cost (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Regarding FLHWs\u0026rsquo; knowledge, research from India, Tanzania, and Uganda suggests that higher-cadre staff, such as doctors, are more knowledgeable in NCD management compared to nurses and other clinical cadres, but the overall evidence remains sparse. Both spatial accessibility and FLHW knowledge shape access and utilization of care: few health facilities, resulting in long travel distance or high transport costs, can deter access (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In contrast, inadequate provider knowledge or perceived competency may drive patients to bypass local primary healthcare facilities in favour of hospitals or private providers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding service availability and facility readiness is critical to guide policy reforms that re-orient services toward integrated NCD care and re-design delivery models for efficiency, equity and sustainability. But these issues are not well understood in the context of Kenya. This study seeks to contribute to bridging this evidence gap. Using hypertension, which is a is a prevalent (33%) condition in Kenya (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), as a lens through which to examine NCD management, we investigated facility readiness, FLHW knowledge, and spatial accessibility of facilities, along with the factors associated with facility readiness, in two rural sub-counties of Kilifi County.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was embedded within a larger project on Improving Hypertension Control in Rural sub-Saharan Africa (IHCoR-Africa), which aimed to co-develop and evaluate a community-centred approach to improve the management of hypertension in two rural sites (Kilifi North and Kilifi South) in Kilifi, Kenya, and Kiang West, the Gambia (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In this paper, we report the facility readiness assessment conducted for the Kenyan component of the study. The selection of the two rural sites in Kilifi was informed by discussions and consultations between the study team and the Kilifi County Department of Health Management Team.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eBetween September and October 2024, we conducted a cross-sectional survey of all public health facilities in Kilifi North and Kilifi South sub-counties (see Table A1 and Figure A1 in Appendix 1) of Kilifi County, located in the coastal region of Kenya. Kilifi North and Kilifi South sub-counties are among the highly populated sub-counties in Kilifi County with a population of over 201,300 and 232,739, respectively (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The county\u0026rsquo;s primary economic activities include subsistence agriculture, fishing, and tourism (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The area has also been reported to experience a high burden of stroke and heart failure (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Along with 46 semi-autonomous counties, the Kilifi government shares responsibilities for the health system with the national government (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Counties provide most health services (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Healthcare is organised into four tiers: community health services, primary healthcare facilities (PHC) (dispensaries and health centres), secondary hospitals (sub-county and county referral hospitals), and tertiary referral hospitals (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), with the latter overseen by the national government. Services are delivered by public and private providers, with private provision slightly dominating. Hypertension services should be provided across all the four tiers (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), but, in practice, hypertension screening and follow-up at the community level have remained ad hoc, with some primary healthcare facilities not offering hypertension management services at all (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eService delivery in Kilifi County mirrors the tiered system documented in the national health policy guidelines (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Access to PHC has improved with the building of new dispensaries, but NCD management at the community level remains ad hoc, and many PHC facilities lack the staffing, essential medicines, and equipment for hypertension management (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Secondary care (Level 4) is limited, with only three of nine designated hospitals fully operational, requiring patients to travel long distances to access specialised NCD services (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Human resources are heavily skewed towards higher-level hospitals, while weaknesses in the supply chain cause frequent stock-outs of essential medicines. Inadequate availability of antihypertensive medicines is in part explained by restrictions in stocking certain antihypertensive classes at PHC facilities (especially dispensaries) compare to higher level hospitals (\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). As a result, and again mirroring the rest of the country, hypertension service delivery remains inconsistent, poorly coordinated, and inaccessible to the population in need (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eWe conducted a cross-sectional survey of all public health facilities in Kilifi North and South sub-counties of Kilifi County.\u003c/p\u003e\n\u003ch3\u003eHealth facility and health care worker selection\u003c/h3\u003e\n\u003cp\u003eAll 34 public health facilities located in both sub-counties were included in the study (Figure A1 in Appendix 1). These included one county referral hospital and two sub-county hospitals (Level 4), four health centres (Level 3), and 27 dispensaries (Level 2). The list of 34 health facilities was obtained from the Kenya master facility list (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and was validated by the sub-county health management teams in Kilifi North and South sub-counties.\u003c/p\u003e \u003cp\u003eFLHWs providing outpatient care to people living with hypertension were eligible for enrolment in the study. Lists of eligible FLHWs were provided by facility managers and included consultant physicians, medical doctors, clinical officers (non-medical doctor clinicians) (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and nurses. All FLHWs offering hypertension care at respective health facilities were approached on the day of facility assessment, briefed about the study and invited to participate in the study by responding to the self-administered questionnaires at a time convenient to them. All potential FLHWs consented to participate in the study except two.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected using an adapted version of the WHO SARA questionnaire (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The adapted structured facility questionnaire consisted of seven modules (Appendix 2), which included questions informed by Kenya\u0026rsquo;s 2024 cardiovascular treatment guidelines (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and the 2023 essential medicines list (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In the adapted SARA questionnaire, we added questions on (i) equipment required for the management of hypertension and (ii) the availability of essential antihypertensive medicines by level of care, respectively. Validation and standardisation of the data collection tool was undertaken in three steps. First, the tool was aligned with questionnaires from previous studies that have conducted facility readiness assessments for hypertension by including related and relevant questions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Second, we consulted with four medical experts (one consultant physician, three medical officers and one pharmacist) to review the tool and assess its relevance and applicability to Kenya\u0026rsquo;s health system. Third, the tool was pilot tested at six health facilities, and adaptations were made as appropriate.