Community Health Worker-led versus facility-based hypertension care for people with uncontrolled blood pressure in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort study

Community Health Worker-led versus facility-based hypertension care for people with uncontrolled blood pressure in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort 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 Article Community Health Worker-led versus facility-based hypertension care for people with uncontrolled blood pressure in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort study Niklaus Labhardt, Felix Gerber, Giuliana Sanchez-Samaniego, Thesar Tahirsylaj, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7022331/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Abstract Access to hypertension care in low- and middle-income countries remains insufficient. Community Health Workers (CHWs) may improve access and outcomes of hypertension care, particularly in remote areas. However, the effectiveness of community-based hypertension care that includes independent drug prescription by lay CHWs is unknown. We conducted a 1:1 cluster-randomized trial nested within the Community Based chronic Care Lesotho (ComBaCaL) cohort study (NCT05596773) in rural Lesotho. Following community-based hypertension screening, 547 non-pregnant adults with blood pressure (BP) ≥140/90 mmHg were enrolled (75% female, median age 64 years, mean BP 149/95 mmHg). In intervention clusters, CHWs offered hypertension care, including independent prescription and titration of first-line antihypertensive medication (amlodipine/hydrochlorothiazide fixed-dose combination), guided by a mobile clinical decision support system (CDSS). In control clusters, participants were referred to the nearest health facility for standard of care. In the primary modified intention-to-treat analysis including 543 participants (four exclusions due to pregnancy), BP control rates after 12 months were 156/271 (58%) and 130/272 (48%) in the intervention and control arm; adjusted odds ratio (aOR) 1.52 (95%CI 1.01-2.29), p=0.046. The mean systolic BP was 131.1 mmHg (standard deviation 16.0) and 134.7 mmHg (17.6) in the intervention and control arm; adjusted mean difference -4.2 mmHg (95%CI -7.6; -0.8). In the intervention arm, 220/271 (81.2%) participants were engaged in care compared to 196/272 (72.1%) in the control arm; aOR 1.65 (95%CI 1.09; 2.51). In a predefined complete case analysis only including participants with available BP measurements, BP control rates at 12 months were 156/227 (69%) and 130/238 (55%) in the intervention and control arm; aOR 1.95 (1.23; 3.10). No relevant differences in safety outcomes were observed. CHW-led hypertension care, including independent, CDSS-guided drug prescription is safe, improves engagement in care and BP control compared to facility-based care. Health policies should consider implementation of this model of care to address prevailing hypertension care gaps. clinicaltrials.gov registration: NCT05684055 Health sciences/Diseases/Cardiovascular diseases/Hypertension Health sciences/Health care/Health services Health sciences/Medical research/Outcomes research Health sciences/Health care/Health policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Main Globally, arterial hypertension is the most important risk factor for premature mortality, accounting for 10.8 million or almost 20% of all deaths in 2019 1 . Low- and middle-income countries (LMICs) bear a disproportionate and growing share of the burden, while many high-income countries have managed to reduce the burden of hypertension substantially over the past decades 2 . Besides demographic and lifestyle changes, insufficient access to preventive, diagnostic and therapeutic services are driving the excessively high morbidity and mortality associated with hypertension in LMICs 3 . In 2019, 78% of the 1.3 billion people with hypertension were living in LMICs with less than 20% of them reaching treatment targets 4 . In LMICs, people living in rural areas and having a lower socioeconomic status face pronounced barriers to accessing hypertension care 5 . Through improved access to antihypertensive medication alone, about 40 million premature deaths could be prevented over the next 25 years 6 . Given the global shortage of healthcare professionals, task-shifting to Community Health Workers (CHWs) has been identified as a promising approach to meet the increasing care needs for hypertension and other chronic conditions in LMICs 7,8 . CHWs bring services closer to the community, reduce access barriers such as transport costs, travel time and low awareness, thereby promoting more equitable and less stigmatized access to care 9 . CHW-led hypertension care interventions have been assessed in different settings showing an overall modest effectiveness on blood pressure (BP) control 10–12 . Interventions without pharmacological treatment component were of no or limited effectiveness 10,11,13 , whereas multilevel interventions combining CHW-led screening, monitoring and drug delivery with prescription by physicians have proven highly effective in several large-scale trials 12,14–16 . However, reliance on physician prescriptions may limit the scalability and workload redistribution of such interventions. Only one trial conducted in rural China evaluated an intervention with independent drug prescription, titration, and monitoring by CHWs 17,18 . The intervention significantly improved BP control rates and reduced cardiovascular events and mortality compared to enhanced usual care 17,18 . However, CHWs in China (“village doctors”) have a substantially higher level of medical education than CHWs in most other countries and it remains unclear whether the findings can be replicated in systems outside China with lower trained CHWs 19 . Digital clinical decision support systems (CDSS) and simplified treatment protocols using fixed-dose combination pills may enable CHWs with limited training to provide hypertension care safely and effectively 20,21 . This cluster-randomized trial aimed to assess the safety and effectiveness of an intervention including independent prescription, monitoring and titration of antihypertensive medication by CDSS-assisted lay CHWs among people with uncontrolled hypertension in rural Lesotho. It addressed several of the top research priorities for improving hypertension care identified by the World Health Organization (WHO), including the development and evaluation of systems for hypertension care delivery closer to home, of CHW-led medication prescription and titration, of a digital approach to improving retention in care, and of task-sharing that addresses barriers in relevant settings 22 . The intervention was developed in collaboration with local community members and the Lesotho Ministry of Health based on a local non-communicable diseases prevalence survey and burden assessment 23,24 and a scoping literature review 25 . The trial was conducted within the Community-Based Chronic disease care Lesotho (ComBaCaL) cohort study (NCT05596773) 26 . This cohort comprises the population of 103 villages in rural Lesotho and serves as platform for the investigation of chronic diseases and their management. It is embedded within the government-led community health program and managed by local CHWs. Results Baseline characteristics Between September 08, 2023, and January 31, 2024, CHWs screened 6’641 out of 8’835 eligible adult ComBaCaL cohort participants for hypertension during home-visits across 103 rural villages. They identified 1’262 cohort participants with hypertension, out of which 547 (274 control, intervention) from 97 villages (47 intervention, 50 control) had uncontrolled BP levels (≥140/90mmHg) and were not pregnant (figure 1). The trial participants had a mean age of 62.3 years and a mean BP of 149.0 (standard deviation (SD) 16.8) over 95.0 (SD 10.1) mmHg; 75.0% were female; 44.6% reported use of antihypertensive medication at enrolment; 35.1% were smokers, 8.4% had diabetes; 15.7% reported living with HIV; 1.3% had heart failure and 4.9% had a history of stroke or myocardial infarction. No substantial imbalance across the arms was observed (table 1 and extended data table 1). Table 1 Baseline characteristics of participants by study arm. Cluster-level characteristics Control Intervention Total Villages, n 50 47 97 Participants per village, median (IQR) 6.0 (3.0, 7.0) 5.0 (3.0, 8.5) 5.0 (3.0, 8.0) Hard access to health facility 1 , n (%) 27 (54.0) 26 (55.3) 53 (54.6) District Butha Buthe (versus Mokhotlong), n (%) 27 (54.0) 24 (51.1) 51 (52.6) Individual-level characteristics Participants, n 274 273 547 Demographics and anthropometrics Female, n (%) 200 (73.0) 210 (76.9) 410 (75.0) Age (years), mean (SD) 61.6 (17.4) 63.0 (16.2) 62.3 (16.8) Body mass index (kg/m 2 ), mean (SD) 27.0 (6.7) 28.5 (6.6) 27.8 (6.7) Abdominal circumference (cm), mean (SD) 90.8 (14.3) 90.6 (15.5) 90.7 (14.9) Education at secondary school or higher, n (%) 57 (20.8) 42 (15.4) 99 (18.1) Working for pay or self-employed, n (%) 99 (36.1%) 117 (42.9%) 216 (39.5%) Married or in a stable relationship, n (%) 143 (52.2) 143 (52.4) 286 (52.3) Blood pressure Systolic blood pressure (mmHg), mean (SD) 147.5 (17.0) 150.5 (16.5) 149.0 (16.8) Diastolic blood pressure (mmHg), mean (SD) 94.5 (9.3) 95.4 (10.9) 95.0 (10.1) Engaged in hypertension care, n (%) 123 (44.9) 121 (44.3) 244 (44.6) CVD risk, lifestyle and comorbidities Cardiovascular disease risk ≥10% 2 , n (%) 131 (50.4) 150 (61.2) 281 (55.6) Current smoking, n (%) 86 (31.4) 106 (38.8) 192 (35.1) Alcohol consumption ≥1 day/week, n (%) 63 (23.1) 62 (22.8) 125 (22.9) Moderate or high physical activity 3 , n (%) 217 (79.2) 231 (85.6) 448 (82.4) In care for a chronic condition 4 , n (%) 52 (19.0) 68 (24.9) 120 (21.9) Heart failure, n (%) 4 (1.5) 3 (1.1) 7 (1.3) History of stroke or myocardial infarction (%) 14 (5.1) 13 (4.8) 27 (4.9) Diabetes, n (%) 23 (8.4) 23 (8.4) 46 (8.4) HIV, n (%) 39 (14.2) 47 (17.2) 86 (15.7) Statin use among eligible participants 5 , n (%) 0 (0.0) 9 (5.7) 9 (3.1) Total cholesterol (mg/dl), mean (SD) 153.2 (44.6) 154.9 (40.6)) 154.0 (41.6) LDL cholesterol (mg/dl), mean (SD) 84.6 (36.8) 85.1 (37.2) 84.8 (37.0) Portion of vegetables and fruits/day, mean (SD) 0.6 (0.6) 0.6 (0.5) 0.6 (0.5) Always or often adding salt to food while eating, n (%) 45 (16.4) 44 (16.2) 89 (16.3) Sweet food consumption ≥3 days/week, n (%) 20 (7.3) 37 (13.6) 57 (10.4) Sweet beverage consumption ≥3 days/week, n (%) 20 (7.3) 19 (7.0) 39 (7.1) Fried food consumption ≥3 days/week, n (%) 40 (14.6) 60 (22.0) 100 (18.3) Table 1 legend 1: Needing to cross a mountain or river or travel >10 km to the nearest health facility 2: 10-year risk for a cardiovascular event, using the WHO laboratory-based cardiovascular risk calculator 27 3: Self-reported physical activity using the International Physical Activity Questionnaire Short Form (IPAQ-S) 28 4: HIV and/or diabetes 5: Participants with history of stroke, myocardial infarction, diabetes, or chronic kidney disease or aged ≥50 years and having a BMI of ≥30kg/m 2 or aged ≥50 years and currently smoking were eligible for statin. Number of participants eligible is 289 (129 in control and 159 in intervention) Abbreviations: SD: standard deviation; CVD: cardiovascular disease; LDL: low-density lipoprotein. Missing data: BMI: 3 control, 3 intervention; abdominal circumference: 2, 8; education: 0, 1; use of antihypertensive medication: 0, 1; WHO cardiovascular risk: 14, 28; alcohol consumption: 1, 1; physical activity: 0, 3; total cholesterol: 14, 28; LDL cholesterol: 25, 57; adding salt to food: 0, 1. Primary outcome and blood pressure-related secondary outcomes In the primary modified intention-to-treat analysis including 543 participants (four exclusions due to pregnancy during follow-up), BP control rates after twelve months were 156/271 (58%) and 130/272 (48%) in the intervention and control arm (table 2). The adjusted odds ratio (aOR) of the intervention on controlled BP was aOR 1.52 (95% confidence interval (CI): 1.01; 2.29; p-value = 0.046). This corresponds to an average intervention effect of 12.5% (95% CI: 2.2; 22.5) increase in BP control rate. In a predefined sensitivity analysis only including participants with available BP measurements at 12 months (complete case), control rates were 156/227 (69%) and 130/238 (55%) in the intervention and control arm; aOR 1.95 (1.23; 3.10). At six months, the BP control rates in the modified intention-to-treat analysis were 144/271 (53%) in the intervention arm and 120/273 (44%) in the control arm; aOR 1.37 (0.91; 2.05). The mean systolic (SBP) and diastolic blood pressure (DBP) after twelve months were -4.2 mmHg (-7.6; -0.8) and -2.4 mmHg (-4.6; -0.2) lower in the intervention compared to the control arm. Table 2 Intervention effects on primary endpoint and blood pressure-related secondary endpoints Control Intervention aOR or adjusted mean difference (95% CIs) 1 Primary endpoint analysis 2 N=272 N=271 BP <140/90 mmHg at 12 months, n (%), mITT 3 130 (47.8) 156 (57.6) 1.52 (1.01; 2.29) Sensitivity analysis 4 N= 238 N=227 BP <140/90 mmHg at 12 months, n (%), complete case 130 (54.6) 156 (68.7) 1.95 (1.23; 3.10) BP-related secondary endpoints at 12 months 2 N=272 N=271 SBP, mmHg, mean (SD) 134.7 (17.6) 131.1 (16.0) -4.2 ( -7.6; -0.8) DBP, mmHg, mean (SD) 86.2 (10.8) 84.1 (9.9) -2.4 ( -4.6; -0.2) BP-related secondary endpoints at six months 5 N=273 N=271 BP <140/90 mmHg, n (%), mITT 3 120 (44.0) 144 (53.1) 1.37 (0.91; 2.05) BP <140/90 mmHg, n (%), complete case 6 120 (48.6) 144 (61.0) 1.64 (1.08; 2.49) SBP, mmHg, mean (SD) 135.6 (17.4) 132.7 (15.6) -3.6 ( -6.5; -0.6) DBP, mmHg, mean (SD) 87.0 (11.3) 84.7 (9.5) -2.6 (-4.4; -0.8) Table 2 legend 1: Effects are derived from mixed effect regression models adjusted for clustering and covariates (sex, age, district, facility access and baseline values when appropriate) 2: Four participants excluded due to pregnancy between enrolment and twelve months 3: Participants with missing BP measurement were considered as having a BP ≥140/90mmHg 4: Only including participants with available BP measurement at twelve months: 238 control, 227 intervention 5: Three participants excluded due to pregnancy between enrolment and six months 6: Only including participants with available BP measurement at six months: 247 control, 236 intervention Abbreviations: BP: blood pressure; SD: standard deviation; SBP: systolic blood pressure; DBP: diastolic blood pressure Missing data: SBP and DBP at twelve months: 34 control, 44 intervention. SBP and DBP at six months: 26 control, 35 intervention. Compared to baseline, the mean SBP decreased by -19.2 mmHg (-21.8; -16.5) in the intervention and by -13.7 mmHg (-16.3; -11.2) in the control arm after twelve months. The mean DBP decreased by -11.4 mmHg (-12.9; -9.8) in the intervention and by -8.5 mmHg (-9.9; -7.2) in the control arm (figure 2). Interaction p-values for the pre-specified potential effect modifiers (sex, age, engagement in hypertension care at baseline, access to health facility, and engagement in HIV care) were larger than 0.1 and therefore no subgroup credibility assessments were performed (see extended data table 4 and extended data figure 1). Further secondary outcomes Figure 3 shows engagement in care and BP control rates at zero, six and 12 months by arm in the modified intention-to-treat and the complete case population. At six months, 220/271 (81.2%) participants in the intervention arm and 190/273 (69.6%) in the control arm were engaged in care; aOR 1.94 (1.27; 2.95) (table 3, figure 3). Among those not engaged in hypertension care at baseline, in the intervention arm 130/151 (86.1%) participants linked to care by six months compared to 44/150 (29.3%) in the control arm, aOR 16.65 (8.30; 33.41; table 3). Among the 220 engaged in care in the intervention arm, 186/220 (84.5%) were in care with their CHW and 34/220 (15.5%) at the health facility (two due to intolerance to amlodipine or hydrochlorothiazide, nine did not reach the treatment target with amlodipine and hydrochlorothiazide, 23 preferred facility-based care) (figure 4). At twelve months, 220/271 (81.2%) participants were engaged in care in the intervention arm versus 196/272 (72.1%) in the control arm; aOR 1.65 (1.09; 2.51) (table 3, figure 3). In the intervention arm 190/220 (86.4%) were in care with their CHW and 30/220 (13.6%) at the health facility (three due to intolerance to amlodipine or hydrochlorothiazide, twelve did not reach the treatment targets with amlodipine and hydrochlorothiazide, 15 preferred facility-based care) (figure 4). There were no significant differences between arms in the estimated 10-year cardiovascular event risk, body-weight, BMI, abdominal circumference, smoking, alcohol consumption, and dietary habits (table 3, extended data tables 2 and 3). The proportion of participants self-reportedly engaging in moderate or high or physical activity was higher in the intervention arm at twelve months, but not at six months. The proportion of participants using statin among those eligible was higher in the intervention at six months, but not at twelve months. Table 3 Further secondary outcomes Control Intervention aOR or adjusted mean difference (95% CIs) Secondary endpoints at 12 months 1 N=272 N=271 Engagement in care 2 , n (%), mITT 3 196 (72.1) 220 (81.2) 1.65 (1.09; 2.51) Engagement in care, n (%), complete case 4 196 (82.0) 220 (96.9) 12.51 (3.45; 45.34) Linkage to care 5 , n (%) 56 (37.3) 133 (88.1) 13.20 (6.75; 25.83) Already on treatment at baseline 122 120 Adherence to antihypertensive medication 6 , n (%) 135 (87.7) 148 (77.9) 0.49 (0.18; 1.29) Not on treatment at 12 months 115 80 WHO CVD risk ≥10%, n (%) 118 (49.8) 125 (55.1) 0.94 (0.45; 1.96) Total cholesterol, mg/dL, mean (SD) 162.2 (44.1) 163.0 (55.7) -0.9 (-11.4; 9.6) LDL, mg/dL, mean (SD) 90.0 (36.2) 93.2 (41.9) 5.8 (-2.2; 13.8) BMI, kg/m 2 , mean (SD) 26.7 (6.4) 28.0 (6.4) -0.4 (-0.8: 0.02) Abdominal circumference, cm, mean (SD) 90.3 (13.8) 91.2 (13.3) 0.2 (-1.4: 1.8) Moderate or high physical activity, n (%) 153 (64.0) 182 (80.5) 7.35 (1.48; 36.46) Current smoking, n (%) 89 (37.2) 105 (46.3) 1.60 (0.59; 4.31) Alcohol consumption ≥1 day/week, n (%) 44 (18.5) 40 (18.0) 1.15 (0.50; 2.63) Statin use among eligible participants 7 , n (%) 5 (5.0) 37 (27.6) 1.28 (0.8; 2.03) Not eligible for statin use 137 99 Portion of vegetables and fruits/day, mean (SD) 0.4 (0.3) 0.4 (0.2) 0.02 (-0.07; 0.10) Always or often adding salt to food, n (%) 44 (16.2) 43 (15.9) 0.76 (0.07;7.98) Sweet food consumption ≥3 days/week, n (%) 15 (6.3) 18 (8.0) 1.32 (0.43; 4.08) Sweet beverage consumption ≥3 days/week, n (%) 11 (4.6) 12 (5.3) 1.28 (0.52; 3.13) Fried food consumption ≥3 days/week, n (%) 23 (9.6) 23 (10.2) 0.99 (0.10; 10.12) Secondary endpoints at six months 8 N=273 N=271 Engagement in care, n (%), mITT 3 190 (69.6) 220 (81.2) 1.94 (1.27; 2.95) Engagement in care, n (%), complete case 4 190 (76.0) 220 (92.4) 4.97 (2.20; 11.25) Linkage to care 5 , n (%) 44 (29.3) 130 (86.1) 16.65 (8.30; 33.41) Already in care at baseline 123 120 Adherence to antihypertensive medication 6 , n (%) 124 (81.0) 139 (72.8) 0.56 (0.23; 1.38) Not on treatment 121 77 WHO CVD risk ≥10%, n (%) 117 (49.0) 115 (52.8) 0.79 (0.38; 1.68) Total cholesterol, mg/dL, mean (SD) 153.5 (38.9) 156.2 (45.9) -1.3 (-10.2; 7.6) LDL, mg/dL, mean (SD) 88.4 (35.9) 88.0 (41.2) -0.9 (-9.6; 7.9) BMI, kg/m 2 , mean (SD) 26.7 (6.5) 27.8 (6.5) -0.4 (-0.8; 0.04) Abdominal circumference, cm, mean (SD) 90.4 (14.7) 90.3 (13.6) 0.1 (-1.3; 1.6) Moderate or high physical activity, n (%) 176 (71.0) 182 (80.2) 1.64 (0.64; 4.17) Current smoking, n (%) 85 (34.1) 94 (39.7) 0.73 (0.27; 1.99) Alcohol consumption ≥1 day/week, n (%) 47 (19.0) 38 (16.9) 0.80 (0.47; 1.38) Statin use among eligible participants 7 , n (%) 4 (3.7) 59 (36.0) 2.96 (1.44; 6.05) Not eligible for statin use 143 105 Portion of vegetables and fruits/day, mean (SD) 0.5 (0.4) 0.5 (0.3) -0.01 (-0.11; 0.08) Always or often adding salt to food, n (%) 44 (16.1) 43 (15.9) 0.84 (0.48; 1.45) Sweet food consumption ≥3 days/week, n (%) 21 (8.5) 13 (5.7) 0.66 (0.21; 2.03) Sweet beverage consumption ≥3 days/week, n (%) 8 (3.2) 7 (3.1) 1.36 (0.07; 24.82) Fried food consumption ≥3 days/week, n (%) 27 (10.9) 26 (11.5) 0.96 (0.34; 2.77) Table 3 legend 1: Four participants excluded due to pregnancy between enrolment and 12 months 2: Reporting use of antihypertensive medication or reaching treatment target without medication 3: Participants with missing measurement were considered as not being engaged in care 4: Only including participants with available endpoint data 5: Proportion of participants who have initiated antihypertensive treatment since enrolment among those not taking treatment at baseline 6: Reporting full adherence to antihypertensive medication over the last four days 7: This was an exploratory endpoint. Statin eligible: participants with history of stroke, myocardial infarction, diabetes, or chronic kidney disease or aged ≥50 years and having a BMI of ≥30kg/m 2 or aged ≥50 years and currently smoking 8: Three participants excluded due to pregnancy between enrolment and 6 months Abbreviations: IQR: interquartile range; SD: standard deviation; BMI: body-mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; LDL: low-density lipoprotein Missing data at twelve months: engagement in care: 33 control, 44 intervention; adherence to antihypertensive medication: 3, 1; WHO CVD risk: 35, 46; total cholesterol: 35, 46; LDL: 58, 7; BMI: 52, 48; abdominal circumference: 48, 56; physical activity: 33, 45; smoking: 33, 44; alcohol consumption: 34, 49; statin use: 34, 44, vegetables and fruit consumption: 33, 45; adding salt to food: 0, 1; sweet food consumption: 33, 45; sweet beverage consumption: 33, 45; fried food consumption: 33, 45. Missing data at six months: engagement in care: 23 control, 33 intervention, total cholesterol: 32, 52; LDL: 62, 73; BMI: 34, 43; abdominal circumference: 32, 40; physical activity: 25, 44; smoking: 24, 34; alcohol consumption: 25, 46; statin use: 23, 34; vegetables and fruit consumption: 25, 44; adding salt to food: 0, 1; sweet food consumption: 25, 44; sweet beverage consumption: 25, 44; fried food consumption: 25, 44. Safety outcomes By twelve months, six participants in the control arm and three in the intervention arm had died. Two participants in the control arm have been hospitalized, while no non-fatal serious adverse events were reported in the intervention group. None of the events were related to study participation. One adverse event of special interest was observed the control group and nine in the intervention group. All adverse events of special interest in the intervention group were related to ankle swelling in association with intake of amlodipine and resolved after switching antihypertensive medication. One stroke was reported in the control arm. In a safety set analysis - considering only outcomes of participants with at least one hypertension care visit by the CHW in the intervention arm or at the health facility in the control arm - BP control rates at 12 months were 156/258 (60.5%) in the intervention arm and 94/206 (45.6%) in the control arm; aOR 1.93 (1.25; 2.97) with similar results observed at six months. Table 4 Safety set analysis and safety endpoints Control Intervention aOR (95% CIs) Safety set analysis at 12 months 1 N=206 N=258 BP <140/90 mmHg, n (%) 2 94 (45.6) 156 (60.5) 1.93 (1.25; 2.97) Safety set analysis at six months 1 N=215 N=255 BP <140/90 mmHg, n (%) 2 86 (40.0) 144 (56.5) 1.94 (1.27; 2.96) Safety endpoints at 12 months N=274 N=273 Deaths, n (%) 6 (2.2) 3 (1.1) Non-fatal serious adverse event 3 , n (%) 2 (0.7) 0 (0.0) Adverse events of special interest 4 , n (%) 1 (0.4) 9 (3.3) Stroke, n (%) 1 (0.4) 0 (0.0 Side effects of antihypertensive medication, n (%) 0 (0.0) 9 (3.3) Table 4 legend 1: Only considering outcomes of participants with at least one visit for hypertension care by the CHW in the intervention arm or at the health facility in the control arm 2: Participants with missing BP measurement were considered as having a BP ≥140/90mmHg 3: Serious adverse events defined as death, hospitalization, life-threatening event, event leading to persistent disability 4: Adverse events of special interest defined as adverse events consistent with hypertension complications, such as myocardial infarction, stroke, heart failure or chronic kidney disease as well as adverse events related to the intake of antihypertensive medication leading to the discontinuation of the medication concerned Discussion In this trial CDSS-assisted hypertension care including independent drug prescription, monitoring and titration by lay CHWs improved BP control rates, mean BP, linkage to care and engagement in care compared to facility-based hypertension care among non-pregnant adults with uncontrolled hypertension living in rural Lesotho. Using a fixed-dose combination pill containing amlodipine and hydrochlorothiazide at two different dosages, lay CHWs independently managed most participants at the community level with no associated safety concerns over 12 months. Given the increasing number of people requiring hypertension care and the limited professional healthcare workforce in LMICs, the need for care models that include task-shifting to non-physician healthcare workers is evident and recommended by the WHO 8 . Interventions combining CHW-led screening, counselling, drug delivery and monitoring services with physician prescription were found to significantly reduce BP levels and to be cost-effective in several large-scale trials 14–16 . The HOPE-4 study, conducted in Colombia and Malaysia, showed a significant cardiovascular risk and BP reduction through non-physician healthcare worker-led cardiovascular risk screening using a CDSS application, algorithm-guided treatment recommendations, lifestyle counselling and drug delivery after physician prescription 14 . The HOPE-4 intervention led to a large mean SBP reduction of -11.5 mmHg (-14.9; -8.0) 14 . However, the generalizability of the findings may be limited because the intervention was not fully embedded within the existing health system infrastructure with non-physician health workers being employed for the study and medication being provided free of charge only in the intervention arm 29 . The China Rural Hypertension Control Project (CRHCP) was the first study that demonstrated the feasibility and effectiveness of a multifaceted intervention that included independent drug prescription, monitoring and titration by CHWs on BP and mortality reduction 17,18 . Enrolling 33,995 individuals with uncontrolled BP in rural China, the intervention achieved an average SBP reduction of 14.5 mmHg (95% CI: -15.7 to -13.3). However, several factors may limit the generalizability of the findings of the CRHCP trial 19 . CHWs in China, called “village doctors”, are accredited after three years of medical education or twenty years of continuous practicing in village clinics and have routine prescription rights for essential medications, unlike lay CHWs in most LMICs 30 . Participants were provided with automated BP machines and trained on home self-measurements 31 , a strategy that may not be feasible and cost-effective in most LMIC settings 19 . Health system components of the intervention, such as provision of discounted or free medications and the use of hypertension control rates as a metric for resource allocation at health system level may be challenging to implement in other settings. The intervention in our study was designed to address some of the limitations identified in previous landmark studies. It is the first study to assess CHW-led hypertension care including independent drug prescription, monitoring and titration by CHWs outside China. It addresses several of the top research priorities for improving hypertension care identified by the WHO and responds to calls for contextualized clinical and health system hypertension research in Africa 22,32 . The intervention was integrated within the routine government CHW system. Participating CHWs were lay people elected by the village population according to the national Village Health Program Policy 33 . They reported to the supervising health facility during routine monthly meetings, where they also received medication supply through the routine supply chain system. The study team only assisted in remote monitoring through the CDSS application to ensure participant safety and completeness of data. The CDSS application that guided CHWs in service delivery and data collection through algorithmic guidance including algorithmic task scheduling was based on the widely available open-source, offline first, interoperable Community Health Toolkit software platform 34 . Screening, diagnosis and treatment algorithms applied by CHWs followed national guidelines with no formal intervention at the facility level. The intervention may thus be easily adoptable and scalable in different settings, especially where a CHW system is already established. Despite its relative simplicity, the intervention did not only outperform professional facility-based care but also led to an even greater SBP reduction than observed in previous landmark studies (-19.2 mmHg (-21.8; -16.5)). Our intervention contained a cardiovascular risk assessment along with CHW-led statin prescriptions. Unlike the HOPE-4 study, we did not observe an effect on the estimated 10-year cardiovascular risk. This discrepancy may be due to the more stringent eligibility criteria for statin prescriptions (59% of participants eligible compared to 93% in HOPE-4), the use of different cardiovascular risk assessment tools (WHO risk calculator versus Framingham risk score), and the insufficient uptake of statins in our study to which difficulties with supply and the CDSS application may have contributed. Our findings, alongside those from the HOPE-4 study, demonstrate the feasibility of algorithm-guided cardiovascular risk assessments conducted by CHWs. The use of fixed-dose combinations that include statins and antihypertensive drugs might be an enabling factor to increase statin uptake and adherence in future CHW-led interventions 35 . Like previous studies, we did not observe relevant effects on behavioral cardiovascular risk factors and associated outcomes, such as BMI or abdominal circumference 16,36–38 . Further research is required on how the effectiveness of CHW-led lifestyle counselling in hypertension management could be improved 39 . This study has several limitations. First, it only included participants with uncontrolled BP at baseline. To assess the effectiveness of the intervention among all people living with hypertension, another trial within the ComBaCaL cohort was conducted that enrolled participants with controlled BP at baseline 40 . Second, while the CHWs in our study were part of the government CHW system, they received an incentive of roughly 20$ per month in addition to the regular government