Predicted 10-year cardiovascular disease risk and impact of community health worker interventions among middle-aged adults with high blood pressure in rural Morogoro, Tanzania

¹Department of Community Health, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania, ²Africa Academy for Public Health, Plot 82A Light Industrial Area, Mikocheni, Dar es Salaam, Tanzania, ³The Bernard Lown Scholars Program, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA, ⁴Department of Physiology, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania, ⁵Deloitte Consulting Limited, Aris House, Plot #152, Haile Selassie Road, Oysterbay, Dar es Salaam, Tanzania, ⁶Department of Development Studies, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania, ⁷Department of Behavioral Sciences, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania, ⁸Department of Epidemiology and Biostatistics, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania

^&Corresponding author
Alfa Muhihi, Department of Community Health, Muhimbili University of Health and Allied Sciences, United Nations Road, Upanga, Dar es Salaam, Tanzania

Introduction    

Cardiovascular diseases (CVDs), which refer to a group of disorders that affect the heart and blood vessels, are the leading causes of death worldwide and major contributors to poor quality of life [1]. In 2019, CVDs were responsible for approximately 18.6 million deaths globally [1]. This figure is anticipated to increase to 35.6 million by 2050 [2]. Low- and middle-income countries account for over three-quarters of these deaths [3]. Despite being largely preventable, the prevalence of CVDs and associated risk factors has continued to rise in most LMICs, mainly due to the ongoing epidemiological and nutrition transition, rapid urbanisation, and changing lifestyles [4,5]. In Tanzania, the prevalence of CVD risk factors, which used to be low in the 1990s, [6] has increased markedly, especially among young and middle-aged adults [7,8]. Findings from the nationally representative survey found that 25.9% of adults were hypertensive, and 26.0% were either overweight or obese (overweight 17.3%, obesity 8.7%) [9]. In Mafia Island, nearly half (49.5%) of the participants were reported as hypertensive [10] primarily due to sedentary lifestyles and high sodium intake from consuming highly salted fish.

The proportion of deaths attributable to CVDs has also increased over time. In 2019, one-third of all deaths in Tanzania were estimated to result from non-communicable diseases (NCDs) [11]. Furthermore, CVDs are the leading cause of NCD-related deaths, accounting for 40% of such deaths [12]. Between 2009 and 2019, deaths from stroke and heart attack increased by 23.7% and 37.9%, respectively, positioning them as the 4^th and 6^th leading causes of death in the country [13].

Although we cannot underestimate the usefulness of information on the prevalence of CVD risk factors, it does not provide sufficient insight into an individual's absolute risk of experiencing a cardiovascular event. Predicting an individual's risk of developing CVD is an important strategy for guiding clinical decisions regarding treatment and communicating personal health risks [14]. Availability of non-laboratory risk prediction tools [15]. Given their strong alignment with laboratory-based risk scores, they can help identify high-risk individuals for effective intervention. This is especially relevant in rural settings where access to laboratory testing and health services for CVD is limited [16]. In this study, we assessed the burden of CVD risk factors and the predicted 10-year CVD risk. We further assessed the effects of community health worker interventions on predicted 10-year CVD risk among middle-aged adults with high blood pressure in rural Tanzania.

Methods     

Study design: this study comprised two components: (i) a cross-sectional analysis of baseline data to estimate the predicted 10-year CVD risk, and (ii) a longitudinal analysis to assess changes in predicted 10-year CVD risk following a 12-month CHW intervention. Both analyses used data collected as part of the cluster-randomised controlled trial of CHW intervention aimed at reducing blood pressure. Writing of the manuscript followed STROBE guidelines for reporting cross-sectional studies [17], supplemented with the CONSORT guidelines for reporting randomised trials for the follow-up intervention component [18].

