Effectiveness of a Primary‐Care‐Centered Integrated Care Intervention for Stratified Cardiovascular Risk Control in China: Study Protocol for a Pragmatic Cluster Randomized Controlled Trial

ABSTRACT

Introduction

Gaps in primary care (PC) restrict its potential as a core foundation of the health system in its response to the growing burden of cardiovascular disease (CVD). China has been promoting County‐wide Tight Medical Alliances to strengthen PC by integrating services from hospitals and PC providers. However, a practical model of care for effective CVD risk management is still lacking. We developed a multifaceted intervention, namely the Cardiovascular‐Risk‐Stratified Integrated Management model, to strengthen PC for CVD risk management by leveraging both PC providers and hospital‐based specialists in the County‐wide Tight Medical Alliances. The purpose of this study is to evaluate the effectiveness of the Cardiovascular‐Risk‐Stratified Integrated Management model.

Methods

This study is a multicenter, cluster‐randomized controlled trial conducted with a total of 2560 patients with diagnosed hypertension and/or diabetes, aged 41–70 years, served by 64 family doctor teams in three county‐wide tight medical alliances. The primary outcome is the change in 10‐year CVD risk score from baseline to 12 months after intervention, using the World Health Organization CVD risk laboratory‐based chart for East Asians. Secondary outcomes include 12‐month and 6‐month changes in CVD risk factors, and incidence rate of major adverse cardiovascular events, and so forth. All participants will receive outcome measurements at baseline, 6, and 12 months.

Discussion

This study pioneers a PC‐based integration of individualized CVD risk management with economic incentives, evaluating its effects on resource allocation and preventive outcomes to inform scalable models in low‐ and middle‐income countries. The study findings will be disseminated through multiple channels, including a final report, publications in peer‐reviewed journals, and evidence‐based recommendations tailored to healthcare providers, health service commissioners, and policymakers.

Trial Registration

Clinical Trials ID: NCT06302127. Date of registration: March 8, 2025. Prospectively registered. URL of Trial Registry Record: https://clinicaltrials.gov/study/NCT06302127.

This study developed a multifaceted intervention to strengthen primary care for cardiovascular risk management by leveraging both primary care and county hospitals in rural China. The purpose of this multicentre, cluster‐randomized controlled trial is to evaluate the effectiveness of intervention on the prevention of cardiovascular risks among patients with diabetes/hypertension.

Abbreviations

CNY

Chinese Yuan

CTMA

County‐wide Tight Medical Alliance

CVD

cardiovascular disease

C‐RISIMA

Cardiovascular‐Risk‐Stratified Integrated Management

FDSC

family doctor service contract

FDT

family doctor team

GP

general practitioner

LMIC

low‐ and middle‐income country

PC

primary care

SBP

systolic blood pressure

THC

township healthcare center

USD

United States Dollar

VC

village clinic

WHO

World Health Organization

1. Background

Cardiovascular diseases (CVDs) are the leading cause of death globally, with approximately 80% of cases occurring in low‐ and middle‐income countries (LMICs), where the burden continues to grow rapidly [1]. In China, CVDs accounted for over 45% of total deaths in 2020, with hospitalization costs reaching 270.9 billion Chinese Yuan (CNY) (39.26 billion United States Dollar [USD]) [2]. Given this growing dual burden of disease and expenditure, the need for effective primary prevention strategies for CVDs in China and other LMICs is more urgent than ever.

Primary care (PC) is vital to ensure that individuals with chronic conditions receive appropriate patient‐centered care, which may prevent severe CVDs [3]. Strengthening PC has been associated with both lowered health expenditure and improved population health [4]. However, in China and other LMICs, efforts to strengthen PC often face significant challenges such as low quality of services, fragmented health systems, shortfalls in human resources, and insufficient and inappropriate financial incentives [5, 6]. These challenges highlight the need to enhance the capacity of PC with the promotion of integrated care, and provide properly directed economic incentives.