\u003c/p\u003e \u003cp\u003eAt each facility, trained research assistants collected data using direct observation, facility record review, and structured interviews with facility managers or other competent FLWHs who were familiar with facility operations. To minimise social desirability bias in respondent-reported information, wherever possible, we prioritised objective verification through direct observation of infrastructure, equipment, and service readiness indicators rather than relying solely on self-report. Information provided during interviews was cross-checked against observed conditions and facility records to enhance accuracy and reduce over- or under-reporting. To assess hypertension management knowledge, as part of the adapted SARA tool, we administered a structured 13-item questionnaire covering clinical evaluation, treatment initiation, and follow-up principles aligned with Kenya's 2024 cardiovascular disease management guidelines. Knowledge items included true/false statements and case-based scenarios (Appendix 2). Data from health facilities were collected electronically on tablets using RedCap (version 13.1.5) software. All study participants provided written informed consent prior to participating in study activities.\u003c/p\u003e\n\u003ch3\u003eService availability and readiness indicator variables\u003c/h3\u003e\n\u003cp\u003eHypertension-specific service availability was assessed by asking respondents from sampled facilities whether they offered diagnosis and/or management services for hypertension. Facility readiness was then evaluated using predefined tracer items across five domains: basic infrastructure, equipment, diagnostic capacity, trained staff and guidelines, and medicines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each domain, a mean availability score was computed, and an overall readiness score was derived as the average across all domains, expressed as a percentage. Following previous studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), facilities scoring\u0026thinsp;\u0026ge;\u0026thinsp;70% were classified as \u0026ldquo;ready\u0026rdquo; to deliver hypertension interventions and vice versa if the score was below the threshold.\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\u003eStudy outcomes assessed across health facilities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eService Provision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstrument for data collection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDerivation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber and proportion of outpatient visits related to hypertension, diabetes, comorbid (hypertension and diabetes) and other NCDs (mental disorders, cancer, chronic obstructive pulmonary diseases, epilepsy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eService statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrom July 2023 to June 2024, and types of facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARA Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of 1) laboratory services, and functional 2) facility-owned phone, 3) computer, and 4) internet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARA Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of functional: 1) blood pressure machine, 2) weight machine, 3) height meter, 4) measuring tape, 5) stethoscope, 6) ophthalmoscope\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARA Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of 1) Electrocardiogram (ECG) machine, 2) ECG thermal paper, 3) Strips for urinalysis, 4) Glucometer, 5) Glucometer strips, 6) Biochemistry equipment, 7) Haematology equipment, 8) X-ray machine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrained staff and treatment guidelines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARA Questionnaire and Self-completed questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReporting hypertension training in past year and familiarity with cardiovascular treatment guidelines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential antihypertensive medicines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSARA Questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAvailability of unexpired 1) \u003cb\u003eAngiotensin converting enzyme Inhibitors (ACEIs)\u003c/b\u003e [enalapril], 2) \u003cb\u003eAngiotensin Receptor Blockers (ARBs)\u003c/b\u003e [Losartan, Telmisartan], 3) \u003cb\u003eBeta Blockers (BBs)\u003c/b\u003e [Bisoprolol, Labetalol, Metoprolol, Nebivolol], 4) \u003cb\u003eCalcium channel Blockers\u003c/b\u003e (CCBs) [Amlodipine, Nifedipine], 5) \u003cb\u003eThiazide \u0026amp; Thiazide-like Diuretics (TTD)\u003c/b\u003e [Chlorthalidone, Hydrochlorothiazide, Indapamide], 6) \u003cb\u003eCombination antihypertensive medicines\u003c/b\u003e [Amlodipine\u0026thinsp;+\u0026thinsp;Hydrochlorothiazide, Amlodipine\u0026thinsp;+\u0026thinsp;Indapamide, Losartan\u0026thinsp;+\u0026thinsp;Hydrochlorothiazide, Lisinopril\u0026thinsp;+\u0026thinsp;Hydrochlorothiazide, Perindopril\u0026thinsp;+\u0026thinsp;Amlodipine, Perindopril\u0026thinsp;+\u0026thinsp;Amlodipine\u0026thinsp;+\u0026thinsp;Indapamide, Telmisartan\u0026thinsp;+\u0026thinsp;Amlodipine, Telmisartan\u0026thinsp;+\u0026thinsp;Amlodipine\u0026thinsp;+\u0026thinsp;Hydrochlorothiazide, Telmisartan\u0026thinsp;+\u0026thinsp;Hydrochlorothiazide], 7) \u003cb\u003eOther anti-hypertensive agents\u003c/b\u003e [ Methyldopa, Spironolactone, Hydralazine, Doxazosin, Prazosin, Phenoxybenzamine, Bosentan, Sildenafil, Tadalafil]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare worker knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComfort in managing hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-completed questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReporting \"poor\", \u0026ldquo;satisfactory\u0026rdquo; or \"good\" when asked whether he/she feel comfortable with managing hypertension\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair knowledge in hypertension management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-completed questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessed via case scenario questionnaires for hypertension and defined as scoring at least 10/13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHealth worker knowledge assessment\u003c/h2\u003e \u003cp\u003eA 13-item knowledge assessment covering clinical evaluation, treatment initiation, and follow-up was administered to any FLHW who managed people with hypertension at respective health facilities (Appendix 2). Scores were categorised as having at least fair knowledge (10/13) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial accessibility analysis\u003c/h3\u003e\n\u003cp\u003eSpatial datasets assembled for the accessibility modelling included a geocoded list of health facilities, detailed road network, land cover, a digital elevation model (DEM), travel barriers (national parks, community nature reserves, forest reserves, wetland areas) and a gridded population dataset. Spatial accessibility to the 34 public health facilities was modelled as travel time using the accessibility analysis module in WHO \u003cem\u003eAccessMod Tool (version 5.8)\u003c/em\u003e (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The analysis considered four travelling scenarios based on the area\u0026rsquo;s local context with defined travel speeds for each land cover and road class as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the combined travel scenarios, it was assumed that rural areas are characterised by poor road networks, and that the individual seeking care would have to walk across various land cover types before getting a motorised transport (motorcycle) on the minor roads and finally a vehicle on the major roads (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) In each scenario, travel time was modelled for all facilities collectively and the mean travel time for each sub-county per scenario obtained. Population residing within 30 minutes, 60 minutes, and over 1 hour travel time for each subcounty were also delineated.