Study setting: the study was conducted in 12 villages in the Kilombero and Ulanga districts of Morogoro, Tanzania. Kilombero and Ulanga districts were selected purposively for the cluster randomised trial because the region had a relatively high number of trained CHWs and previous reports of high mortality rates from non-communicable diseases [19]. Data from the Health and Demographic Surveillance Systems (HDSS) reported an increase in NCD deaths from 16% to 24%, representing an 8% rise over five years [19]. Cerebrovascular diseases were the most common cause of NCD deaths in the population, accounting for 11% [19].

Participants and eligibility criteria: participants for the baseline survey included adults aged 25-64 from the study villages who provided written informed consent. We excluded individuals who were seriously ill or bedridden, had severe mental illness, or were unable or unwilling to sign informed consent. All participants who had high blood pressure during the baseline survey were enrolled in the cluster-randomised trial of CHW interventions. In this analysis, we included a sub-population of the study participants aged 40-64 years, enrolled in the cluster-randomised trial.

Sample size and sampling procedures: the sample size for the baseline cross-sectional survey was based on the WHO STEPwise approach to chronic disease surveillance [20]. We used a 95% confidence interval, a 5% margin of error, and a national prevalence of 25.9% for hypertension [21]. A design effect of 1.2 was applied to account for the cluster design, and a 10% loss to follow-up was also taken into account. The target population was stratified into four groups by sex, resulting in a final sample size of 3,145. All participants who had high blood pressure during the baseline survey were subsequently enrolled in the cluster-randomized controlled trial. A multistage sampling method was deployed using villages as the primary sampling units. Villages were stratified by district, and for each selected village, a total of 262 households were selected. At each household, the next birthday method was used to choose one eligible adult for an interview. A total of 3,000 (95.4%) participants were interviewed at baseline, of whom 1441 were aged 40-64 years. Baseline analysis included all 1441 participants, while analysis of the effects of CHW interventions included 542 who had high blood pressure and had both baseline and evaluation assessment data.

Baseline cross-sectional survey component: a baseline cross-sectional survey was carried out for two weeks, from 15^th July 2019 to 30^th July 2019, to gather information on sociodemographic profile and prevalence of CVD risk factors in the study population. Data collection was done through face-to-face interviews using a structured questionnaire, administered in Kiswahili by a separate team of trained research assistants. The research assistants were not involved in the subsequent delivery of the intervention. All baseline data were collected prior to the start of intervention activities.

Socio-demographic variables: sociodemographic variables collected and included in the analysis were age, sex, marital status, education level, and occupation. These variables were selected and included in the analysis as they have been used in previous population surveys in Tanzania [22].

Behavioral CVD risk factors: we collected information on behavioural CVD risk factors, including smoking, alcohol drinking, and dietary habits. The question on smoking assessed the current smoking status. Those who responded "yes" were coded as current smokers, and those who responded "no" were coded as non-smokers. Similarly, the question on alcohol assessed current alcohol drinking. Those who responded "yes" were coded as current drinkers, and those who responded "no" were coded as non-drinkers. Dietary assessment included the frequency of intake of fruits and vegetables per week. Self-reported use of table salt was categorised as always, sometimes, and never.

Blood pressure: we measured participants' blood pressure using a digital blood pressure machine (OMRON HEM-712C, Omron Healthcare, Inc.). Trained research assistants with experience conducting health and demographic surveys measured blood pressure on the non-dominant arm with participants seated. High BP measurements were confirmed on the following day. Participants with confirmed high BP were referred to a nearby health facility for further evaluation and management, in accordance with the 2018 Tanzanian standard treatment guidelines [23]. During analysis, we used an average of three blood pressure readings to define hypertension. For participants with repeat measurements, we used their confirmation blood pressure readings for analysis. We defined hypertension as an average systolic blood pressure (SBP) of ≥ 140mmHg and/or a diastolic blood pressure (DBP) of ≥ 90mmHg, and/or currently taking blood pressure-lowering medications [23].