In 2019, the Chinese government launched the Healthy China Action Plan, prioritizing the prevention and treatment of CVDs as a national health goal and emphasizing the co‐management of hypertension, hyperglycemia, and hyperlipidemia—the “three highs” [7]. Despite nationwide efforts, the awareness rate, treatment rate, and control rate of hypertension, diabetes, and hyperlipidemia in China have risen but remain undesirably low [8, 9, 10]. Therefore, more effective interventions are needed to manage CVD risk factors and reduce the incidence of CVDs.

Cardiovascular events are typically driven by the interplay of multiple, coexisting risk factors [11]. CVD risk scores serve as a comprehensive tool that integrates information from various CVD risk factors, allowing for quantitative estimation of an individual's total risk or likelihood of experiencing a cardiovascular event within a specific timeframe [12]. Through CVD risk assessment, it becomes possible to identify individuals who are most likely to benefit from timely and appropriate management of risk factors. Based on the assessment results, stratifying individuals into risk groups with similar healthcare needs enables the implementation of targeted management strategies for each risk stratum group. This risk‐stratified approach offers a cost‐effective solution that is more focused than population health management while remaining less expensive than individualized case management [13].

Although risk stratification is frequently included in chronic disease management frameworks, most existing studies focus solely on population risk assessment without developing risk‐based interventions [12]. Furthermore, among studies that do implement such interventions, most target only high‐risk individuals, without evaluating effects across the full spectrum of risk levels. A few studies have applied stratified interventions but failed to incorporate timely adjustments when patients' risk level changed, thus overlooking the dynamic risk control needs of patients [14]. Meanwhile, most existing research on economic incentive reforms focuses primarily on single service levels, rarely addressing interventions that span both primary healthcare institutions and hospitals. Additionally, the effectiveness of economic incentives in promoting service integration and team collaboration remains poorly understood [15].

To address these gaps, we developed the Cardiovascular‐Risk‐Stratified Integrated Management (C‐RISIMA) model, which incorporates multidisciplinary, primary‐care‐centered service delivery with a blended payment approach. This study aims to evaluate the effectiveness of the C‐RISIMA model in a cluster randomized controlled trial. The C‐RISIMA model will be built in the context of the County‐wide Tight Medical Alliance (CTMA) in China, implemented by family doctor teams (FDTs) on patients with diabetes and/or hypertension, including CVD risk assessment, treatment, and management.

2. Methods

2.1. Setting

The study is being conducted in two counties—County L and County S—in rural China. County L is located in the Guangxi Zhuang Ethnic Autonomous Region in southwest China. In 2022, County L had a total of 411,400 residents with a gross domestic product per capita of 32697 CNY (4587.8 USD based on 2022 exchange rate), falling in the income range of upper‐middle income level by 2022 World Bank categorization of levels of economic development [16]. County S is located in Fujian Province in southeast China. In 2022, County S had about 249,000 residents with an annual disposable income of 38126 CNY (5665.1 USD based on 2022 exchange rate), also falling in the income range of upper‐middle income level.

2.2. CTMA

The model of CTMA has been vigorously promoted by the Chinese government since 2016 as the primary approach to establishing people‐centered integrated care [17]. A CTMA is typically led by a county‐level hospital, and includes all township healthcare centers (THCs) and village clinics in their catchment areas [18]. The leading hospital is responsible for the administrative management and business operation of the personnel, finance, and health resources of the THCs. Larger counties with around 400,000 residents usually establish multiple CTMAs, as was the case in County L, where two CTMAs were established. Smaller counties may have just one CTMA, as was the case in County S (Table 1 contains more details about the CTMAs where this trial is conducted).

Table 1.

Characteristics of the study setting at baseline.