stipend of about 40$ to compensate the study-related workload. The additional incentive was the same in the control and intervention arm. Third, the CHWs who delivered the intervention also assessed all endpoints. Desirability and recall bias may affect the self-reported behavioral outcomes. The CHWs were trained and guided by the CDSS application for correct BP measurements with the application automatically calculating the average of the last two out of three entered measurements as outcome. We thus deem the risk of measurement bias on the primary outcome as small. Fourth, we used a pragmatic titration algorithm that only included a combination of amlodipine and hydrochlorothiazide at two dosages and the same treatment target of 140/90 mmHg for all participants. Amlodipine and hydrochlorothiazide are the only drugs available as fixed-dose combination pill at an affordable price. They have a favorable effectiveness and safety profile, especially in absence of electrolyte monitoring 32,41 . However, this pragmatic approach led to suboptimal treatment for participants with comorbidities, such as diabetes or chronic kidney disease, that would benefit from a renin-angiotensin-aldosterone-system blocker and younger participants for whom more stringent treatment targets may be recommended. Based on our experience, increasing the treatment complexity for CHW-led, CDSS-supported management may be considered in future projects. Fifth, this study assessed the effectiveness of a CHW-led hypertension and cardiovascular risk management while there is a consensus that in most settings, CHWs should provide a comprehensive, integrated service package rather than single-disease focused interventions. However, this trial was implemented in parallel with a similar trial within the ComBaCaL cohort assessing the effectiveness of CHW-led diabetes management 42 . Further, the CHWs conducted routine government tasks related to health promotion and linkage services that were not formally monitored within the study. Thus, the intervention was partially integrated with other services while a formal assessment of the integrated care delivery is lacking. Further research is needed to assess both, the (cost)effectiveness of targeted interventions for other conditions and their integration to develop an evidence-based CHW-led essential service package. Lay CHWs guided by an open-source CDSS application can safely deliver hypertension care including independent first-line drug prescription, monitoring and titration, thereby improving engagement in care and BP control in a remote rural setting. Health policies should consider the broader adoption to address prevailing hypertension care gaps. Online methods Study design and setting We conducted a 1:1 cluster-randomized, open-label, superiority trial nested within the ComBaCaL cohort study following the Trials within Cohorts (TwiCs) design 43 . The ComBaCaL cohort study is conducted in 103 randomly selected villages in the two rural districts, Butha-Buthe and Mokhothlong, in Lesotho, a small, landlocked country surrounded by South Africa. In each ComBaCaL village, a CHW was elected by the community according to the procedures outlined in the Lesotho Ministry of Health Village Health Program policy 33 . CHWs play an important role in the Lesotho healthcare system providing health promotion services and linking the community to facility-based care, mainly for maternal and child health and HIV 33,44,45 . Lesotho is a typical example of an African LMIC where a developing health system is facing the double burden of still highly prevalent infectious diseases (HIV/AIDS and tuberculosis) in combination with a rapidly growing non-communicable disease epidemic 24,46,47 . Screening and enrolment Participants were recruited among the ComBaCaL cohort population, which includes all consenting inhabitants of the 103 randomly selected rural villages. ComBaCaL cohort participants aged 18 years or older were screened for hypertension by their CHW during home visits according to the diagnostic algorithm of the Lesotho Standard Treatment Guidelines 48 (supplementary information figure 1). All non-pregnant adult ComBaCaL cohort participants with hypertension, defined as reporting intake of antihypertensive medication or being newly diagnosed during screening with uncontrolled BP levels (≥140/90) were eligible. BP was measured using automated devices (Omron M3 Comfort [HEM7131-E]). Measurements were taken after selecting the appropriate cuff size, with participants seated comfortably—feet flat on the floor, arm supported, and remaining still and silent for five minutes. During the first visit, BP was measured in both arms to determine the reference arm for subsequent assessments. The arm with higher systolic BP was identified as reference arm and used for all subsequent BP measurements. The BP value was calculated as the mean of the last two out of three consecutive measurements at intervals of one minute. For the diagnosis of hypertension, two elevated measurements in the range of 140-179/90-109 mmHg on two different days or two measurements of 180/110 mmHg or higher on the same day, at least 30 minutes apart, were required. Randomization and blinding Half of the ComBaCaL cohort villages were randomly allocated to the intervention group by a statistician not involved in the study before the start of the hypertension screening. The randomization was stratified by district (Butha-Buthe versus Mokhothlong) and access to health facilities (easy versus difficult access, defined as needing to cross a mountain or river or travel >10 km to the nearest health facility). Participants were blinded to the allocation, meaning that participants in the control villages were not aware of the intervention being implemented in other villages. CHWs who enrolled participants, provided the intervention, and collected endpoint data were not blinded. Outcomes The primary endpoint was BP control rate (proportion of participants with BP <140/90mmHg) twelve months after enrolment. Secondary endpoints were the BP control rate after six months, mean SBP, mean DBP, engagement in care (proportion of participants reporting intake of antihypertensive medication or reaching treatment targets without intake of medication), linkage to care (proportion of participants not taking treatment at enrolment who have initiated antihypertensive treatment during follow-up), self-reported adherence to antihypertensive treatment, 10-year cardiovascular risk using the WHO cardiovascular disease risk prediction tool 27 , BMI, abdominal circumference, blood lipid status, physical activity using the International Physical Activity Questionnaire Short Form (IPAQ-SF) 28 , alcohol consumption, tobacco use, and dietary habits using a shortened unquantified food frequency questionnaire adapted from an assessment tool for obesity used in South Africa 49 , fruit, vegetable and salt consumption using the WHO steps instrument 50 six and twelve months after enrolment (see footnotes of extended data tables 1, 2, and 3 for details). Safety outcomes were the occurrence of Serious Adverse Events (SAEs) and Adverse Events of Special Interest (AESIs) within six and twelve months after enrolment. AESIs were defined as adverse events consistent with hypertension complications, such as myocardial infarction, stroke, heart failure or chronic kidney disease as well as adverse events related to the intake of antihypertensive medication leading to the discontinuation of the medication concerned. Exploratory endpoints included the use of statins among people with elevated cardiovascular risk, further exploratory endpoints will be reported separately. In addition to the pre-specified endpoints, the change in mean SBP and DBP compared to baseline is reported to allow for comparison with other relevant trials in the field. For all endpoints measured after six months, a window of 150 to 240 days, and for twelve months’ endpoints, a window of 300 to 420 days after enrolment applied. Procedures In each village, there was one CHW, equipped with a password-protected tablet loaded with a tailored CDSS application. The CDSS application provided algorithmic guidance for all study procedures and served as a data collection tool. The application was configured by the study team based on the open source Community Health Toolkit Core Framework (versions 3.12, 3.14, 4.6 and 4.13), a widely-used, offline-first, open source software toolkit designed for community health systems 34 . Data were synchronized regularly to a secure server hosted at the University Hospital Basel, Switzerland. Data were monitored locally by the CHW supervisors and centrally by the data management team of the University Hospital Basel. CHWs were elected by the village population in a participatory process according to the Lesotho Ministry of Health Village Health Program policy 33 . All CHWs received a one-week training on data collection, screening, and diagnosis of hypertension. CHWs in the intervention villages received an additional two-days training on the intervention services. In both control and intervention villages, CHWs received the regular government stipend of roughly 40$ per month and an additional 20$ to compensate the work related to study participation. In control villages, CHWs offered lifestyle counselling and referred participants to their closest health facility for further care after diagnosis and enrolment. At six months, CHWs conducted a check-up with lifestyle counselling and another referral in case the participant had not linked to care yet. In intervention villages, CHWs offered a community-based hypertension care package that included lifestyle counselling, prescription of antihypertensive fixed-dose combinations containing amlodipine and hydrochlorothiazide at low-dose (5mg/12.5mg) or high-dose (10mg/25g), lipid-lowering (atorvastatin 10mg) and antiplatelet (acetyl salicylic acid 100mg) treatment for eligible participants (supplementary information figure 2). Screening, diagnosis, as well as treatment initiation and monitoring followed the recommendations of the current Lesotho Standard Treatment Guidelines 48 . Insufficient BP control under high-dose antihypertensive fixed-dose combination, potential contraindications, side effects or the presence of clinical alarm signs or symptoms, triggered the CDSS application to recommend referral to the health facility. Participants were free to accept or refuse the services offered by the CHW at any time. Participants not eligible for CHW-led care or refusing it were referred to their closest health facility for further management with bi-monthly follow-ups by the CHW at the community level. All data were collected by CHWs during home visits. During follow-up visits, CHWs inquired about the occurrence of possible adverse events. In addition, CHWs solicited adverse events through reporting by participants, friends or relatives, screening of participants’ personal health booklet and reporting by routine health facility staff any time during the follow-up period. Adverse events reported by the CHWs were followed-up by supervising study staff to collect further clinical information. The pseudonymized reports were submitted to the study physician who classified the reports as SAE, AESI or neither of the two. Sample size and statistical analysis The sample size was calculated assuming an individual randomization inflated by a design effect that accounts for variation at cluster level, according to Rotondi and Donner 51 . Based on preliminary results from a non-communicable disease prevalence survey in Lesotho 32 , we expected an adult prevalence of hypertension of 18% with 55% having BP levels ≥140/90mmHg 24 . Considering an average village size of 100 adult inhabitants, the average cluster size of eligible participants was estimated at eight. Assuming that 75% would accept the CHW care intervention, a probability of BP control of 60% among participants accepting the intervention and of 30% among those refusing it, we estimated an overall BP control rate of 52.5% in the intervention arm. We further assumed an intra-cluster correlation of 0.015 52,53 , a mean cluster size of 8 (standard deviation=5), and a probability of BP control of 35% in the control group 23 . A minimal sample size of 304 (152 in each arm, 19 clusters per study arm) was required to detect superiority with a type I error of 0.025 and a statistical power of 80%. Due to operational reasons and uncertainty of some of the estimates, we decided to recruit in all 103 ComBaCaL cohort villages, for an anticipated sample size of 824 participants. Analyses were performed following the principles for analysis of cluster-randomized trials in health research as outlined by Donner and Klar 54 . The primary hypothesis was assessed in a modified intention-to-treat analysis set, including all participants as randomized except those with pregnancy during the follow-up. For the primary outcome, we used a mixed effect logistic regression model adjusted for stratification factors, sex, age, district, and access to health facilities with a random intercept at the cluster level. Full adherence to the offered care package was not expected and therefore no strict per-protocol analysis was performed. However, to approximate a per-protocol estimate, we pre-specified a safety set considering only participants with at least one hypertension care visit by the CHW in the intervention arm and at least one hypertension care visit at the health facility in the control arm. We conducted a predefined complete case analysis only including participants with available endpoint measurements. We estimated marginal predicted probabilities for SBP and DBP over time, based on the secondary analysis model (linear mixed-effects model with BP as outcome, cluster as random effect, covariate-adjusted as the primary model, but including baseline BP), and derived 95% confidence intervals using 1000 bootstrap samples. In addition, changes in SBP and DBP compared to baseline were modelled using the same adjusted mixed-effects model including 2.5 th and 97.5 th percentiles of predictions on 1000 bootstrap samples. For analyses of secondary outcomes, we used mixed effects logistic or linear regression models adjusted for baseline outcomes in addition to stratification factors, without formal testing. Serious adverse events, adverse events of special interest, adherence to antihypertensive treatment, and exploratory outcomes were analyzed descriptively. Modification of the intervention effect on the primary endpoint was assessed for the following predefined factors: eligibility for pharmacological treatment by the CHW (excluding participants requiring three or more antihypertensive drugs or having an intolerance against amlodipine or hydrochlorothiazide), age, sex, engagement in antihypertensive care at baseline, engagement in HIV care at baseline, access to health facility. Only if the p-values of an interaction term were found to be below 0.1, formal subgroup analyses and subgroup credibility assessments are conducted. Data management was done using Stata IC version 16.0 and data analysis using R version 4.3.3 55,56 . Inclusion and ethics The trial was approved by the National Health Research and Ethics Committee (NH-REC) of Lesotho (ID 102-2022). Additionally, the Ethikkomission Nordwest- und Zentralschweiz (EKNZ) in Switzerland provided a statement confirming the trial meets all ethical requirements for a Swiss research project (ID AO_2022_00074). The