Body weight and height: we measured body weight to the nearest 0.1kg using a SECA 803 digital flat scale (Hamburg, Germany), with the participant wearing light clothing and no shoes. We measured height to the nearest 0.1cm with the participant in a standing position, with heels perpendicular to the portable stadiometer. We then calculated body mass index (BMI) from the measured weight and height (kg/m²). We defined being overweight as BMI ≥25kg/m² but <30kg/m², and obesity as BMI ≥30kg/m².

Assessment of predicted 10-year CVD risk: we calculated the 10-year CVD risk using office-based Globorisk prediction charts specific to the Tanzanian population [24]. The office-based Globorisk scores are country-specific CVD risk prediction models that estimate the 10-year risk of the first fatal and non-fatal stroke and ischemic heart disease for a given individual, based on age, sex, and CVD risk factors, including systolic blood pressure, body mass index, and smoking status [24]. The tool was developed based on pooled data from multiple cohorts of adults aged 40-80 years. It demonstrated good statistical performance for both internal and external validations. The tool can be implemented in any country with access to basic epidemiological data. As a result, laboratory-based and office-based risk scores and charts are available for more than 182 countries, including Tanzania [25]. The office-based Globorisk score chart has seven categories: <5%, 5-9%, 10-19%, 20-29%, 30-39%, 40-49%, and ≥50%. In our analysis, we classified participants with predicted 10-year CVD risk score <10% as "low risk", 10-19% as "moderate risk", and ≥20% as "high risk" based on prior recategorization [26,27]. The Globorisk tool has previously been used in Tanzania and seven other countries in sub-Saharan Africa [26].

Cluster-randomised controlled trial component: a community-based, parallel-group, 1:1 cluster-randomised controlled trial with a pre- and post-intervention design was conducted to evaluate the effect of CHW-delivered home-based lifestyle intervention on CVD knowledge and CVD risk reduction.

CHW-delivered lifestyle intervention: trained CHWs conducted home visits to provide health education and a healthy lifestyle promotion intervention to participants with high blood pressure during the baseline survey. The intervention aimed to enhance participants' understanding of risk factors and warning signs for CVD while encouraging the adoption of healthier behaviours. The home visits took place monthly for the first six months, followed by bi-monthly visits through 12 months. During each visit, CHWs delivered health messages addressing the effects of CVD risk factors and promoting healthy behaviours, including maintaining a healthy body weight, quitting smoking, reducing excessive alcohol intake, lowering salt intake, increasing consumption of fruits and green leafy vegetables, using vegetable cooking oil, and consuming unrefined cereals. They also worked with participants to set realistic, individualised short-term behaviour change goals and monitored adherence to antihypertensive medication among those on treatment. CHWs used a structured checklist as a reference during the home visits.

Control group: participants in the control group received standard of care, including referral to a nearby health facility for further evaluation and management. They did not receive home-based health education, healthy lifestyle promotion, or blood pressure monitoring during the study.

Evaluation survey: An independent team of trained research assistants conducted an evaluation survey from 20^th July to 5^th September 2020 to assess changes in key outcomes following the CHW-delivered lifestyle intervention. The outcome variables assessed at baseline, including the prevalence of behavioural and physiological CVD risk factors, were reassessed during the evaluation survey. The effectiveness of the CHW intervention was measured through comparison of changes between the intervention and control groups. A flow diagram from baseline survey to evaluation survey is presented in Figure 1.