\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\u003eDefined travel speeds above each road class and land cover type used to model travel time to the 34 public health facilities in Kilifi North and Kilifi South for four selected travel scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRoad and Land cover type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eDefined Speed (Km/hr) for the travel scenario\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1 (Walking)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (Motorcycle)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (Combined: 1 \u0026amp; 2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (Combined: 1, 2 \u0026amp; Vehicle)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand Cover Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7..5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare ground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFacility readiness for hypertension management was visualised using graphical methods. Continuous variables were summarised as mean (95% confidence intervals) or median (interquartile range), depending on distribution. Descriptive analyses were conducted for all indicators and results were presented as frequencies and percentages. Comparisons of outcomes by facility level were performed using Fisher's exact test for 2\u0026times;c contingency tables (55). For domain-based indicators, mean readiness scores with 95% confidence intervals were calculated.\u003c/p\u003e \u003cp\u003eTo evaluate the robustness of facility readiness score estimates, we computed composite scores using three approaches: 1) an unweighted average of domain scores (primary analysis), 2) an item-weighted average, proportional to the number of indicators within each domain, and 3) \u0026lsquo;an expert-weighted average\u0026rsquo; (medicines\u0026thinsp;=\u0026thinsp;30%, equipment\u0026thinsp;=\u0026thinsp;25%, diagnostics\u0026thinsp;=\u0026thinsp;25%, infrastructure\u0026thinsp;=\u0026thinsp;10%, and guidelines\u0026thinsp;=\u0026thinsp;10%) informed by empirical literature (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and policy guidelines (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The unweighted approach was prioritized for comparability with existing literature.\u003c/p\u003e \u003cp\u003eHealth worker knowledge scores were analysed using one-way ANOVA with post-hoc Tukey's Honestly Significant Difference (HSD) tests to examine differences by facility level and cadre (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). We used Welch's ANOVA and robust standard errors to account for unequal variances (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Multivariable linear regression examined predictors of knowledge scores, controlling for facility type and cadre. Mixed-effects models accounted for clustering within facilities. T-tests compared knowledge scores between ready and non-ready facilities. All analyses accounted for clustering within facilities using robust standard errors where appropriate. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using Stata Statistical Software: Release 15 (StataCorp LLC, College Station, TX, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of health facilities\u003c/h2\u003e\n \u003cp\u003eOf the 34 surveyed facilities, Kilifi North sub-county had more facilities 20 (59%) compared with Kilifi South 14 (41%). All surveyed facilities provided hypertension services. In the financial year (FY) 2023/24, the workload across the health facilities was highest in hospitals and least in dispensaries (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Only nine (26%) health facilities operated 24-hour service, and only two (7%) dispensaries did so. The majority (28; 82%) of health facilities were contracted by the social health insurance fund (National Health Insurance Fund) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of surveyed health facilities in Kilifi North and South sub-counties\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDispensary n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealth centre n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHospital n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub-county\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKilifi North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKilifi South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperate 24 hours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContracted by NHIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvides hypertension services\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkload statistics (FY 2023/24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatchment population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8510 (6825\u0026ndash;12195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23876 (15727\u0026ndash;36388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67749 (24790\u0026ndash;71151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10318 (7280\u0026ndash;18565)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutpatient visits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10213 (7097\u0026ndash;17803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32532 (24729\u0026ndash;41547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44584 (8911-185357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12439 (8756\u0026ndash;21714)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal hypertension cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135 (56\u0026ndash;617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438 (188\u0026ndash;1513)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2322 (1211\u0026ndash;12985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213 (63\u0026ndash;877)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal NCD cases*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 (139\u0026ndash;1285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869 (427\u0026ndash;4496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2769 (2144\u0026ndash;34393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429 (145\u0026ndash;2144)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNHIF \u0026ndash; National Health Insurance Fund; FY \u0026ndash; Financial Year; NCD \u0026ndash; Non-Communicable Diseases; IQR - Interquartile Range\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e*Includes hypertension, diabetes, asthma, epilepsy, and mental health conditions\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eFacility readiness for hypertension management by facility level\u003c/h2\u003e\n \u003cp\u003eThe overall mean readiness index was 42.9% (95% CI: 37.1\u0026ndash;48.8), which is lower than the 70% readiness threshold defined in our study. Hospitals had the highest readiness index (79.7%; 95% CI: 54.2-105.4), surpassing the readiness threshold, while the opposite was the case for health centres and dispensaries (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Domain-specific strength was observed in equipment (mean 80.3%; 95% CI: 77.2\u0026ndash;83.3), with all health facilities scoring above 70% for this domain. Conversely, availability of essential antihypertensive medicines scored lowest (mean 21.4%; 95% CI: 12.2\u0026ndash;30.6), followed by training/guidelines (mean 25%; 95% CI: 11.6\u0026ndash;38.1).