intervention was developed in collaboration with local community members and the Lesotho Ministry of Health based on a local non-communicable diseases prevalence survey and burden assessment 23,24 and a scoping literature review on community-based hypertension care in southern Africa 25 . Community advisory boards consisting of patient representatives, CHWs, village authorities, local health facility staff and local Ministry of Health administrative staff advice on the implementation of the larger ComBaCaL project, including this study. Representatives of both community advisory boards are part of the ComBaCaL project steering committee and involved in strategic decision-making. The study addressed an issue of high local relevance. Given the remoteness of most villages, poor transport infrastructure and limited health system resources, community-based health service delivery has been a cornerstone of the Lesotho health system for decades. There is a growing number of people with hypertension and other chronic conditions and the inclusion of non-communicable disease services are considered a priority area in the Lesotho Community-Based Health Services Strategy 57 . It is a guiding principle of the project to align research activities with the local needs and to ensure local impact beyond the generation of novel scientific evidence through capacity building, collaboration, community involvement and health system strengthening activities. Prior to study start, the intervention was piloted in ten villages in both study districts. Feedback from community members and CHWs have guided the design and implementation of the final trial intervention 58,59 . Local researchers have been involved throughout the research process and share authorship. The local and regional research relevant to the study have been included in citations. Declarations Reporting Further information on research design is available in the Nature Portfolio Reporting Summary in the supplementary material. This study was conducted and reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2025 guidelines 60 . The checklist is available in the supplementary material Data availability A de-identified key dataset for reproducing the primary and secondary endpoints is available at https://doi.org/10.5281/zenodo.15674324. The trial was registered with ClinicalTrials.gov (NCT05684055), where a full protocol and statistical analysis plan are available. A study protocol manuscript has been published previously 40 . Requests for access to more detailed data may be made to the corresponding author by submitting a proposal, which will be reviewed by the trial consortium. Code availability The code for the CDSS application used by CHWs for data collection and service delivery can be found at https://github.com/clinepi-usb/cht-combacal. Access to the test environment of the CDSS application is available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Acknowledgements We would like to acknowledge the SolidarMed team in Lesotho and Switzerland, the involved CHWs, and participants for their essential contributions. This study was part of the ComBaCaL project funded by the TRANSFORM grant of the Swiss Agency for Development and Cooperation (project number 7F-10345.01.01) and a grant by the World Diabetes Foundation (WDF-1778). FG’s salary was funded through a personal MD/PhD grant by the Swiss National Science Foundation (grant number 323530_207035). AA’s salary was funded through a grant of the Swiss National Science Foundation (Postdoc mobility #P500PM_221961). The funders had no role in the study design, data collection, analysis, data interpretation or writing of the publication. Authors' contributions AA and NDL were the principal investigators, they acquired the funding, led the project, and conceptualized the study together with FG. FG drafted the manuscript, led the clinical development of the CDSS application together with TT, and the local implementation together with RG. 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CONSORT 2025 statement: updated guideline for reporting randomized trials. Nat. Med. (2025) doi:10.1038/s41591-025-03635-5. Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation20250630.docx Supplementary information Extendeddata.docx nreditorialpolicychecklist20250701.pdf Editorial Policy Checklist Consortchecklist20250630.docx Consort checklist nrreportingsummary20250701.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Lesotho","correspondingAuthor":false,"prefix":"","firstName":"Mosa","middleName":"","lastName":"Tlahali","suffix":""},{"id":480284047,"identity":"3ba48b21-ec14-486c-b1d6-95741805c267","order_by":23,"name":"Malitaba Litaba","email":"","orcid":"","institution":"Ministry of Health Lesotho","correspondingAuthor":false,"prefix":"","firstName":"Malitaba","middleName":"","lastName":"Litaba","suffix":""},{"id":480284048,"identity":"95a2c04c-5e99-4f44-8cbb-a579e41d22bf","order_by":24,"name":"Kevin Kindler","email":"","orcid":"","institution":"University Hospital Basel, Department of Clinical Research, Division of Clinical Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Kindler","suffix":""},{"id":480284049,"identity":"de3ab4d4-df4a-4c9b-8167-aff70455bd50","order_by":25,"name":"Dave Basler","email":"","orcid":"","institution":"University Hospital Basel, Department of Clinical Research, Division of Clinical Epidemiology","correspondingAuthor":false,"prefix":"","firstName":"Dave","middleName":"","lastName":"Basler","suffix":""},{"id":480284050,"identity":"34f70b9c-9a29-4ba4-a010-ebf432835066","order_by":26,"name":"Irene Ayakaka","email":"","orcid":"","institution":"SolidarMed","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"","lastName":"Ayakaka","suffix":""},{"id":480284051,"identity":"735e3b1f-cbe8-4ab0-8afb-e82195a19a19","order_by":27,"name":"Pauline Grimm","email":"","orcid":"","institution":"SolidarMed","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"","lastName":"Grimm","suffix":""},{"id":480284052,"identity":"3e21afea-8d42-4fff-b2da-ba0599343ea0","order_by":28,"name":"Thilo Burkard","email":"","orcid":"https://orcid.org/0000-0001-6373-9820","institution":"University Hospital Basel","correspondingAuthor":false,"prefix":"","firstName":"Thilo","middleName":"","lastName":"Burkard","suffix":""},{"id":480284053,"identity":"9996e91b-f31e-4e06-b4c7-1c45c2b8d0ea","order_by":29,"name":"Frédérique Chammartin","email":"","orcid":"https://orcid.org/0000-0001-8959-2724","institution":"University Hospital Basel","correspondingAuthor":false,"prefix":"","firstName":"Frédérique","middleName":"","lastName":"Chammartin","suffix":""},{"id":480284054,"identity":"55a9ccb7-80c4-411e-9edc-7cd76d4b6501","order_by":30,"name":"Alain Amstutz","email":"","orcid":"https://orcid.org/0000-0003-1716-993X","institution":"University Hospital Basel","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Amstutz","suffix":""}],"badges":[],"createdAt":"2025-07-01 16:35:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7022331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7022331/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41591-026-04208-w","type":"published","date":"2026-02-12T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86310283,"identity":"8db09f26-f91d-4645-93a2-271301878f90","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":562714,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow chart. BP: blood pressure; mITT: modified intention-to-treat.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/b25a4b35ca032ba1a6ab833e.png"},{"id":86310290,"identity":"535c8230-71bc-466d-ab2b-6b2ab90f1e14","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234461,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized mean systolic blood pressure (a), mean diastolic blood pressure (b), change in systolic blood pressure (c) and change in diastolic blood pressure (d) in control and intervention arms. SBP: systolic blood pressure; DBP: diastolic blood pressure.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/eeff7602ec92bc0b2f342e77.png"},{"id":86310284,"identity":"e878ec77-4ec1-4c89-8a60-96d53e2fc699","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128671,"visible":true,"origin":"","legend":"\u003cp\u003eObserved proportion of participants engaged in care and reaching the control target of \u0026lt;140/90 mmHg at baseline, six months, and 12 months by study arm. a: modified intention-to-treat population (n=547 at baseline, n=544 at six months, n=543 at twelve months) with participants with missing endpoint data considered as not engaged and not controlled. b: complete-case population (n=547 at baseline, n= 483 at six months, n= 465 at twelve months) only considering participants with available endpoint data.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/df623a83b749acb0e5b8ebac.png"},{"id":86311583,"identity":"eb24f348-8b6c-4527-b53f-64bbc0b43a76","added_by":"auto","created_at":"2025-07-09 08:12:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":397432,"visible":true,"origin":"","legend":"\u003cp\u003eEngagement in care at baseline, six months, and twelve months by study arm and provider. CHW: Community Health Worker\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/db89c35c81ae7f3f10543cd5.png"},{"id":102575611,"identity":"56cae2c0-f70b-4b3a-9b6d-eb7d3da4587b","added_by":"auto","created_at":"2026-02-13 08:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2972735,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/b784a4eb-e1ab-4126-af85-bf9636de92dc.pdf"},{"id":86311585,"identity":"b3011d58-ef65-48d8-8d1d-8cb227001662","added_by":"auto","created_at":"2025-07-09 08:12:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":127920,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"Supplementaryinformation20250630.docx","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/7f375fc4dbac59dd4c6735af.docx"},{"id":86310288,"identity":"651d7f86-255e-49c1-9a55-7d78b27edb71","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":136231,"visible":true,"origin":"","legend":"","description":"","filename":"Extendeddata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/df09a6baa263a74c4bee285d.docx"},{"id":86310289,"identity":"5376778f-550c-48ff-a1b5-95dd36e7832e","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":693286,"visible":true,"origin":"","legend":"Editorial Policy Checklist","description":"","filename":"nreditorialpolicychecklist20250701.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/bd78fb7886c29c1430f5dd0f.pdf"},{"id":86310287,"identity":"eab27eb6-a6db-45a2-b8c3-7d21744704ac","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":37773,"visible":true,"origin":"","legend":"Consort checklist","description":"","filename":"Consortchecklist20250630.docx","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/705a0a3fdd12cfcae8a53dc7.docx"},{"id":86310292,"identity":"46430c15-b63f-4621-ba5e-1c73d79dd8fd","added_by":"auto","created_at":"2025-07-09 08:04:04","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1257838,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"nrreportingsummary20250701.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7022331/v1/595931bc09880a69e67ead85.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Community Health Worker-led versus facility-based hypertension care for people with uncontrolled blood pressure in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort study","fulltext":[{"header":"Main","content":"\u003cp\u003eGlobally, arterial hypertension is the most important risk factor for premature mortality, accounting for 10.8 million or almost 20% of all deaths in 2019\u003csup\u003e1\u003c/sup\u003e. Low- and middle-income countries (LMICs) bear a disproportionate and growing share of the burden, while many high-income countries have managed to reduce the burden of hypertension substantially over the past decades\u003csup\u003e2\u003c/sup\u003e. Besides demographic and lifestyle changes, insufficient access to preventive, diagnostic and therapeutic services are driving the excessively high morbidity and mortality associated with hypertension in LMICs\u003csup\u003e3\u003c/sup\u003e. In 2019, 78% of the 1.3 billion people with hypertension were living in LMICs with less than 20% of them reaching treatment targets\u003csup\u003e4\u003c/sup\u003e. In LMICs, people living in rural areas and having a lower socioeconomic status face pronounced barriers to accessing hypertension care\u003csup\u003e5\u003c/sup\u003e. Through improved access to antihypertensive medication alone, about 40 million premature deaths could be prevented over the next 25 years\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the global shortage of healthcare professionals, task-shifting to Community Health Workers (CHWs) has been identified as a promising approach to meet the increasing care needs for hypertension and other chronic conditions in LMICs\u003csup\u003e7,8\u003c/sup\u003e. CHWs bring services closer to the community, reduce access barriers such as transport costs, travel time and low awareness, thereby promoting more equitable and less stigmatized access to care\u003csup\u003e9\u003c/sup\u003e.\u0026nbsp;CHW-led hypertension care interventions have been assessed in different settings showing an overall modest effectiveness on blood pressure (BP) control\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. Interventions without pharmacological treatment component were of no or limited effectiveness\u0026nbsp;\u003csup\u003e10,11,13\u003c/sup\u003e, whereas multilevel interventions combining CHW-led screening, monitoring and drug delivery with prescription by physicians have proven highly effective in several large-scale trials\u003csup\u003e12,14\u0026ndash;16\u003c/sup\u003e. However, reliance on physician prescriptions may limit the scalability and workload redistribution of such interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly one trial conducted in rural China evaluated an intervention with independent drug prescription, titration, and monitoring by CHWs\u003csup\u003e17,18\u003c/sup\u003e. The intervention significantly improved BP control rates and reduced cardiovascular events and mortality compared to enhanced usual care\u003csup\u003e17,18\u003c/sup\u003e. However, CHWs in China (\u0026ldquo;village doctors\u0026rdquo;) have a substantially higher level of medical education than CHWs in most other countries and it remains unclear whether the findings can be replicated in systems outside China with lower trained CHWs\u003csup\u003e19\u003c/sup\u003e. Digital clinical decision support systems (CDSS) and simplified treatment protocols using fixed-dose combination pills may enable CHWs with limited training to provide hypertension care safely and effectively\u003csup\u003e20,21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis cluster-randomized trial aimed to assess the safety and effectiveness of an intervention including independent prescription, monitoring and titration of antihypertensive medication by CDSS-assisted lay CHWs among people with uncontrolled hypertension in rural Lesotho. It addressed several of the top research priorities for improving hypertension care identified by the World Health Organization (WHO), including the development and evaluation of systems for hypertension care delivery closer to home, of CHW-led medication prescription and titration, \u0026nbsp;of a digital approach to improving retention in care, and \u0026nbsp;of \u0026nbsp;task-sharing that addresses barriers in relevant settings\u003csup\u003e22\u003c/sup\u003e. The intervention was developed in collaboration with local community members and the Lesotho Ministry of Health based on a local non-communicable diseases prevalence survey and burden assessment\u003csup\u003e23,24\u003c/sup\u003e and a scoping literature review\u003csup\u003e25\u003c/sup\u003e. The trial was conducted within the Community-Based Chronic disease care Lesotho (ComBaCaL) cohort study (NCT05596773)\u003csup\u003e26\u003c/sup\u003e. This cohort comprises the population of 103 villages in rural Lesotho and serves as platform for the investigation of chronic diseases and their management. It is embedded within the government-led community health program and managed by local CHWs.