Characteristics	County L‐CTMA 1	County L‐CTMA 2	County S‐CTMA 3
Characteristics of CTMAs
Institution involved
Hospitals	1	1	1
THC	5	5	12
Village clinics	56	70	163
Presence of an interoperable health information system across facilities within CTMA	No	No	Yes
Financing
Inpatient	Diagnosis‐related groups	Diagnosis‐related groups	Diagnosis‐related groups
Outpatient	Fee for service	Fee for service	Capitation + fee for service
Human Resources
Number of cardiologists per 10,000 population	0.41	0.43	0.42
Number of neurologists per 10,000 population	0.36	0.48	0.33
Number of GPs per 10,000 population	3.63	3.51	4.72
Number of village doctors per 10,000 population	4.58	4.49	3.44
Characteristics of FDTs
Number of FDT	58	72	85
Health professionals involved
Specialists	No	No	Yes (loosely joined the team)
GP	Yes	Yes	Yes
Village doctors	Yes	Yes	Yes
Management compliance rate of patients with diabetes (%)a	78.50	76.08	88.57
Management compliance rate of patients with hypertension (%)a	84.12	85.54	89.12
Financing for chronic service	Fee for service	Fee for service	Pay for performance + fee for service

Abbreviations: CTMA, County‐wide Tight Medical Alliance; FDT, family doctor team; GP, general practitioner; THC, township healthcare center.

^a The management compliance rate refers to the proportion of patients with hypertension and diabetes who received at least four follow‐up visits and one physical examination within a year.

2.3. FDT

China has been promoting “contracted services provided by family doctors” nationwide since 2016 [19]. Although there are no fixed rules for the composition of an FDT, it generally consists of a general practitioner (GP) based at THC, a public health practitioner based at THC, and a village doctor. Typically, a village doctor covers a village. In case of small villages in sparsely populated mountainous areas, a village doctor may serve more than one village. Village doctors typically do not hold university medical degrees and are not classified as fully licensed physicians under the national health system. Instead, they would be certified with a special license for village doctors and play a key role in delivering PC services at the village level.

FDTs intend to bring about a continuous relationship of accountability between these staff and the residents who sign the contract with these staff, and to integrate or coordinate a range of preventive and curative services previously provided separately by FDT members. The government made the signing of a family doctor service contract (FDSC) with key groups (people confirmed with hypertension and/or diabetes, the elderly, and people with low income) a key performance indicator for PC providers. As a result, most individuals in these key groups have been formally enrolled in an FDSC with their local FDTs. However, many may be unaware of their contracted team, as the FDSC is often nominal, with minimal additional services provided that would be noticeable to patients. Under CTMA, FDTs are managed by THCs, with or without a centralized administrative structure from the CTMA for the PC services. All three CTMAs initiated the FDT policy in 2016 (Table 1). However, the CTMA in County S operates in a more centralized manner, with FDTs receiving a substantial portion of their income based on performance evaluations conducted by the CTMA.

2.4. Study Design

The C‐RISIMA trial is a multi‐center, two‐arm cluster randomized controlled trial in which residents in the catchment of 64 FDTs (i.e., villages) among 3 CTMAs in 2 counties have been randomized to receive usual health services from FDT or integrated health management based on CVD risk score stratification supported by enhanced FDTs for 12 months (Figure 1). An enhanced FDT consists of health professionals at the village, township, and county levels, who directly implement the C‐RISIMA intervention measures to the participants. This protocol is registered on www.clinicaltrials.gov (identifier NCT06302127).

Figure 1.

Flow chart of study design. CTMA, County‐wide Tight Medical Alliance; C‐RISIMA, Cardiovascular‐Risk‐Stratified Integrated Management; FDT, family doctor team.

2.5. Study Population

This trial adopted a multistage random sampling design including teams and individuals. Sampling was implemented at the team level, with teams that managed fewer than 40 patients with hypertension and/or diabetes being excluded from the trial. 40 FDTs and 24 FDTs were selected from the two groups of teams in County L and County S, respectively. Teams were sampled to avoid discrepancies in population size (County L 411,400 and County S 249,000) and to ensure the feasibility of distribution within the county jurisdiction. Since there are two CTMAs in County L, a total of 20 FDTs were selected from CTMA‐1, 20 from CTMA‐2 in County L, and 24 from CTMA‐3 in County S. In each team, 40 patients were identified for participation in the trial. If there were more than 40 eligible patients, a random sample of 40 of these patients would be selected. This trial has recruited 64 FDTs (i.e., clusters), each with 40 participants, for a total of 2,560 participants.