Statistical analysis: baseline data were presented using frequency distribution with relative frequencies (%) for categorical variables and median with interquartile range [IQR] for asymmetrically distributed continuous variables. Comparisons between groups were done using Pearson's chi-square test or Fisher's exact test as appropriate.To assess the effect of CHW interventions on predicted 10-year CVD risk, we applied the intention-to-treat principle, where all participants were analysed in the treatment group to which they were randomly assigned. We first calculated net differences in predicted 10-year CVD risk scores between baseline and 12 months separately for the intervention and control groups using paired-samples t-tests. We then compared net changes in predicted 10-year CVD risk scores between intervention and control groups using an independent-samples t-test. The effect of CHW interventions on predicted 10-year CVD risk score was assessed using the difference-in-differences (DID) method as follows:

EFFECT = [Yi (t=12) - Yi (t=0)] - [Yc (t=12) - Yc (t=0)]

Where, Yi (t=12) = Predicted 10-year CVD risk score in the intervention group at follow-up

Yi (t=0) = Predicted 10-year CVD risk score in the intervention group at baseline

Yc (t=12) = Predicted 10-year CVD risk score in the control group at follow-up

Yc (t=0) = Predicted 10-year CVD risk score in the control group at baseline

We conducted multi-level analysis using mixed-effects regression models to examine mean changes in predicted 10-year CVD risk score, accounting for inter- and intra-observer variation across groups over time (at baseline and follow-up). The model included a fixed effect for intervention. The model was adjusted for age, sex, education level, smoking status, use of raw table salt, baseline SBP, and baseline DBP. The intervention effect size was reported as mean differences with 95% confidence intervals. The analysis followed a complete case analysis, excluding participants with missing data on the key outcome variables. This approach was selected because the proportion of missing data was small and assumed to be missing completely at random. All statistical analyses were performed using the IBM Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA) Version 30 Software for Windows. A p-value of ≤0.05 was considered statistically significant.

Ethical considerations: the conduct of the study adhered to the ethical principles in the Declaration of Helsinki. Ethical approval was obtained from the Muhimbili University of Health and Allied Sciences (MUHAS-REC-1-2017-070). Participants were given information regarding objectives, procedures, importance, risks, and benefits, as well as the voluntary nature of participation and the freedom to withdraw. All participants signed an informed consent form before joining the study. Participants with identified risk factors received health education, and those with elevated blood pressure were referred to a local healthcare facility for further investigation, diagnosis, and management in accordance with national guidelines.

Results     

Socio-demographic characteristics: a total of 3,000 participants were surveyed at baseline, of whom 1,441 aged 40-64 were included in the analysis for baseline predicted 10-year CVD risk. Sociodemographic characteristics of the participants are summarised in Table 1. The median age of the study participants was 49 [interquartile range: 44-56] years. Most participants were women (70.9%), married (69.4%), in the 45-54 age group (43.6%), with primary education or less (96.0%), while 4.0% had secondary education or higher. Farming was the main income-generating activity, accounting for 94.4% of all participants.

Distribution of CVD risk factors: risk factors for CVD are also presented in Table 1. The prevalence of alcohol drinking was 26.2%, while that of current smokers was 8.2%. Both alcohol drinking and smoking were higher among men (p<0.001). The prevalence of hypertension was 43.2% and did not differ by sex (42.7% in men vs 43.4% in women). The prevalence of overweight and obesity was 46.1%, and it was significantly lower in men compared to women (29.8% in men vs 52.8% in women, p<0.001). Only 24 participants (1.7%) self-reported diagnosed diabetes mellitus. There were no differences in the prevalence of self-reported previous diagnosis of diabetes mellitus by gender.

Predicted 10-year CVD risk and associated factors: at baseline, the median (IQR) predicted 10-year CVD risk score in this rural population was 5% (3%-8%). Stratification of the predicted 10-year CVD risk is presented in Figure 2. Overall, the proportion of participants with a low predicted 10-year CVD risk was 93.6%. Participants at moderate predicted 10-year CVD risk were 5.8%, while only 0.6% were at high predicted 10-year CVD risk. We summarised the association between sociodemographic factors and predicted 10-year CVD risk in Table 2. The proportion of participants at high predicted 10-year CVD risk increased with age and was significantly higher among participants aged 55-64 years (p<0.001). All eight participants (100%) who had a high predicted 10-year CVD risk were aged 55-64 years. Most (85.5%) of the participants at moderate risk were also aged 55-64 years. We also observed differences in predicted risk by occupation, where retired individuals and those with no job had a higher predicted 10-year CVD risk (p = 0.017). However, we did not find a significant association between predicted 10-year CVD risk and sex, place of residence, marital status, or education level. Associations between modifiable risk factors and predicted 10-year CVD risk are summarised in Table 3. As expected, current smokers had higher predicted 10-year CVD risk compared to non-smokers (p<0.001). All participants who were at high predicted risk were smokers. Adding raw table salt at the time of eating was also associated with higher predicted 10-year risk of CVD (p = 0.027). Similarly, hypertensive individuals had higher predicted 10-year CVD risk when compared to normotensive individuals (14.3% vs 0.2%, p<0.001). We did not find overweight/obesity to be associated with higher predicted 10-year CVD risk.