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eBaseline characteristics\u003c/h3\u003e\n\u003cp\u003eBetween September 08, 2023, and January 31, 2024, CHWs screened 6’641 out of 8’835 eligible adult ComBaCaL cohort participants for hypertension during home-visits across 103 rural villages. They identified 1’262 cohort participants with hypertension, out of which 547 (274 control, intervention) from 97 villages (47 intervention, 50 control) had uncontrolled BP levels (≥140/90mmHg) and were not pregnant (figure 1).\u003c/p\u003e\n\u003cp\u003eThe trial participants had a mean age of 62.3 years and a mean BP of 149.0 (standard deviation (SD) 16.8) over 95.0 (SD 10.1) mmHg; 75.0% were female; 44.6% reported use of antihypertensive medication at enrolment; 35.1% were smokers, 8.4% had diabetes; 15.7% reported living with HIV; 1.3% had heart failure and 4.9% had a history of stroke or myocardial infarction. No substantial imbalance across the arms was observed (table 1 and extended data table 1).\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline characteristics of participants by study arm.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster-level characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVillages, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParticipants per village, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0 (3.0, 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 (3.0, 8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 (3.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHard access to health facility\u003csup\u003e1\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDistrict Butha Buthe (versus Mokhotlong), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual-level characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParticipants, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics and anthropometrics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200 (73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e210 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e410 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.6 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.0 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.3 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.0 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbdominal circumference (cm), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.8 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.6 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.7 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation at secondary school or higher, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorking for pay or self-employed, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e216 (39.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried or in a stable relationship, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e286 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147.5 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150.5 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e149.0 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94.5 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95.4 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95.0 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngaged in hypertension care, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e244 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD risk, lifestyle and comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease risk ≥10%\u003csup\u003e2\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131 (50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e150 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e281 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e192 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlcohol consumption\u0026nbsp;≥1 day/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate or high physical activity\u003csup\u003e3\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e217 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e231 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e448 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn care for a chronic condition\u003csup\u003e4\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of stroke or myocardial infarction (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHIV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatin use among eligible participants\u003csup\u003e5\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dl), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153.2 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e154.9 (40.6))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e154.0 (41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL cholesterol (mg/dl), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.6 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.1 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.8 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePortion of vegetables and fruits/day, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlways or often adding salt to food while eating, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet beverage consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFried food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 1 legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1: Needing to cross a mountain or river or travel \u0026gt;10 km to the nearest health facility\u003c/p\u003e\n\u003cp\u003e2: 10-year risk for a cardiovascular event, using the WHO laboratory-based cardiovascular risk calculator\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e3: Self-reported physical activity using the International Physical Activity Questionnaire Short Form (IPAQ-S)\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e4: HIV and/or diabetes\u003c/p\u003e\n\u003cp\u003e5: Participants with history of stroke, myocardial infarction, diabetes, or chronic kidney disease or aged\u0026nbsp;≥50 years and having a BMI of\u0026nbsp;≥30kg/m\u003csup\u003e2\u003c/sup\u003e or aged ≥50 years and currently smoking were eligible for statin. Number of participants eligible is 289 (129 in control and 159 in intervention)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Abbreviations: SD: standard deviation; CVD: cardiovascular disease; LDL: low-density lipoprotein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMissing data: BMI: 3 control, 3 intervention; abdominal circumference: 2, 8; education: 0, 1; use of antihypertensive medication: 0, 1; WHO cardiovascular risk: 14, 28; alcohol consumption: 1, 1; physical activity: 0, 3; total cholesterol: 14, 28; LDL cholesterol: 25, 57; adding salt to food: 0, 1.\u003c/p\u003e\n\u003ch3\u003ePrimary outcome and blood pressure-related secondary outcomes\u003c/h3\u003e\n\u003cp\u003eIn the primary modified intention-to-treat analysis including 543 participants (four exclusions due to pregnancy during follow-up), BP control rates after twelve months were 156/271 (58%) and 130/272 (48%) in the intervention and control arm (table 2). The adjusted odds ratio (aOR) of the intervention on controlled BP was aOR 1.52 (95% confidence interval (CI): 1.01; 2.29; p-value = 0.046). This corresponds to an average intervention effect of 12.5% (95% CI: 2.2; 22.5) increase in BP control rate. In a predefined sensitivity analysis only including participants with available BP measurements at 12 months (complete case), control rates were 156/227 (69%) and 130/238 (55%) in the intervention and control arm; aOR 1.95 (1.23; 3.10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt six months, the BP control rates in the modified intention-to-treat analysis were 144/271 (53%) in the intervention arm and 120/273 (44%) in the control arm; aOR 1.37 (0.91; 2.05).\u003c/p\u003e\n\u003cp\u003eThe mean systolic (SBP) and diastolic blood pressure (DBP) after twelve months were -4.2 mmHg (-7.6; -0.8) and -2.4 mmHg (-4.6; -0.2) lower in the intervention compared to the control arm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2 Intervention effects on primary endpoint and blood pressure-related secondary endpoints\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eaOR or adjusted mean difference (95% CIs)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary endpoint analysis\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg at 12 months, n (%), mITT\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52 (1.01; 2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN= 238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg at 12 months, n (%), complete case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.95 (1.23; 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP-related secondary endpoints at 12 months\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP, mmHg, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134.7 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.1 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.2 ( -7.6; -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP, mmHg, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.2 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.1 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.4 ( -4.6; -0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBP-related secondary endpoints at six months\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg, n (%), mITT\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37 (0.91; 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg, n (%), complete case\u003csup\u003e6\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64 (1.08; 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP, mmHg, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135.6 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e132.7 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.6 ( -6.5; -0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP, mmHg, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.0 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.7 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.6 (-4.4; -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2 legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1: Effects are derived from mixed effect regression models adjusted for clustering and covariates (sex, age, district, facility access and baseline values when appropriate)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2: Four participants excluded due to pregnancy between enrolment and twelve months\u003c/p\u003e\n\u003cp\u003e3: Participants with missing BP measurement were considered as having a BP ≥140/90mmHg\u003c/p\u003e\n\u003cp\u003e4: Only including participants with available BP measurement at twelve months: 238 control, 227 intervention\u003c/p\u003e\n\u003cp\u003e5: Three participants excluded due to pregnancy between enrolment and six months\u003c/p\u003e\n\u003cp\u003e6: Only including participants with available BP measurement at six months: 247 control, 236 intervention\u003c/p\u003e\n\u003cp\u003eAbbreviations: BP: blood pressure; SD: standard deviation; SBP: systolic blood pressure; DBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eMissing data: SBP and DBP at twelve months: 34 control, 44 intervention. SBP and DBP at six months: 26 control, 35 intervention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Compared to baseline, the mean SBP decreased by -19.2 mmHg (-21.8; -16.5) in the intervention and by -13.7 mmHg (-16.3; -11.2) in the control arm after twelve months. The mean DBP decreased by -11.4 mmHg (-12.9; -9.8) in the intervention and by -8.5 mmHg (-9.9; -7.2) in the control arm (figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Interaction p-values for the pre-specified potential effect modifiers (sex, age, engagement in hypertension care at baseline, access to health facility, and engagement in HIV care) were larger than 0.1 and therefore no subgroup credibility assessments were performed (see extended data table 4 and extended data figure 1).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFurther secondary outcomes\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eFigure 3 shows engagement in care and BP control rates at zero, six and 12 months by arm in the modified intention-to-treat and the complete case population. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt six months, 220/271 (81.2%) participants in the intervention arm and 190/273 (69.6%) in the control arm were engaged in care; aOR 1.94 (1.27; 2.95) (table 3, figure 3). Among those not engaged in hypertension care at baseline, in the intervention arm 130/151 (86.1%) participants linked to care by six months compared to 44/150 (29.3%) in the control arm, aOR 16.65 (8.30; 33.41; table 3). Among the 220 engaged in care in the intervention arm, 186/220 (84.5%) were in care with their CHW and 34/220 (15.5%) at the health facility (two due to intolerance to amlodipine or hydrochlorothiazide, nine did not reach the treatment target with amlodipine and hydrochlorothiazide, 23 preferred facility-based care) (figure 4).\u003c/p\u003e\n\u003cp\u003eAt twelve months, 220/271 (81.2%) participants were engaged in care in the intervention arm versus 196/272 (72.1%) in the control arm; aOR 1.65 (1.09; 2.51) (table 3, figure 3). In the intervention arm 190/220 (86.4%) were in care with their CHW and 30/220 (13.6%) at the health facility (three due to intolerance to amlodipine or hydrochlorothiazide, twelve did not reach the treatment targets with amlodipine and hydrochlorothiazide, 15 preferred facility-based care) (figure 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There were no significant differences between arms in the estimated 10-year cardiovascular event risk, body-weight, BMI, abdominal circumference, smoking, alcohol consumption, and dietary habits (table 3, extended data tables 2 and 3). The proportion of participants self-reportedly engaging in moderate or high or physical activity was higher in the intervention arm at twelve months, but not at six months. The proportion of participants using statin among those eligible was higher in the intervention at six months, but not at twelve months. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Further secondary outcomes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIntervention\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eaOR or adjusted mean difference (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary endpoints at 12 months\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngagement in care\u003csup\u003e2\u003c/sup\u003e, n (%), mITT\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e196 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.65 (1.09; 2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngagement in care, n (%), complete case\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e196 (82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220 (96.