At each FDT, a list of potentially eligible participants was generated from the local health records, and selected village doctors were asked to contact participants with hypertension and/or diabetes after receiving training on trial objectives, procedures, and the importance of maintaining confidentiality. We will use the following inclusion and exclusion criteria in recruiting participants.

Inclusion criteria: participants must be aged between 41 and 70 years, diagnosed with hypertension and/or diabetes at the county‐level hospital, permanent residents of the study area, and have signed up with an FDT in the study area.

Exclusion criteria: those who are unable to independently complete the intervention content involved in the study, are unable to cooperate with the visit, or are suffering from cancer that may affect the study visit or intervention, pregnant or lactating women, or those deemed otherwise unsuitable to participate in the study.

2.6. Informed Consent

All participants were asked to sign an informed consent form before completing a questionnaire and CVD risk assessment to determine study eligibility. Participants who wish to participate in the trial must sign an informed consent form. An informed enrolment consent form was given by the primary healthcare professionals who fully informed the participants, both verbally and in writing, about the intervention protocol of the study, the rights and interests of the subjects, the risks, and benefits of the trial, and gave the subjects sufficient time and opportunity to understand the details of the study.

2.7. Randomization and Masking

Following screening and complete participant enrolment within a given FDT, the 24 FDTs in County S CTMA and the 40 FDTs from the other two CTMAs in County L were randomized, respectively, on a 1:1 allocation ratio to either intervention or usual care. A computer‐generated, central randomization system was run by the trial statistician, who was masked to team identifiers. Randomization of each team took place after all patients of eligibility were screened to avoid selection bias. All participants within the defined team will receive the allocated treatment. Patients will be informed about their allocation through a message sent by the research team. Due to the nature of the intervention, team health professionals and participants will be aware of their treatment allocation.

Within the context of the intervention, it is not feasible to blind FDT members and participants involved in the execution of the trial. Nevertheless, data collection and analysis will be conducted by the trial researchers masked to intervention allocation. To further minimize potential bias, objective outcome measures derived from routine health records were used, standardized protocols and training were implemented to ensure consistency in intervention delivery, and analyses were conducted using an intention‐to‐treat approach with mixed‐effects models to account for clustering and baseline differences.

2.8. Intervention

2.8.1. Development

A package of multifaceted interventions was developed to address the problems identified in earlier qualitative and quantitative research on the problems experienced by healthcare professionals and patients regarding CVD management, along with the findings from the in‐depth mixed‐methods situation analysis of the CTMAs in the study sites [20, 21, 22, 23]. Following a series of workshops with healthcare professionals, patients, health officials, and experts from various research fields, including clinical, health education, and trial design, the study has developed a complex intervention with multiple components involving different levels of healthcare institutions. Two FDTs, randomly selected from different counties, participated in an external feasibility study to assess and refine the intervention. These teams were excluded from the main trial to avoid potential bias. The views of patients, healthcare professionals, and health officials were fed back to the research team.

Substantial gaps in the workforce, service delivery, and financing for CVD prevention occur globally. There are three layers of barriers to CVD prevention: health systems, health care providers, and patients. Guided by the Capability, Opportunity, Motivation‐Behavior framework, we systematically summarized barriers reported from the layer of healthcare providers in terms of capability, opportunity, motivation, and behaviors [24]. In addition, at the health system layer, we utilized the analytical framework of health system building blocks for a comprehensive analysis of barriers to CVD risk management. Strategies were identified to address each of these barriers at different layers, as shown in Table 2. In order to address practical level challenges identified in light of the theoretical framework, we have proposed a new team‐based care model involving specified, aligned roles of specialists and PC practitioners with new guidelines and reimbursement to address provider level challenges related to capability, opportunity and motivation. We have also developed strategies to address the system‐level barriers to facilitate the implementation of the integrated management. Based on the identified barriers and corresponding strategies across multiple levels, an integrated care approach emerges as a key solution due to its potential to align specialist and PC services and to enhance patient‐centered CVD risk management (e.g., care pathway, performance‐based incentives and enhanced information platform).