Effect of CHW interventions on the 10-year CVD risk: of the 597 participants with high BP enrolled in the cluster randomised controlled trial, 542 (90.8%) had complete follow-up data. Table 4 summarises participant characteristics by trial arm. Characteristics of participants lost to follow-up in the intervention and control arms are summarised in Table 5. We assessed the effect of CHW intervention by comparing changes in predicted 10-year CVD risk among 542 participants with completed follow-up data. At baseline, the mean predicted 10-year CVD risk score was 5.63 (3.47) in the intervention group and 5.75 (4.36) in the control group. At follow-up, the mean predicted 10-year CVD risk score was 5.03 (3.17) in the intervention group and 5.45 (3.84) in the control group. The predicted 10-year CVD risk score decreased by 0.60 (2.22) in the intervention group and by 0.31 (2.41) in the control group. In the adjusted analysis, participants in the intervention group experienced a 0.26 greater reduction in predicted 10-year CVD risk score than in the control group [difference-in-differences -0.26 (95% CI; -0.62 to 0.10)] (Table 6). At baseline, 468 (86.3%) of the participants were predicted to be at low risk, 67 (12.4%) at moderate risk, and 7 (1.3%) at high 10-year CVD risk. At follow-up, the proportion at predicted low risk increased slightly to 87.6%, while that at moderate risk remained relatively unchanged, and no participant was at predicted high risk. However, we found no significant differences in predicted 10-year CVD risk between the study arms (Table 7). Lastly, we further assessed the direction of change in predicted 10-year CVD risk across study arms. A decrease in predicted risk was observed in 41.4% of the participants in the intervention group compared with 33.1% in the control group. The predicted risk remained unchanged in 39.8% and 44.8% of participants in the intervention and control groups, respectively. An increase in predicted 10-year CVD risk was noted in 18.8% of participants in the intervention group, compared with 22.1% in the control group (Figure 3).

Discussion     

We analysed and estimated the 10-year CVD risk among middle-aged adults in rural Tanzania using the Globorisk tool and further determined the effect of CHW-delivered interventions on the predicted 10-year CVD risk. The findings indicated that this population had low predicted 10-year CVD risk, as most participants (93.6%) were predicted to have less than a 10% chance of developing CVD in the next 10 years. Only 6.4% were classified as having a moderate to high risk of developing CVD. Older age, occupation, smoking, use of raw table salt, and hypertension were associated with increased individual risk of developing CVD in the next 10 years. Notably, at the 12-month follow-up, the CHW-home-delivered intervention did not alter the predicted 10-year risk of developing CVD.

The proportion of participants with predicted low risk in our study is comparable to that reported in Kenya, where 94.5% of slum dwellers were reported to have a low 10-year risk [28]. However, the proportion classified as high risk was nearly three times higher in the Kenyan study (1.7%) compared to that observed in our study (0.6%) [28]. In comparison to other rural community surveys, our study also showed a higher proportion of participants at low risk [29-31]. For instance, the proportion of individuals at low risk was 83.0% in rural South India [29], 86.4% in rural Nepal [30], and 81.3% in rural Bangladesh [31]. Additionally, the recent study of seven communities in East and West Africa (SevenCEWA), which also applied the same office-based Globorisk tool, found that 13.2% of participants had a high 10-year risk of developing CVD [26]. In contrast, only 8 (0.6%) of our study population fell in this high-risk category [32]. Most participants had a lower risk, and lifestyle modification strategies should be encouraged to help them maintain this lower risk.