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.51 (3.45; 45.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLinkage to care\u003csup\u003e5\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133 (88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.20 (6.75; 25.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Already on treatment at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdherence to antihypertensive medication\u003csup\u003e6\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e148 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49 (0.18; 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Not on treatment at 12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWHO CVD risk\u0026nbsp;≥10%, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94 (0.45; 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol, mg/dL, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e162.2 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e163.0 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.9 (-11.4; 9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL, mg/dL, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.0 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.2 (41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 (-2.2; 13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.7 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.0 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4 (-0.8: 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbdominal circumference, cm, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.3 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.2 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2 (-1.4: 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate or high physical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e182 (80.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.35 (1.48; 36.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.60 (0.59; 4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlcohol consumption\u0026nbsp;≥1 day/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15 (0.50; 2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatin use among eligible participants\u003csup\u003e7\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28 (0.8; 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Not eligible for statin use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePortion of vegetables and fruits/day, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 (-0.07; 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlways or often adding salt to food, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76 (0.07;7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32 (0.43; 4.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet beverage consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28 (0.52; 3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFried food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.10; 10.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary endpoints at six months\u003csup\u003e8\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngagement in care, n (%), mITT\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.94 (1.27; 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEngagement in care, n (%), complete case\u003csup\u003e4\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e190 (76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.97 (2.20; 11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLinkage to care\u003csup\u003e5\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130 (86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.65 (8.30; 33.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Already in care at baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdherence to antihypertensive medication\u003csup\u003e6\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139 (72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56 (0.23; 1.38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Not on treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWHO CVD risk\u0026nbsp;≥10%, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e115 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79 (0.38; 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol, mg/dL, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e153.5 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156.2 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.3 (-10.2; 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL, mg/dL, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.4 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.0 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.9 (-9.6; 7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.7 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.8 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.4 (-0.8; 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbdominal circumference, cm, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.4 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.3 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 (-1.3; 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate or high physical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176 (71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e182 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64 (0.64; 4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73 (0.27; 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlcohol consumption\u0026nbsp;≥1 day/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.47; 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatin use among eligible participants\u003csup\u003e7\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.96 (1.44; 6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Not eligible for statin use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePortion of vegetables and fruits/day, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01 (-0.11; 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlways or often adding salt to food, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.48; 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66 (0.21; 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweet beverage consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.36 (0.07; 24.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFried food consumption\u0026nbsp;≥3 days/week, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96 (0.34; 2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1: Four participants excluded due to pregnancy between enrolment and 12 months\u003c/p\u003e\n\u003cp\u003e2: Reporting use of antihypertensive medication or reaching treatment target without medication\u003c/p\u003e\n\u003cp\u003e3: Participants with missing measurement were considered as not being engaged in care\u003c/p\u003e\n\u003cp\u003e4: Only including participants with available endpoint data\u003c/p\u003e\n\u003cp\u003e5: Proportion of participants who have initiated antihypertensive treatment since enrolment among those not taking treatment at baseline\u003c/p\u003e\n\u003cp\u003e6: Reporting full adherence to antihypertensive medication over the last four days\u003c/p\u003e\n\u003cp\u003e7: This was an exploratory endpoint. Statin eligible: participants with history of stroke, myocardial infarction, diabetes, or chronic kidney disease or aged\u0026nbsp;≥50 years and having a BMI of\u0026nbsp;≥30kg/m\u003csup\u003e2\u003c/sup\u003e or aged\u0026nbsp;≥50 years and currently smoking\u003c/p\u003e\n\u003cp\u003e8: Three participants excluded due to pregnancy between enrolment and 6 months\u003c/p\u003e\n\u003cp\u003eAbbreviations: IQR: interquartile range; SD: standard deviation; BMI: body-mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; LDL: low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eMissing data at twelve months: engagement in care: 33 control, 44 intervention; adherence to antihypertensive medication: 3, 1; WHO CVD risk: 35, 46; total cholesterol: 35, 46; LDL: 58, 7; BMI: 52, 48; abdominal circumference: 48, 56; physical activity: 33, 45; smoking: 33, 44; alcohol consumption: 34, 49; statin use: 34, 44, vegetables and fruit consumption: 33, 45; adding salt to food: 0, 1; sweet food consumption: 33, 45; sweet beverage consumption: 33, 45; fried food consumption: 33, 45.\u003c/p\u003e\n\u003cp\u003eMissing data at six months: engagement in care: 23 control, 33 intervention, total cholesterol: 32, 52; LDL: 62, 73; BMI: 34, 43; abdominal circumference: 32, 40; physical activity: 25, 44; smoking: 24, 34; alcohol consumption: 25, 46; statin use: 23, 34; vegetables and fruit consumption: 25, 44; adding salt to food: 0, 1; sweet food consumption: 25, 44; sweet beverage consumption: 25, 44; fried food consumption: 25, 44.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Safety outcomes\u003c/p\u003e\n\u003cp\u003eBy twelve months, six participants in the control arm and three in the intervention arm had died. Two participants in the control arm have been hospitalized, while no non-fatal serious adverse events were reported in the intervention group. None of the events were related to study participation. One adverse event of special interest was observed the control group and nine in the intervention group. All adverse events of special interest in the intervention group were related to ankle swelling in association with intake of amlodipine and resolved after switching antihypertensive medication. One stroke was reported in the control arm.\u003c/p\u003e\n\u003cp\u003eIn a safety set analysis - considering only outcomes of participants with at least one hypertension care visit by the CHW in the intervention arm or at the health facility in the control arm - BP control rates at 12 months were 156/258 (60.5%) in the intervention arm and 94/206 (45.6%) in the control arm; aOR 1.93 (1.25; 2.97) with similar results observed at six months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4 Safety set analysis and safety endpoints\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eaOR (95% CIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafety set analysis at 12 months\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=206\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg, n (%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e94 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.93 (1.25; 2.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafety set analysis at six months\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBP \u0026lt;140/90 mmHg, n (%)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e144 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.94 (1.27; 2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSafety endpoints at 12 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN=273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeaths, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-fatal serious adverse event\u003csup\u003e3\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdverse events of special interest\u003csup\u003e4\u003c/sup\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Stroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Side effects of antihypertensive medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 legend\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1: Only considering outcomes of participants with at least one visit for hypertension care by the CHW in the intervention arm or at the health facility in the control arm\u003c/p\u003e\n\u003cp\u003e2: Participants with missing BP measurement were considered as having a BP ≥140/90mmHg\u003c/p\u003e\n\u003cp\u003e3: Serious adverse events defined as death, hospitalization, life-threatening event, event leading to persistent disability\u003c/p\u003e\n\u003cp\u003e4: Adverse events of special interest defined as adverse events consistent with hypertension complications, such as myocardial infarction, stroke, heart failure or chronic kidney disease as well as adverse events related to the intake of antihypertensive medication leading to the discontinuation of the medication concerned\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this trial\u0026nbsp;CDSS-assisted hypertension care including independent drug prescription, monitoring and titration by lay CHWs improved BP control rates, mean BP, linkage to care and engagement in care compared to facility-based hypertension care among non-pregnant adults with uncontrolled hypertension living in rural Lesotho. Using a fixed-dose combination pill containing amlodipine and hydrochlorothiazide at two different dosages, lay CHWs independently managed most participants at the community level with no associated safety concerns over 12 months.\u003c/p\u003e\n\u003cp\u003eGiven the increasing number of people requiring hypertension care and the limited professional healthcare workforce in LMICs, the need for care models that include task-shifting to non-physician healthcare workers is evident and recommended by the WHO\u003csup\u003e8\u003c/sup\u003e. Interventions\u0026nbsp;combining\u0026nbsp;CHW-led screening, counselling, drug delivery and monitoring services with physician prescription\u0026nbsp;were\u0026nbsp;found to significantly reduce BP levels and to be cost-effective in several large-scale trials\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. The HOPE-4 study, conducted in Colombia and Malaysia, showed a significant cardiovascular risk and BP reduction through non-physician healthcare worker-led cardiovascular risk screening using a CDSS application, algorithm-guided treatment recommendations, lifestyle counselling and drug delivery after physician prescription\u003csup\u003e14\u003c/sup\u003e. The HOPE-4 intervention led to a large mean SBP reduction of -11.5 mmHg (-14.9; -8.0)\u003csup\u003e14\u003c/sup\u003e. However, the generalizability of the findings may be limited because the intervention was not fully embedded within the existing health system infrastructure with non-physician health workers being employed for the study and medication being provided free of charge only in the intervention arm\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe China Rural Hypertension Control Project (CRHCP) was the first study that demonstrated the feasibility and effectiveness of a multifaceted intervention that included independent drug prescription, monitoring and titration by CHWs on BP and mortality reduction\u003csup\u003e17,18\u003c/sup\u003e. Enrolling 33,995 individuals with uncontrolled BP in rural China, the intervention achieved an average SBP reduction of 14.5 mmHg (95% CI: -15.7 to -13.3). However, several factors may limit the generalizability of the findings of the CRHCP trial\u003csup\u003e19\u003c/sup\u003e. CHWs in China, called \u0026ldquo;village doctors\u0026rdquo;, are accredited after three years of medical education or twenty years of continuous practicing in village clinics and have routine prescription rights for essential medications, unlike lay CHWs in most LMICs\u003csup\u003e30\u003c/sup\u003e. Participants were provided with automated BP machines and trained on home self-measurements\u003csup\u003e31\u003c/sup\u003e, a strategy that may not be feasible and