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFacility readiness for hypertension management\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFacility type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall readiness index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInfrastructure index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquipment index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiagnostics index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedicines index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGuidelines \u0026amp; Training index mean (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispensary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4 (32.9\u0026ndash;41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.2 (20.1\u0026ndash;50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0 (77.8\u0026ndash;84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8 (23.8\u0026ndash;31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.7 (5.0-20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.8 (2.8\u0026ndash;26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.7 (32.3\u0026ndash;73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.3 (6.2-106.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.4 (52.9\u0026ndash;90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.9 (27.8\u0026ndash;65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.3 (17.5\u0026ndash;61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0 (-15.0-115.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.7 (54.2-105.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.7 (55.8-127.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7 (85.7\u0026ndash;85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.8 (23.4-118.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.2 (55.7\u0026ndash;96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.3 (11.6\u0026ndash;155.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e43.0 (37.1\u0026ndash;48.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e42.7 (29.0-56.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.3 (77.2\u0026ndash;83.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e33.8 (27.9\u0026ndash;39.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.4 (12.2\u0026ndash;30.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.0 (11.6\u0026ndash;38.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows individual facilities ordered by level of readiness to manage hypertension at the 70% readiness threshold. Although dispensaries were below the readiness threshold, five of them had achieved a higher readiness index than health centres. One health centre achieved a higher readiness index than one of the hospitals, also surpassing the readiness threshold (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eFacility readiness for hypertension management by sub-county\u003c/h2\u003e\n \u003cp\u003eOverall, the mean readiness index was 46.4% (95% CI: 36.5\u0026ndash;56.4) and 40.4% (95% CI: 32.8\u0026ndash;48.2) in Kilifi South and Kilifi North, respectively. For the antihypertensive medicines domain, the mean index in Kilifi South was 26.5% (95% CI: 12.1\u0026ndash;40.9) compared to 17.1% (95% CI: 5.0\u0026ndash;30.7) in Kilifi North. The mean readiness index for the equipment domain was similar in the two sub-counties (Fig. 2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eFacility readiness by domains of readiness\u003c/h2\u003e\n \u003cp\u003eFigure 3 shows facility readiness across five domains. Calcium channel blockers (mean 35.3%; 95% CI: 18.4\u0026ndash;52.2) were the most available class of antihypertensives across the health facilities. Overall, 11.8% (95% CI: 0.4\u0026ndash;23.2%) of facilities had at least one combination therapy (CT) available. Availability of CT varied markedly by facility level, with hospitals having the highest proportion (66.7%; 95% CI: \u0026minus;76.8\u0026ndash;210.1%), followed by health centres (25.0%; 95% CI: \u0026minus;54.6\u0026ndash;104.6%) and dispensaries (3.7%, 95% CI: \u0026minus;3.9\u0026ndash;11.3%) (results not shown). Only four of the nine combination therapies were available, all in hospitals: losartan+ hydrochlorothiazide (HCTZ), telmisartan\u0026thinsp;+\u0026thinsp;amlodipine, telmisartan\u0026thinsp;+\u0026thinsp;amlodipine\u0026thinsp;+\u0026thinsp;HCTZ, and telmisartan\u0026thinsp;+\u0026thinsp;HCTZ (each 33.3%, 95% CI: \u0026minus;110.1\u0026ndash;176.8%). At primary healthcare level, only losartan\u0026thinsp;+\u0026thinsp;HCTZ was found in 3.7% of dispensaries and 25.0% of health centres (results not shown).\u003c/p\u003e\n \u003cp\u003eOphthalmoscope and electrocardiogram (ECG) machine, and ECG paper were the least available basic equipment and diagnostics, respectively (Fig. 3, panels c and d). The mean overall availability of guidelines and training was only 25% (95% CI: 11.9\u0026ndash;38.1) while the mean overall availability of infrastructure was only 42.6% (95% CI: 29.0-56.3), with laboratory infrastructure being the most available (mean 50%; 95% CI: 32.2\u0026ndash;67.7) and internet being the least available (mean 29.4%; 95% CI: 13.3\u0026ndash;45.5) for this domain (Fig. 3, panels e and f).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eHealth worker knowledge\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e3.1. Participant characteristics and knowledge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA total of 73 (out of 75 approached) health workers from all 34 surveyed facilities completed the hypertension knowledge assessment. The sample consisted predominantly of nursing officers followed by clinical officers, with the majority of health workers coming from dispensaries (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The mean overall knowledge score was 11.9 out of 13 (91.4%; 95% CI 89.3\u0026ndash;93.4). Most healthcare workers (97.3%, 71/73) had fair/adequate knowledge, indicating generally strong theoretical knowledge of hypertension management principles. At the same time, over half of FLHWs (52.7%) reported not being familiar with any cardiovascular treatment guidelines, with only 14% (n\u0026thinsp;=\u0026thinsp;10) reporting familiarity with the 2024 cardiovascular treatment guidelines.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2. Knowledge variation by facility level\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKnowledge scores showed a clear hierarchical pattern across facility levels (ANOVA, F\u0026thinsp;=\u0026thinsp;7.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Hospital-based staff had near-perfect knowledge scores (mean: 12.7/13, 97.6% (95% CI: 95.1\u0026ndash;100.0) compared to health centre staff (mean: 12.2/13, 93.8% (95% CI: 88.8\u0026ndash;98.9)) and dispensary staff (mean: 11.5/13, 88.7% (95% CI: 86.0-91.4)). However, only the difference between hospital and dispensary staff was statistically significant (mean difference: 1.16 points, 95% CI: 0.42\u0026ndash;1.89,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table A2 Appendix 2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipant characteristics and knowledge scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean score (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%Score (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;10/13 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9 (11.6\u0026ndash;12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.4 (89.3\u0026ndash;93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCadre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNursing officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4 (11.1\u0026ndash;11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.8 (85.3\u0026ndash;90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6 (12.2\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.1 (94.1\u0026ndash;100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0 (13.0\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 (100.0-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsultant Physician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0 (13.0\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0 (100.0-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispensary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5 (11.2\u0026ndash;11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.7 (86.0-91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.2 (11.5\u0026ndash;12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.8 (88.8\u0026ndash;98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.7 (12.4\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.6 (95.1\u0026ndash;100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.3. Association of health worker knowledge and facility readiness status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHealthcare workers in facilities meeting the hypertension service readiness threshold (\u0026ge;\u0026thinsp;70%) had significantly higher knowledge scores than those in facilities not meeting that threshold (12.6 vs. 11.7 out of 13, mean difference: 0.96 points, t=-3.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) (Table A3 Appendix 1). This represents a moderate effect size (Cohen\u0026apos;s d\u0026thinsp;=\u0026thinsp;0.79), with staff working in facilities with a high readiness index scoring approximately 7.4 percentage points higher (97.1% vs. 89.7%).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.4. Cadre-based knowledge differences\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKnowledge varied substantially by professional cadre (ANOVA, F\u0026thinsp;=\u0026thinsp;10.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Medical officers and consultant physicians had perfect knowledge (13/13, 100%), followed by clinical officers (12.6/13, 97.1% (95% CI: 94.1\u0026ndash;100.0)), and nursing officers (11.4/13, 87.8% (95%: 85.3\u0026ndash;90.3)) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the adjusted multivariable analysis controlling for facility type, clinical officers scored 9.0 percentage points higher than nursing officers (95% CI: 4.0\u0026ndash;14.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while medical officers scored 9.3 percentage points higher (95% CI: 0.3\u0026ndash;18.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) (Table A4 Appendix 1).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.5. Remaining knowledge gaps\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDespite generally strong performance, critical gaps in hypertension management knowledge were identified. Only 69.9% (51/73) of healthcare workers correctly identified calcium channel blocker (CCB)-based combination therapy as the preferred first-line treatment-the most common knowledge gap across all cadres and facility types. Furthermore, 21.9% (16/73) indicated they would (inappropriately) switch a stable hypertensive patient to diazepam and furosemide for isolated headaches, representing a potential patient safety concern. When examining critical errors (defined as mistakes in four high-stakes clinical domains: hypertension definition, medication management, treatment continuity, and follow-up frequency), a facility gradient emerged: dispensary staff averaged 0.45 errors per worker, health centre staff 0.40 errors, and hospital staff only 0.06 errors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.6. Multivariable analysis of predictors of knowledge scores\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the mixed-effects models accounting for facility clustering, cadre remained a strong independent predictor of knowledge after controlling for facility type. Clinical officers scored 8.7 percentage points higher than nursing officers (95% CI: 4.1\u0026ndash;13.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while facility type differences were attenuated in adjusted models (Table A5 Appendix 1).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.7 Factors associated with facility readiness for hypertension management\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe found strong evidence that geographic location, facility type and supervisory support were associated with hypertension service readiness (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). A strong urban-rural disparity was observed. While 66.7% (2/3) of urban facilities were classified as ready, only 3.2% (1/31) of rural facilities met the readiness threshold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). There was strong evidence that facilities that had received a supervisory visit in the past three months were more likely to be classified as ready, at 66.7% (2/3) compared to 3.2% (1/31) of those without a visit (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFacility characteristics and bivariate associations with hypertension service readiness (N\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNot ready (\u0026lt;\u0026thinsp;70% index) n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReady (\u0026ge;\u0026thinsp;70% index)\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value*\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDispensary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealth Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupervisory visit (past 3 months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e-values derived from Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.8 Robustness of readiness scores\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe overall hypertension service readiness score was calculated using three methodologies (Figure A2 in Appendix 1). Both the unweighted (primary) and item-weighted approaches yielded a score of 42.9%, indicating strong internal consistency within the measurement tool. The expert-weighted score was slightly lower at 41.6%, suggesting that facilities less frequently possessed items designated as high-priority by clinical and operational experts.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.9 Spatial accessibility\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe average travel time from any population location in each sub-county to the 34 public health facilities using scenario 4 (walking\u0026thinsp;+\u0026thinsp;motorcycle\u0026thinsp;+\u0026thinsp;vehicle, which was the most practical scenario) was 25 minutes in Kilifi North and 30 minutes in Kilifi South. Scenario 3, which combined walking and use of motorcycle-also a highly used travel scenario in the region - had an average travel time of 27 minutes in Kilifi North and 31 minutes in Kilifi South. In the two sub-counties, over 80 per cent of the total population resided within 30 minutes travel time of a public health facility for the most pragmatic scenario as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eScenario 1 (walking only) and scenario 2 (motorcycle only) were also analysed to give a broader perspective of the different transport options in the two sub-counties based on the population\u0026rsquo;s socio-economic class (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In scenario 1, the average travel time was 44 minutes in Kilifi North and 47 minutes in Kilifi South while in scenario 2, 15 minutes in Kilifi North and 16 minutes in Kilifi South.