The comparison of proportions of individuals classified as low, moderate, or high CVD risk across these studies should be interpreted with caution. While our study used the Globorisk prediction charts calibrated specifically for the Tanzanian population, several other comparison studies from Kenya, India, Nepal, and Bangladesh employed the WHO/ISH risk prediction charts. These methodological differences may account, in part, for the observed variations in risk proportion across studies, and direct comparisons of absolute risk categories across tools may not be valid. Future studies using harmonised risk prediction tools across settings will allow for more valid cross-country comparisons".

Despite the risk being generally low in our study population, older individuals (55-64 years) had a significantly higher predicted 10-year risk of developing CVD. This finding aligns with evidence showing that CVD risk increases with age [30,33,34]. Ageing increases the risk of CVD through ageing-induced structural and biochemical changes, gradual autonomic nervous system dysregulation, atherosclerosis, stroke, and epigenetic changes [35], primarily due to environmental factors, including stress, poor nutrition, and sedentary lifestyles. It is not surprising that the 10-year CVD risk is higher among older individuals because even the prevalence of CVD-related risk factors is generally higher for older individuals compared to younger individuals [36].

Smoking was also associated with predicted 10-year CVD risk. Smoking causes endothelial wall damage through oxidative stress, atherosclerosis, enhanced platelet activation, and impaired fibrinolytic activity, which collectively promote a thrombogenic state and consequently the development of CVD [37,38]. The 10-year risk associated with smoking observed in our study is substantially higher than that observed in the Canadian population, where smokers had a 1.5 times greater risk for CVD than non-smokers [39]. Type of cigarettes, smoking intensity, and racial differences can partly explain the observed higher risk among smokers in our study compared to that reported in Canada [40]. Our findings suggest that interventions targeting smoking cessation may yield a greater public health impact in reducing the subsequent risk of CVD [41].

Another significant risk factor was the addition of raw table salt to food. Excessive salt intake increases plasma volume and blood pressure [42]. Our previous research already showed that the use of raw table salt is associated with more than twice the odds of being hypertensive [43], which in turn is associated with an increased risk of a CVD event [44]. Given that 43.2% of our study participants were hypertensive, and 14.3% of them had elevated 10-year CVD risk, targeted raw salt reduction interventions are warranted to decrease the risk of hypertension. Control of hypertension, either through lifestyle modification or pharmacological therapy, should be central to the management of hypertension to reduce CVD risk in this rural population. WHO guidelines for the assessment and management of CVDs already recommend initiating appropriate drug therapy for individuals with blood pressure ≥160/100mmHg, regardless of their 10-year CVD risk level [45].

Interestingly, we did not find overweight and obesity to be associated with increased risk for CVD. Given our population comprised of middle-aged adults, it is possible that those with overweight and obesity had not yet developed metabolic complications detectable by the model. However, other studies, including one from Nigeria, found increased CVD risk among overweight and obese individuals [46]. Obesity is strongly linked to hypertension, diabetes, and dyslipidemia [47]. Nearly half of the participants (46.1%) were either overweight or obese, indicating a gradual increase in overweight and obese individuals in rural populations. The increasing prevalence of overweight and obesity is partly driven by reduced physical activity [48], especially with the widespread use of motorcycle transport (boda-boda) [49], which has replaced walking and cycling as primary modes of transport.