cost-effective in most LMIC settings\u003csup\u003e19\u003c/sup\u003e. Health system components of the intervention, such as provision of discounted or free medications and the use of hypertension control rates as a metric for resource allocation at health system level may be challenging to implement in other settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe intervention in our study was designed to address some of the limitations identified in previous landmark studies. It is the first study to assess CHW-led hypertension care including independent drug prescription, monitoring and titration by CHWs outside China. It addresses several of the top research priorities for improving hypertension care identified by the WHO and responds to calls for contextualized clinical and health system hypertension research in Africa\u003csup\u003e22,32\u003c/sup\u003e. The intervention was integrated within the routine government CHW system. Participating CHWs were lay people elected by the village population according to the national Village Health Program Policy\u003csup\u003e33\u003c/sup\u003e. They reported to the supervising health facility during routine monthly meetings, where they also received medication supply through the routine supply chain system. The study team only assisted in remote monitoring through the CDSS application to ensure participant safety and completeness of data. The CDSS application that guided CHWs in service delivery and data collection through algorithmic guidance including algorithmic task scheduling was based on the widely available open-source, offline first, interoperable Community Health Toolkit software platform\u003csup\u003e34\u003c/sup\u003e. Screening, diagnosis and treatment algorithms applied by CHWs followed national guidelines with no formal intervention at the facility level. The intervention may thus be easily adoptable and scalable in different settings, especially where a CHW system is already established. Despite its relative simplicity, the intervention did not only outperform professional facility-based care but also led to an even greater SBP reduction than observed in previous landmark studies (-19.2 mmHg (-21.8; -16.5)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur intervention contained a cardiovascular risk assessment along with CHW-led statin prescriptions. Unlike the HOPE-4 study, we did not observe an effect on the estimated 10-year cardiovascular risk. This discrepancy may be due to the more stringent eligibility criteria for statin prescriptions (59% of participants eligible compared to 93% in HOPE-4), the use of different cardiovascular risk assessment tools (WHO risk calculator versus Framingham risk score), and the insufficient uptake of statins in our study to which difficulties with supply and the CDSS application may have contributed. Our findings, alongside those from the HOPE-4 study, demonstrate the feasibility of algorithm-guided cardiovascular risk assessments conducted by CHWs. The use of fixed-dose combinations that include statins and antihypertensive drugs might be an enabling factor to increase statin uptake and adherence in future CHW-led interventions\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLike previous studies, we did not observe relevant effects on behavioral cardiovascular risk factors and associated outcomes, such as BMI or abdominal circumference\u003csup\u003e16,36\u0026ndash;38\u003c/sup\u003e. Further research is required on how the effectiveness of CHW-led lifestyle counselling in hypertension management could be improved\u003csup\u003e39\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, it only included participants with uncontrolled BP at baseline. To assess the effectiveness of the intervention among all people living with hypertension, another trial within the ComBaCaL cohort was conducted that enrolled participants with controlled BP at baseline\u003csup\u003e40\u003c/sup\u003e. Second, while the CHWs in our study were part of the government CHW system, they received an incentive of roughly 20$ per month in addition to the regular government stipend of about 40$ to compensate the study-related workload. The additional incentive was the same in the control and intervention arm. Third, the CHWs who delivered the intervention also assessed all endpoints. Desirability and recall bias may affect the self-reported behavioral outcomes. The CHWs were trained and guided by the CDSS application for correct BP measurements with the application automatically calculating the average of the last two out of three entered measurements as outcome. We thus deem the risk of measurement bias on the primary outcome as small. Fourth, we used a pragmatic titration algorithm that only included a combination of amlodipine and hydrochlorothiazide at two dosages and the same treatment target of 140/90 mmHg for all participants. Amlodipine and hydrochlorothiazide are the only drugs available as fixed-dose combination pill at an affordable price. They have a favorable effectiveness and safety profile, especially in absence of electrolyte monitoring\u003csup\u003e32,41\u003c/sup\u003e. However, this pragmatic approach led to suboptimal treatment for participants with comorbidities, such as diabetes or chronic kidney disease, that would benefit from a renin-angiotensin-aldosterone-system blocker and younger participants for whom more stringent treatment targets may be recommended. Based on our experience, increasing the treatment complexity for CHW-led, CDSS-supported management may be considered in future projects. Fifth, this study assessed the effectiveness of a CHW-led hypertension and cardiovascular risk management while there is a consensus that in most settings, CHWs should provide a comprehensive, integrated service package rather than single-disease focused interventions. However, this trial was implemented in parallel with a similar trial within the ComBaCaL cohort assessing the effectiveness of CHW-led diabetes management\u003csup\u003e42\u003c/sup\u003e. Further, the CHWs conducted routine government tasks related to health promotion and linkage services that were not formally monitored within the study. Thus, the intervention was partially integrated with other services while a formal assessment of the integrated care delivery is lacking. Further research is needed to assess both, the (cost)effectiveness of targeted interventions for other conditions and their integration to develop an evidence-based CHW-led essential service package.\u003c/p\u003e\n\u003cp\u003eLay CHWs guided by an open-source CDSS application can safely deliver hypertension care including independent first-line drug prescription, monitoring and titration, thereby improving engagement in care and BP control in a remote rural setting. Health policies should consider the broader adoption to address prevailing hypertension care gaps.\u003c/p\u003e"},{"header":"Online methods","content":"\u003ch2\u003eStudy design and setting\u003c/h2\u003e\n\u003cp\u003eWe conducted a 1:1 cluster-randomized, open-label, superiority trial nested within the ComBaCaL cohort study following the Trials within Cohorts (TwiCs) design\u003csup\u003e43\u003c/sup\u003e. The ComBaCaL cohort study is conducted in 103 randomly selected villages in the two rural districts, Butha-Buthe and Mokhothlong, in Lesotho, a small, landlocked country surrounded by South Africa. In each ComBaCaL village, a CHW was elected by the community according to the procedures outlined in the Lesotho Ministry of Health \u0026nbsp;Village Health Program policy\u003csup\u003e33\u003c/sup\u003e. CHWs play an important role in the Lesotho healthcare system providing health promotion services and linking the community to facility-based care, mainly for maternal and child health and HIV\u003csup\u003e33,44,45\u003c/sup\u003e. Lesotho is a typical example of an African LMIC where a developing health system is facing the double burden of still highly prevalent infectious diseases (HIV/AIDS and tuberculosis) in combination with a rapidly growing non-communicable disease epidemic\u003csup\u003e24,46,47\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eScreening and enrolment\u003c/h2\u003e\n\u003cp\u003eParticipants were recruited among the ComBaCaL cohort population, which includes all consenting inhabitants of the 103 randomly selected rural villages. ComBaCaL cohort participants aged 18 years or older were screened for hypertension by their CHW during home visits according to the diagnostic algorithm of the Lesotho Standard Treatment Guidelines\u003csup\u003e48\u003c/sup\u003e (supplementary information figure 1). All non-pregnant adult ComBaCaL cohort participants with hypertension, defined as reporting intake of antihypertensive medication or being newly diagnosed during screening with uncontrolled BP levels (\u0026ge;140/90) were eligible. BP\u0026nbsp;was measured using automated devices (Omron M3 Comfort [HEM7131-E]). Measurements were taken after selecting the appropriate cuff size, with participants seated comfortably\u0026mdash;feet flat on the floor, arm supported, and remaining still and silent for five minutes. During the first visit, BP was measured in both arms to determine the reference arm for subsequent assessments.\u0026nbsp;The arm with higher systolic BP was identified as reference arm and used for all subsequent BP measurements. The BP value was calculated as the mean of the last two out of three consecutive measurements at intervals of one minute.\u0026nbsp;For the diagnosis of hypertension, two elevated measurements in the range of 140-179/90-109 mmHg on two different days or two measurements of 180/110 mmHg or higher on the same day, at least 30 minutes apart, were required.\u003c/p\u003e\n\u003ch2\u003eRandomization and blinding\u003c/h2\u003e\n\u003cp\u003eHalf of the ComBaCaL cohort villages were randomly allocated to the intervention group by a statistician not involved in the study before the start of the hypertension screening. The randomization was stratified by district (Butha-Buthe versus Mokhothlong) and access to health facilities (easy versus difficult access, defined as needing to cross a mountain or river or travel \u0026gt;10 km to the nearest health facility). Participants were blinded to the allocation, meaning that participants in the control villages were not aware of the intervention being implemented in other villages. CHWs who enrolled participants, provided the intervention, and collected endpoint data were not blinded. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eOutcomes\u003c/h2\u003e\n\u003cp\u003eThe primary endpoint was BP control rate (proportion of participants with BP \u0026lt;140/90mmHg) twelve months after enrolment. Secondary endpoints were the BP control rate after six months, mean SBP, mean DBP, engagement in care (proportion of participants reporting intake of antihypertensive medication or reaching treatment targets without intake of medication), linkage to care (proportion of participants not taking treatment at enrolment who have initiated antihypertensive treatment during follow-up), self-reported adherence to antihypertensive treatment, 10-year cardiovascular risk using the WHO cardiovascular disease risk prediction tool\u003csup\u003e27\u003c/sup\u003e, BMI, abdominal circumference, blood lipid status, physical activity using the International Physical Activity Questionnaire Short Form (IPAQ-SF)\u003csup\u003e28\u003c/sup\u003e, alcohol consumption, tobacco use, and dietary habits using a shortened unquantified food frequency questionnaire adapted from an assessment tool for obesity used in South Africa\u003csup\u003e49\u003c/sup\u003e, fruit, vegetable and salt consumption using the WHO steps instrument\u003csup\u003e50\u003c/sup\u003e six and twelve months after enrolment (see footnotes of extended data tables 1, 2, and 3 for details). Safety outcomes were the occurrence of Serious Adverse Events (SAEs) and Adverse Events of Special Interest (AESIs) within six and twelve months after enrolment. AESIs were defined as adverse events consistent with hypertension complications, such as myocardial infarction, stroke, heart failure or chronic kidney disease as well as adverse events related to the intake of antihypertensive medication leading to the discontinuation of the medication concerned. Exploratory endpoints included the use of statins among people with elevated cardiovascular risk, further exploratory endpoints will be reported separately. In addition to the pre-specified endpoints, the change in mean SBP and DBP compared to baseline is reported to allow for comparison with other relevant trials in the field.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor all endpoints measured after six months, a window of 150 to 240 days, and for twelve months\u0026rsquo; endpoints, a window of 300 to 420 days after enrolment applied.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProcedures\u003c/h2\u003e\n\u003cp\u003eIn each village, there was one CHW, equipped with a password-protected tablet loaded with a tailored CDSS application. The CDSS application provided algorithmic guidance for all study procedures and served as a data collection tool. The application was configured by the study team based on the open source Community Health Toolkit Core Framework (versions 3.12, 3.14, 4.6 and 4.13), a widely-used, offline-first, open source software toolkit designed for community health systems\u003csup\u003e34\u003c/sup\u003e. Data were synchronized regularly to a secure server hosted at the University Hospital Basel, Switzerland. Data were monitored locally by the CHW supervisors and centrally by the data management team of the University Hospital Basel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHWs were elected by the village population in a participatory process according to the Lesotho Ministry of Health Village Health Program policy\u003csup\u003e33\u003c/sup\u003e. All CHWs received a one-week training on data collection, screening, and diagnosis of hypertension. CHWs in the intervention villages received an additional two-days training on the intervention services. In both control and intervention villages, CHWs received the regular government stipend of roughly 40$ per month and an additional 20$ to compensate the work related to study participation.\u003c/p\u003e\n\u003cp\u003eIn control villages, CHWs offered lifestyle counselling and referred participants to their closest health facility for further care after diagnosis and enrolment. At six months, CHWs conducted a check-up with lifestyle counselling and another referral in case the participant had not linked to care yet.