Table 2.

Barriers to effective CVD management and identified strategies.

Type of barrier	Example of the barrier	Strategies
Patient
Availability, access, and costs of medication and healthcare	High cost of specialized care	Involvement of specialists in daily management
Self‐management knowledge and ability	Low knowledge of how to self‐manage	Strengthened health education program
Healthcare provider
Capability	Lack of knowledge and skills in CVD primary and secondary prevention	Involvement of specialists in daily management
		Simplified management algorithm using evidence‐based recommendations
Opportunity	Lack of guidelines	Simplified management algorithm using evidence‐based recommendations
Motivation	Lack of motivation	Reimbursement for CVD management
Health system
Health care financing	Low priority for prevention service and integrated care	Financial incentives for the integration of care
Health workforce	A shortage of adequately trained GP and healthcare professionals constrains effective patient management	Risk‐stratified care pathway
		Task‐shifting responsibilities to non‐physician health workers—village doctors
Health information	Limited information system to ensure monitoring of health status and performance	Enhanced information system for risk‐related data management
Leadership and governance	Low priority for CVD prevention	CVD risk screening during each home visit
	Lack of a screening program
Medical products, vaccines, and technologies	The regulation limited the availability of essential drugs at the PC facilities	Medication delivery at the village level
Service delivery	Lack of health education service	Team‐based care
	Poor coordination of care	Health education program co‐provided by the GP and village doctors

Abbreviations: CVD, cardiovascular disease; GP, general practitioner; PC, primary care.

2.8.2. Intervention Components

The C‐RISIMA model consists of two core elements provided as an intervention package: a risk‐stratified integrated care pathway and a team‐based blended payment mechanism.

2.8.2.1. Risk‐Stratified Integrated Care Pathway

The C‐RISIMA model is provided by an enhanced family healthcare team comprising one village doctor, one GP at township health centers, and one specialist (a cardiologist, neurologist, or endocrinologist from a local county‐level hospital), as shown in Figure 2. For villages without village doctors, public health professionals or nurses from township health centers would assume their role in the team. Within the team, three team members carry out the different functions. The village doctor is responsible for regular home visits, monitoring blood pressure, and organizing health education courses. The GP, as the core service provider in the team, is responsible for CVD risk monitoring, providing health education courses for medium‐ and high‐risk participants, and generating integrated care plans for each participant. Specialists are responsible for providing guidance, particularly for high‐risk population management, to township and village doctors within the team. Specialists from the county hospital integrated into the FDT are expected to provide regular clinical guidance to the PC team through monthly case discussions to improve patient care. With this professional support, GP and village doctors may gain greater confidence and competence in adjusting medication plans, particularly for patients with complex conditions [25]. All participants with emergency CVD‐related events that cannot be solved at the village and township level will be transferred to the county level. A regular team meeting is held for overall risk monitoring and case discussion. The frequency of meetings is changed from twice a month in the first quarter to once a month from the second quarter to the end of the study. In other words, by incorporating the specialist and forming continuous relationship between township‐level staff (i.e., GPs) and the village doctors and between such FDT and patients with specified roles for each actor, the intervention establishes an integrated team‐based model of care.

Figure 2.

Levels of integration within the CTMA. CTMA, County‐wide Tight Medical Alliance; FDT, family doctor team; GP, general practitioner.