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a comprehensive assessment of hypertension service readiness and spatial accessibility of health facilities in two sub-counties in Kilifi County, Kenya, by integrating structural, human resource, and spatial dimensions. Our findings reveal substantial systemic gaps that threaten equitable access to quality hypertension care in this predominantly rural setting. The overall mean readiness index of 42.9% falls considerably below the 70% threshold defined in our study for effective service delivery. Importantly, whereas the overall mean readiness index reported in our study was lower than that reported in a national study in Kenya for cardiovascular diseases (CVDs), at 69%, it is very similar to the one reported in Nepal (38.1%) for CVDs (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e), in Bangladesh (45.1%) for CVDs (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) and in Ethiopia (29%) for hypertension (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). The difference in readiness index scores between our study and the national readiness study in Kenya likely stems from methodological differences in domains of readiness assessed and the sampling approach for health facilities.\u003c/p\u003e\u003cp\u003eThe strong gradient where no dispensaries met the readiness threshold compared to 25% of health centres and 67% of hospitals is noteworthy. Dispensaries, which constitute the majority (88%) of facilities and form the backbone of primary care delivery in rural Kilifi, demonstrated critical shortfalls in essential antihypertensive medicines (mean availability 12.7%) and staff training (14.8%) required for basic hypertension management. In part, these results can be attributed to national policies that restrict certain commodity and equipment stocking in dispensaries and health centres (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e). Nevertheless, in a bid to promote access to hypertension care and decongest secondary hospitals, the County Department of Health procures antihypertensive medicines for dispensaries, and facilities engage in adaptive practices such as borrowing medicines from one another to reduce stock-outs (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). While these coping strategies are important for maintaining continuity of care, as evidenced by our findings, they remain insufficient to ensure consistent availability of essential antihypertensive medicines, especially at lower-level facilities.\u003c/p\u003e\u003cp\u003eThese contextual realities notwithstanding, the marked disparities in readiness across facility tiers creates a fundamental misalignment with the principle of primary healthcare and forces a \u003cem\u003ede facto\u003c/em\u003e rationing of quality care by facility tier and geography. In other words, patients who depend on public primary healthcare do not consistently access the essential service delivery inputs required for the management of hypertension, a finding consistent with facility readiness assessments for NCDs across SSA (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e). The concentration of readiness at higher-level facilities exacerbates inequitable access to quality hypertension care and creates a fundamental mismatch with population health needs as most rural residents depend on lower-level facilities for routine chronic care. Moreover, this gradient in facility readiness for hypertension is potentially a powerful structural determinant of health-seeking behaviour, likely influenced by the well-documented phenomenon of bypassing, a finding documented in a recent study (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThree facility-level factors emerged as significant predictors of readiness: urban location, facility level, and recent supervision. The urban-rural disparity was pronounced, with 67% of urban facilities ready versus only 3% of rural facilities. It is important to note that geographic location has been documented as one of the critical factors explaining socioeconomic inequalities in the utilization of screening and treatment interventions for hypertension care in Kenya (\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e). The strong association with recent supervision suggests that regular supportive oversight may improve the level of facility readiness for hypertension. Previous studies on health facility readiness for NCDs in Tanzania, Ethiopia and Nepal have also found that urban location, existence of supportive supervision and facility tier as some of the determinants of facility readiness (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e The weakest readiness domains, medicines (21.4%) and training/guidelines (25%), represent critical service delivery bottlenecks. The limited availability of combination therapies (11.8%) documented in our study is particularly concerning as their limited availability alongside other essential antihypertensive classes risks weakening treatment adherence and blood pressure control. Although acceptable in reducing pill burden and improve medication adherence (\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e), challenges at multiple health systems levels continue to undermine the implementation of combination therapies in the Kenyan context (\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e). Inefficient procurement systems, including restrictive commodity order forms for lower-level facilities, and budgetary constraints have been reported to be among the main causes for the chronic stock out of antihypertensive medication in Kilifi county (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). Other critical implications of the chronic stock-out of antihypertensives on patients include catastrophic healthcare expenditure, defaulting from care or seeking alternative forms of treatment that may worsen patients’ health conditions (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHealth worker knowledge presented a paradoxical picture. While overall knowledge scores were high, critical gaps persisted in first-line treatment selection and medication safety. Only 70% correctly identified calcium channel blocker-based combination therapy as preferred first-line treatment, and 22% indicated they would inappropriately switch stable patients to diazepam and furosemide for isolated headaches, a concerning patient safety issue. This discordance between high overall scores and specific clinical knowledge gaps may reflect the challenge of translating theoretical knowledge into clinical practice, likely because of inadequate dissemination of treatment guidelines. We found that majority of FLHWs (71%) were not familiar with any cardiovascular treatment guidelines and only 14% reported familiarity with the 2024 cardiovascular treatment guidelines. These findings suggests the need for concerted efforts to disseminate the 2024 cardiovascular treatment guidelines and institute measure for routine training of FLWHs on hypertension management, a finding that has been reported elsewhere (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e), as has the knowledge gradient across cadres and facility types (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e). Higher-level cadres (medical officers, consultants) who achieved perfect scores work exclusively in hospitals, while nursing officers (who constitute majority of primary healthcare providers in Kilifi and predominantly staff dispensaries) had comparatively lower scores (88%) (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e). This distribution creates a dual burden: the providers with the greatest knowledge gaps work in facilities with the fewest resources. This confounded relationship suggests that both FLHW knowledge and facility-level enabling environments are needed for quality hypertension care. A recent study in Kilifi (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e) has shown patient-safety concerns around administration of antihypertensive medicines, particularly at lower-level facilities and from private pharmacies. This evidence reinforces the urgent needs to train FLHWs and improve regulatory oversight of private pharmacies to mitigate risks arising from inappropriate prescribing or medication handling.