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn intervention villages, CHWs offered a community-based hypertension care package that included lifestyle counselling, prescription of antihypertensive fixed-dose combinations containing amlodipine and hydrochlorothiazide at low-dose (5mg/12.5mg) or high-dose (10mg/25g), lipid-lowering (atorvastatin 10mg) and antiplatelet (acetyl salicylic acid 100mg) treatment for eligible participants (supplementary information figure 2). Screening, diagnosis, as well as treatment initiation and monitoring followed the recommendations of the current Lesotho Standard Treatment Guidelines\u003csup\u003e48\u003c/sup\u003e. Insufficient BP control under high-dose antihypertensive fixed-dose combination, potential contraindications, side effects or the presence of clinical alarm signs or symptoms, triggered the CDSS application to recommend referral to the health facility. Participants were free to accept or refuse the services offered by the CHW at any time. Participants not eligible for CHW-led care or refusing it were referred to their closest health facility for further management with bi-monthly follow-ups by the CHW at the community level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data were collected by CHWs during home visits. During follow-up visits, CHWs inquired about the occurrence of possible adverse events. In addition, CHWs solicited adverse events through reporting by participants, friends or relatives, screening of participants\u0026rsquo; personal health booklet and reporting by routine health facility staff any time during the follow-up period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdverse events reported by the CHWs were followed-up by supervising study staff to collect further clinical information. The pseudonymized reports were submitted to the study physician who classified the reports as SAE, AESI or neither of the two.\u003c/p\u003e\n\u003ch2\u003eSample size and statistical analysis\u003c/h2\u003e\n\u003cp\u003eThe sample size was calculated assuming an individual randomization inflated by a design effect that accounts for variation at cluster level, according to Rotondi and Donner\u003csup\u003e51\u003c/sup\u003e. Based on preliminary results from a non-communicable disease prevalence survey in Lesotho\u003csup\u003e32\u003c/sup\u003e, we expected an adult prevalence of hypertension of 18% with 55% having BP levels\u0026nbsp;\u0026ge;140/90mmHg\u003csup\u003e24\u003c/sup\u003e. Considering an average village size of 100 adult inhabitants, the average cluster size of eligible participants was estimated at eight. Assuming that 75% would accept the CHW care intervention, a probability of BP control of 60% among participants accepting the intervention and of 30% among those refusing it, we estimated an overall BP control rate of 52.5% in the intervention arm. We further assumed an intra-cluster correlation of 0.015\u003csup\u003e52,53\u003c/sup\u003e, a mean cluster size of 8 (standard deviation=5), and a probability of BP control of 35% in the control group\u003csup\u003e23\u003c/sup\u003e. A minimal sample size of 304 (152 in each arm, 19 clusters per study arm) was required to detect superiority with a type I error of 0.025 and a statistical power of 80%. Due to operational reasons and uncertainty of some of the estimates, we decided to recruit in all 103 ComBaCaL cohort villages, for an anticipated sample size of 824 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalyses were performed following the principles for analysis of cluster-randomized trials in health research as outlined by Donner and Klar\u0026nbsp;\u003csup\u003e54\u003c/sup\u003e. The primary hypothesis was assessed in a modified intention-to-treat analysis set, including all participants as randomized except those with pregnancy during the follow-up. For the primary outcome, we used a mixed effect logistic regression model adjusted for\u0026nbsp;stratification factors, sex, age, district, and\u0026nbsp;access to health facilities with a random intercept at the cluster level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFull adherence to the offered care package was not expected and therefore no strict per-protocol analysis was performed. However, to approximate a per-protocol estimate, we pre-specified a safety set considering only participants with at least one hypertension care visit by the CHW in the intervention arm and at least one hypertension care visit at the health facility in the control arm. We conducted a predefined complete case analysis only including participants with available endpoint measurements. We estimated marginal predicted probabilities for SBP and DBP over time, based on the secondary analysis model (linear mixed-effects model with BP as outcome, cluster as random effect, covariate-adjusted as the primary model, but including baseline BP), and derived 95% confidence intervals using 1000 bootstrap samples. In addition, changes in SBP and DBP compared to baseline were modelled using the same adjusted mixed-effects model including 2.5\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e percentiles of predictions on 1000 bootstrap samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor analyses of secondary outcomes, we used mixed effects logistic or linear regression models adjusted for baseline outcomes in addition to stratification factors, without formal testing. Serious adverse events, adverse events of special interest, adherence to antihypertensive treatment, and exploratory outcomes were analyzed descriptively. Modification of the intervention effect on the primary endpoint was assessed for the following predefined factors: eligibility for pharmacological treatment by the CHW (excluding participants requiring three or more antihypertensive drugs or having an intolerance against amlodipine or hydrochlorothiazide), age, sex, engagement in antihypertensive care at baseline, engagement in HIV care at baseline, access to health facility. Only if the p-values of an interaction term were found to be below 0.1, formal subgroup analyses and subgroup credibility assessments are conducted.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData management was done using Stata IC version 16.0 and data analysis using R version 4.3.3\u003csup\u003e55,56\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eInclusion and ethics\u003c/h2\u003e\n\u003cp\u003eThe trial was approved by the National Health Research and Ethics Committee (NH-REC) of Lesotho (ID 102-2022).\u0026nbsp;Additionally, the Ethikkomission Nordwest- und Zentralschweiz (EKNZ) in Switzerland provided a statement confirming the trial meets all ethical requirements for a Swiss research project (ID AO_2022_00074).\u0026nbsp;The intervention was developed in collaboration with local community members and the Lesotho Ministry of Health based on a local non-communicable diseases prevalence survey and burden assessment\u003csup\u003e23,24\u003c/sup\u003e and a scoping literature review on community-based hypertension care in southern Africa\u003csup\u003e25\u003c/sup\u003e. Community advisory boards consisting of patient representatives, CHWs, village authorities, local health facility staff and local Ministry of Health administrative staff advice on the implementation of the larger ComBaCaL project, including this study. Representatives of both community advisory boards are part of the ComBaCaL project steering committee and involved in strategic decision-making. The study addressed an issue of high local relevance. Given the remoteness of most villages, poor transport infrastructure and limited health system resources, community-based health service delivery has been a cornerstone of the Lesotho health system for decades. There is a growing number of people with hypertension and other chronic conditions and the inclusion of non-communicable disease services are considered a priority area in the Lesotho Community-Based Health Services Strategy\u003csup\u003e57\u003c/sup\u003e. It is a guiding principle of the project to align research activities with the local needs and to ensure local impact beyond the generation of novel scientific evidence through capacity building, collaboration, community involvement and health system strengthening activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior to study start, the intervention was piloted in ten villages in both study districts. Feedback from community members and CHWs have guided the design and implementation of the final trial intervention\u003csup\u003e58,59\u003c/sup\u003e. Local researchers have been involved throughout the research process and share authorship. The local and regional research relevant to the study have been included in citations.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eReporting\u003c/h2\u003e\n\u003cp\u003eFurther information on research design is available in the Nature Portfolio Reporting Summary in the supplementary material. This study was conducted and reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2025 guidelines\u003csup\u003e60\u003c/sup\u003e. The checklist is available in the supplementary material\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eA de-identified key dataset for reproducing the primary and secondary endpoints is available at https://doi.org/10.5281/zenodo.15674324. The trial was registered with ClinicalTrials.gov (NCT05684055), where a full protocol and statistical analysis plan are available. A study protocol manuscript has been published previously\u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;Requests for access to more detailed data may be made to the corresponding author by submitting a proposal, which will be reviewed by the trial consortium.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe code for the CDSS application used by CHWs for data collection and service delivery can be found at https://github.com/clinepi-usb/cht-combacal. Access to the test environment of the CDSS application is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge the SolidarMed team in Lesotho and Switzerland, the involved CHWs, and participants for their essential contributions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was part of the ComBaCaL project funded by the TRANSFORM grant of the Swiss Agency for Development and Cooperation (project number 7F-10345.01.01) and a grant by the World Diabetes Foundation (WDF-1778). FG\u0026rsquo;s salary was funded through a personal MD/PhD grant by the Swiss National Science Foundation (grant number 323530_207035). AA\u0026rsquo;s salary was funded through a grant of the Swiss National Science Foundation (Postdoc mobility #P500PM_221961). The funders had no role in the study design, data collection, analysis, data interpretation or writing of the publication.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eAA and NDL were the principal investigators, they acquired the funding, led the project, and conceptualized the study together with FG. FG drafted the manuscript, led the clinical development of the CDSS application together with TT, and the local implementation together with RG. TIL, MC, TK, MalebM, MalehM, MosM, MotM, ManM, MK, PMS, MB and RM were responsible for the local implementation through training and supervision of CHWs and local data monitoring. GSS was responsible for central data management, SM for the local data management. MadM and SP are responsible for the collaboration with the Lesotho Ministry of Health and gave input on the study design to ensure alignment with local guidelines and practices. DB and KK lead the technical development of the CDSS application. TB reviewed and approved the clinical algorithms. FC wrote the statistical analysis plan, conducted sample size calculations and all analyses. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurray, C. J. 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Empowering community health workers with mobile health: learnings from two projects on non-communicable disease care. (2023) doi:10.5167/UZH-233798.\u003c/li\u003e\n\u003cli\u003eHopewell, S. \u003cem\u003eet al.\u003c/em\u003e CONSORT 2025 statement: updated guideline for reporting randomized trials. \u003cem\u003eNat. Med.\u003c/em\u003e (2025) doi:10.1038/s41591-025-03635-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7022331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7022331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccess to hypertension care in low- and middle-income countries remains insufficient. Community Health Workers (CHWs) may improve access and outcomes of hypertension care, particularly in remote areas. However, the effectiveness of community-based hypertension care that includes independent drug prescription by lay CHWs is unknown. We conducted a 1:1 cluster-randomized trial nested within the Community Based chronic Care Lesotho (ComBaCaL) cohort study (NCT05596773) in rural Lesotho. Following community-based hypertension screening, 547 non-pregnant adults with blood pressure (BP) ≥140/90 mmHg were enrolled (75% female, median age 64 years, mean BP 149/95 mmHg). In intervention clusters, CHWs offered hypertension care, including independent prescription and titration of first-line antihypertensive medication (amlodipine/hydrochlorothiazide fixed-dose combination), guided by a mobile clinical decision support system (CDSS). In control clusters, participants were referred to the nearest health facility for standard of care. In the primary modified intention-to-treat analysis including 543 participants (four exclusions due to pregnancy), BP control rates after 12 months were 156/271 (58%) and 130/272 (48%) in the intervention and control arm; adjusted odds ratio (aOR) 1.52 (95%CI 1.01-2.29), p=0.046. The mean systolic BP was 131.1 mmHg (standard deviation 16.0) and 134.7 mmHg (17.6) in the intervention and control arm; adjusted mean difference -4.2 mmHg (95%CI -7.6; -0.8). In the intervention arm, 220/271 (81.2%) participants were engaged in care compared to 196/272 (72.1%) in the control arm; aOR 1.65 (95%CI 1.09; 2.51). In a predefined complete case analysis only including participants with available BP measurements, BP control rates at 12 months were 156/227 (69%) and 130/238 (55%) in the intervention and control arm; aOR 1.95 (1.23; 3.10). No relevant differences in safety outcomes were observed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHW-led hypertension care, including independent, CDSS-guided drug prescription is safe, improves engagement in care and BP control compared to facility-based care. Health policies should consider implementation of this model of care to address prevailing hypertension care gaps.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eclinicaltrials.gov registration: NCT05684055\u003c/p\u003e","manuscriptTitle":"Community Health Worker-led versus facility-based hypertension care for people with uncontrolled blood pressure in rural Lesotho: a cluster-randomized trial within the ComBaCaL cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 08:03:58","doi":"10.21203/rs.3.rs-7022331/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nm","sideBox":"Learn more about [Nature Medicine](http://www.nature.com/nm/)","snPcode":"","submissionUrl":"","title":"Nature Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ef18eee2-1ec6-48ed-bb70-f034d2dbc2e9","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51005779,"name":"Health sciences/Diseases/Cardiovascular diseases/Hypertension"},{"id":51005780,"name":"Health sciences/Health care/Health services"},{"id":51005781,"name":"Health sciences/Medical research/Outcomes research"},{"id":51005782,"name":"Health sciences/Health care/Health policy"}],"tags":[],"updatedAt":"2026-02-13T08:12:23+00:00","versionOfRecord":{"articleIdentity":"rs-7022331","link":"https://doi.org/10.1038/s41591-026-04208-w","journal":{"identity":"nature-medicine","isVorOnly":false,"title":"Nature Medicine"},"publishedOn":"2026-02-12 05:00:00","publishedOnDateReadable":"February 12th, 2026"},"versionCreatedAt":"2025-07-09 08:03:58","video":"","vorDoi":"10.1038/s41591-026-04208-w","vorDoiUrl":"https://doi.org/10.1038/s41591-026-04208-w","workflowStages":[]},"version":"v1","identity":"rs-7022331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7022331","identity":"rs-7022331","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}