At the end of the 12-month follow-up, none of the participants was at high risk for CVD. However, we did not observe significant differences between the intervention and control groups. This means the CHW intervention had no significant impact on the predicted 10-year CVD risk. This finding may be attributable to the relatively short duration of follow-up, as a meaningful reduction in CVD risk often requires sustained positive behaviour change over a longer period. It is also important to note that this rural population was generally a low-risk population at baseline, which may have limited the potential impact of CHW interventions to produce an appreciable reduction in 10-year CVD risk during the study period.

Our analysis has strengths and limitations that should be considered when interpreting these findings. The main strength lies in the utilisation of Globorisk prediction scores specifically tailored for the Tanzanian population, thereby enhancing the accuracy of CVD risk prediction and contextual relevance of the predicted 10-year CVD risk estimates. However, CVD risk factors such as diabetes mellitus were self-reported and may have underestimated their true prevalence in this population. The observed association of age, smoking, and hypertension with predicted 10-year CVD risk reflects the score construction rather than independent epidemiological associations. Our comparisons with prior studies that used different CVD risk prediction charts such as WHO/ISH should be interpreted cautiously, as tool-specific differences in variables and calibrations may influence the classification of risk categories independently of the true underlying population risk. Our study was cross-sectional, which does not permit causal associations between socio-demographic, behavioural, and physiological variables and predicted 10-year CVD risk. Other limitations include the study population, which was predominantly female (70.9%), thus limiting generalizability of the findings to the broader rural Tanzanian population. The original sample size was calculated for a cluster-randomised trial of CHW interventions for the reduction of blood pressure, and our analysis may be underpowered as we used a sub-population of participants aged 40-64 years.

Conclusion     

The predicted 10-year CVD risk in this rural population was generally low, with only a small proportion identified as being at moderate and high risk. However, these findings should be interpreted with caution given the observed high prevalence of modifiable CVD risk factors in this population. Our study did not demonstrate a significant effect of CHW intervention on predicted 10-year CVD risk, possibly because of short follow-up time. The potential role of CHW approaches, including CHW-delivered interventions for reducing CVD risk, warrants further rigorous research with extended follow-up. Nonetheless, CHWs remain a plausible platform for delivery of structured CVD screening interventions using non-invasive tools such as Globorisk for risk stratification and identifying individuals for early intervention.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Conception and study design: Alfa Muhihi, Marina Njelekela, and David Urassa. Data collection: Alfa Muhihi and Amani Anaeli. Data analysis and interpretation: Alfa Muhihi, Marina Njelekela, Amani Anaeli, Rose Mpembeni. Initial manuscript draft: Alfa Muhihi. Critical review and editing: Marina Njelekela, Amani Anaeli, Bruno Sunguya, Deodatus Kakoko, Anna Tengia Kessy, and David Urassa. All authors read and approved the final version of the manuscript.

Acknowledgments     

The authors greatly appreciate the research assistants who collected the data, as well as the local authorities for their support, which ensured smooth data collection. The authors also thank the participants for their time and significant contributions to these findings.

Tables and figures     

Table 1: general characteristics of the study participants
Table 2: associations between socio-demographic variables and predicted 10-year CVD risk among young and middle-aged adults in rural Morogoro, Tanzania
Table 3: associations between modifiable risk factors and predicted 10-year CVD Risk among young and middle-aged adults in rural Morogoro, Tanzania
Table 4: general characteristics of young and middle-aged participants enrolled in the cluster-randomised controlled trial
Table 5: general characteristics of the participants in the cluster-randomised trial lost to follow-up
Table 6: effect of CHW interventions on predicted 10-year CVD risk among middle-aged adults with high blood pressure in rural Morogoro, Tanzania
Table 7: changes in proportions of predicted 10-year CVD risk among middle-aged adults with high blood pressure in rural Morogoro, Tanzania
Figure 1: CONSORT flow diagram of trial participants in the subgroup analysis
Figure 2: baseline predicted 10-Year CVD risk among young and middle-aged adults in rural Morogoro, Tanzania
Figure 3: direction of change in predicted 10-year CVD risk among young and middle-aged participants with high blood pressure

References