\u003c/p\u003e\u003cp\u003eSpatial accessibility analysis revealed that 81% of the population could reach a health facility within 30 minutes by a combined travel scenario and when considering a walking scenario, only 9% of the population resided in the marginalised areas, where facilities were least likely to be ready to offer hypertension care. The rural border areas of Kilifi South sub-county exhibited significant areas of marginalisation that could exacerbate existing health disparities despite having good access metrics across the study area. These findings align with studies from Malawi, which found that physical access barriers disproportionately affect rural populations seeking chronic care (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). It is worthwhile noting that previous studies conducted in Kilifi reported that transport costs incurred during routine clinic appointments were a major patient cost driver when seeking hypertension care (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, to minimise the transport cost burden on patients and enhance access to care, the county government of Kilifi should prioritise improving road infrastructure that could lower transport costs and prioritize investing in the capacity of the existing lower-level facilities.\u003c/p\u003e\u003cp\u003eHospitals generally had higher readiness index scores, in part, because they are better resourced as they generate their own revenues from user fees, and do not solely rely on county allocations (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). Nevertheless, this could lead to potential over-reliance on higher-level facilities for routine chronic care, a pattern observed in Tanzania and Uganda (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e), and contributes to health system inefficiency. This highlights the need to reorient higher-level facilities toward supporting primary care through mentorship, supply chain support, and clear referral protocols as envisioned in the establishment of primary care networks (\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003e4.1 Strengths and limitations\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe key strengths of this study include its rigorous adaptation of WHO SARA tool, with multiple weighting approaches to test robustness of facility readiness index, integration of high-resolution geospatial analysis, and simultaneous assessment of structural readiness and health worker knowledge. The mixed-methods approach provides a comprehensive health system assessment.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the study only focused on public facilities in two sub-counties, which limits the generatability of its findings to Kenya as a whole. Second, the cross-sectional design does not allow for drawing causal inference about factors associated with readiness. Finally, while we assessed spatial accessibility, we did not measure actual utilisation. Future research should consider a longitudinal design to assess readiness trends over time and expand coverage of health facilities to include the private sector and a wider geographic scope.\u003c/p\u003e\u003cp\u003e \u003cb\u003e4.2 Policy implications and recommendations\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo ensure equitable and accessible hypertension care, we recommend prioritised, multifaceted interventions. First, there is an urgent need to strengthen measures to ensure the availability of essential antihypertensive medicines at dispensary and health centre levels through, for example, increased commodity budgetary allocation and expanding the essential medicines list to allow dispensaries to stock essential commodities. Investing in the capacity of PHC facilities (i.e., through adequate staffing) to ensure they can stock essential antihypertensives. Second, there is a need for hypertension workforce capacity building tailored to nursing officers and clinical officers working in PHC facilities, complemented by targeted treatment guidelines dissemination, regular supportive supervision and mentorship from higher-level facilities. Third, it will be important to develop outreach strategies targeting underserved rural areas of Kilifi South, including transportation subsidies for patients. The Kilifi County Health Department and facility managers could use the readiness findings from this study as a baseline to track and improve readiness in these facilities. Finally, our findings suggest that integrated care redesign where higher-level facilities (hospitals) support primary healthcare facilities through clear referral protocols, mentorship programs, as envisioned in the primary care networks, could enhance access to hypertension care. Priorities should include strengthening medicine supply chains, institutionalising guideline availability and supportive supervision at primary care level.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e Primary healthcare facilities in Kilifi North and South sub-counties of Kilifi County exhibit substantial readiness gaps for hypertension management, characterized by critical shortages of essential medicines, inadequate dissemination of treatment guideline, and pronounced urban-rural and facility-tier disparities. While health worker knowledge is generally adequate, specific clinical knowledge gaps persist, particularly among primary healthcare providers. Spatial access barriers exist in Kilifi South, especially for rural populations dependent on walking. Addressing these interconnected challenges requires coordinated interventions targeting supply chains, workforce capacity, geographic equity, and supportive supervision. As Kenya advances toward universal health coverage, prioritising hypertension care readiness at the primary healthcare level will be essential for equitable, effective prevention and control of this condition.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACEIs \u0026ndash; Angiotensin converting enzyme Inhibitors, ARBs \u0026ndash; Angiotensin Receptor Blockers, BBs \u0026ndash; Beta Blockers, BP \u0026ndash; Blood Pressure, CCBs \u0026ndash; Calcium channel Blockers, CI \u0026ndash; Confidence Interval, CT(s) \u0026ndash; Combination therapy / Combination therapies, CVD(s) \u0026ndash; Cardiovascular disease(s), DEM \u0026ndash; Digital elevation model, ECG \u0026ndash; Electrocardiogram, FLHW(s) \u0026ndash; Front-line health worker(s), FY \u0026ndash; Financial Year, HSD \u0026ndash; Honestly Significant Difference (Tukey\u0026rsquo;s), IHCoR-Africa \u0026ndash; Improving Hypertension Control in Rural sub-Saharan Africa, IQR \u0026ndash; Interquartile Range, KEMRI \u0026ndash; Kenya Medical Research Institute (from institutional names), KHDSS \u0026ndash; Kilifi Health and Demographic Surveillance System, LMICs \u0026ndash; Low- and middle-income countries, LSHTM \u0026ndash; London School of Hygiene and Tropical Medicine, NACOSTI \u0026ndash; National Commission for Science, Technology and Innovation, NCD(s) \u0026ndash; Non-communicable disease(s), NHIF \u0026ndash; National Health Insurance Fund, NIHR \u0026ndash; National Institute for Health and Care Research, PEN \u0026ndash; Package of essential noncommunicable (disease interventions), PHC \u0026ndash; Primary healthcare, REDCap \u0026ndash; Research Electronic Data Capture (software), SARA \u0026ndash; Service Availability and Readiness Assessment, SDG \u0026ndash; Sustainable Development Goal, SSA \u0026ndash; Sub-Saharan Africa, TTD(s) \u0026ndash; Thiazide \u0026amp; Thiazide-like Diuretics, UHC \u0026ndash; Universal health coverage, WHO \u0026ndash; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations:\u0026nbsp;\u003c/strong\u003eAll study procedures were carried out in accordance with the Declaration of Helsinki\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eEthical approval was obtained from the KEMRI Scientific and Ethics Review Unit (Ref. 4631), LSHTM Ethics Committee (Ref. 28313), and NACOSTI (Ref. NACOSTI/P/23/24745). Kilifi County Health Department gave administrative permission. In addition, permission was sought from sub-county health management teams and from facility managers of all participating facilities prior to data collection. During the informed consent process, all potential participants were clearly informed that participation in the study was entirely voluntary. They were assured that they had the right to decline to answer any question or withdraw from the study at any time without any penalty or effect on their access to services, and that no harm would come to them or their families as a result of their participation decisions. All members of the research team completed accredited human subjects’ protection training prior to engaging in any study‑related procedures. Data collection commenced only after participants had provided written informed consent. Any sensitive information obtained during the study was de‑identified, and all study data were stored securely with password protection and access limited strictly to authorized research personnel for legitimate research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eThis manuscript was written with the permission of Director KEMRI CGMRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data underlying this article cannot be shared publicly due to confidentiality of some of the data. Data are, however, available from the authors upon reasonable request and with permission of KEMRI Wellcome Trust Data Governance Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This project is funded by the National Institute for Health and Care Research (NIHR) under its ‘Global Health Research Units and Groups Programme’ (Grant Reference Number NIHR134544). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003eConception or design of the work: R.O., J.A., E.B., B.T., E.N., P.P., and A.E. Data collection: R.O., E.M., R.L., A.B., C.M., E.S., C.K., and J.B. Data analysis and interpretation: R.O., N.K., E.M., N.A., A.E., P.P., E.B., E.N., and B.T. Drafting the article: R.O. Critical revision of the article: R.O., E.M., N.A., B.T., E.N., P.P., and A.E. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe thank the Kilifi County Health Department, facility staff, and research assistants for their cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Noncommunicable diseases progress monitor 2025. World Health Organization; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatkins DA, Danforth K, Ahmed S, Chisholm D, Cieza A, Iunes R, et al. Financing policies to sustain improved prevention, control, and management of non-communicable diseases and mental health conditions. 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PLOS global public health. 2025;5(3):e0003012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy A, Mbuthia D, Willis R, Tsofa B, Gichagua M, Mugo P, et al. Improving implementation of NCD care in low-and middle-income countries: the case of fixed dose combinations for hypertension in Kenya. Health Syst Reform. 2025;11(1):2448862.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatende D, Mutungi G, Baisley K, Biraro S, Ikoona E, Peck R, et al. Readiness of Ugandan health services for the management of outpatients with chronic diseases. Tropical Med Int Health. 2015;20(10):1385\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health. Kenya Primary Health Care Strategic Framework 2019\u0026ndash;2024. Kenya: Ministry of Health Available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ipfkenya.or.ke/wp-content/uploads/2020/07/Kenya-Primary-Healthcare-Strategic\u003c/span\u003e\u003cspan address=\"https://ipfkenya.or.ke/wp-content/uploads/2020/07/Kenya-Primary-Healthcare-Strategic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed 11 Jan 2026). 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-communicable diseases, Hypertension, Health facility readiness, Geospatial access, Universal Health Coverage","lastPublishedDoi":"10.21203/rs.3.rs-9003184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-communicable diseases (NCDs) currently cause 43\u0026nbsp;million deaths globally. Health systems in low-and middle-income countries, including Kenya, are struggling to respond to the growing NCD burden and respond to population health needs in an equitable and accessible manner. The aim of this study was to examine health facility readiness, health workers\u0026rsquo; knowledge, and spatial accessibility to hypertension management in Kilifi County, Kenya.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional survey of 34 facilities in Kilifi North and Kilifi South sub-counties. Readiness was assessed across five domains: basic infrastructure, equipment, diagnostics, medicines, and training/guidelines. Facilities with readiness index\u0026thinsp;\u0026ge;\u0026thinsp;70% for all the assessed domains were classified as ready to provide hypertension services. Fisher\u0026rsquo;s exact test was used to examine factors associated with facility readiness. Health worker knowledge in managing hypertension was evaluated using self-administered questionnaires. Spatial accessibility to geocoded health facilities was modelled in AccessMod using high spatial resolution raster datasets of the elevation, land cover, and population combined with vector datasets of a detailed road network and travel barriers. Four travel scenarios were adopted: walking only, motorcycle only, walking followed by motorcycle, and walking followed by motorcycle and then vehicle.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall mean hypertension service readiness index was 42.9% (95% CI: 37.1\u0026ndash;48.8). We found strong evidence that readiness varied by facility type, facility location and supervisory practices (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The weakest readiness domains were in the availability of anti-hypertensive medicines (21.4%; 95% CI: 12.2\u0026ndash;30.6) and staff training/guidelines (25%; 95% CI: 11.6\u0026ndash;38.1). Whereas the mean overall knowledge score was 11.9 out of 13 (91.4%; 95% CI: 89.3\u0026ndash;93.4), only 14% of health workers were familiar with the latest cardiovascular treatment guidelines. Spatial accessibility analysis using the most pragmatic travel scenario for the Coast region indicated that over 80% of the population in the two sub-counties (~\u0026thinsp;530,000 people) resided within 30 minutes travel time to a health facility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHealth facilities were geographically accessible, but they lacked the readiness to deliver hypertension care. To improve health facility readiness, measures to ensure the availability of anti-hypertensive medicines, healthcare worker training and dissemination of treatment guidelines should be prioritised.\u003c/p\u003e","manuscriptTitle":"Assessing Facility Readiness and Spatial Accessibility for the Management of Hypertension in Kilifi County, Kenya: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 13:22:28","doi":"10.21203/rs.3.rs-9003184/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-25T06:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133069707125490514238262400489116619898","date":"2026-03-12T11:47:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-12T09:49:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T09:28:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T09:30:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T09:21:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-01T17:40:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43847794-79cc-4cae-be79-a896dcc1b5f5","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-13T13:22:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 13:22:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003184","identity":"rs-9003184","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}