Using the updated World Health Organization (WHO) CVD risk laboratory‐based chart for East Asians [26], the baseline population will be divided into three groups: low risk (10‐year CVD risk: < 10%), moderate risk (10‐year CVD risk: 10%–20%), and high risk (10‐year risk: > 20%), based on their age, sex, blood pressure, smoking status, total cholesterol and presence or absence of diabetes. With support from clinical experts and health care professionals, the study developed a risk‐stratified integrated care pathway based on clinical guidance. According to the pathway, participants at different risk levels are provided with differentiated management plans, especially in terms of health education and follow‐up frequency.

2.8.2.1.1. Health Education

Health education will be delivered as monthly courses over 6 months. Our study was designed with eight health education modules based on the risk factors and management guidelines for CVDs by GPs and village doctors (Table 3 contains details about the topics of each module). Village doctors will select six relevant modules for educational courses tailored to the patients' specific management needs. High‐risk participants are strongly encouraged to take the education course, whereas participation for middle‐risk participants is entirely voluntary. Alternative one‐on‐one health counselling will be provided for high‐risk individuals who do not attend. All participants would receive educational messages twice a month, which introduce tips about the management of hypertension, diabetes, and CVD risk factors.

Table 3.

Topics of health education sessions.

Module Number	Topics
1	Medication adherence
2	Balanced diet
3	Exercise
4	Weight control
5	Stress management
6	Sodium intake control
7	Self‐management of hypertension
8	Self‐management of diabetes

2.8.2.1.2. CVD Risk‐Based Follow‐Up

CVD risk‐based follow‐up will be conducted by village doctors, who will not only collect individual data on blood glucose, blood pressure, height, weight, and waist measurement for regular CVD risk evaluation and communicate risk information with patients, but also provide personalized health education advice based on the results of lifestyle counselling. Risk assessment will be repeated at each follow‐up visit using a standardized risk calculation approach based on the WHO CVD risk laboratory‐based model. Village doctors are primarily responsible for risk‐related data collection and entry supported by GPs where needed. Changes in risk category are determined by updated risk scores crossing predefined thresholds for low, moderate, and high risk, which subsequently trigger adjustments in follow‐up frequency and management intensity.

Participants in the low, moderate, and high‐risk groups will be visited separately semi‐annually, quarterly, and monthly after baseline for regulating the intervention instantaneously. The intervention visit schedule for each participant in the intervention groups will be dynamically updated based on their most recent risk assessment (Figure 3). CVD risk data is shared within the team, and updates of personal risk reported by village doctors will be known automatically by GPs and specialists.

Figure 3.

Follow‐up design. CVD, cardiovascular disease; ISH, International Society of Hypertension; LDL, low‐density lipoprotein; WHO, World Health Organization.

To ensure feasibility across different settings, two implementation pathways are applied. In CTMAs with established health information systems, village doctors enter updated patient data into the system after each visit, and risk scores are automatically calculated and shared in real time with GPs and specialists. In settings without interoperable systems, standardized paper‐based or offline calculation tools are used, and updated risk information is shared within the team during routine communication or case discussions. In addition, a dynamic patient risk monitoring information system is established to support FDTs in CVD risk calculation and data sharing where available. The system automatically generates risk scores and classifies patients into risk categories, facilitating appropriate care pathways. Updated risk information is accessible to GPs and specialists simultaneously. Policymakers may also use aggregated risk data as indicators for FDT performance evaluation.

To ensure consistency, all participating teams receive standardized training on risk assessment procedures, use of calculation tools, and criteria for risk classification. This approach enables consistent implementation of dynamic risk‐based follow‐up across sites with varying levels of digital infrastructure.

2.8.2.1.3. Monthly Case Discussion Meetings

To strengthen the management of high‐risk patients and promote multidisciplinary collaboration, a structured case discussion mechanism was established within the intervention teams. Case discussion meetings were organized at the village level and involved all members of the integrated care team, including village doctors, GPs, public health physicians, and specialists. This mechanism was designed to facilitate continuous communication between PC providers and county‐level specialists and to enhance the comprehensive management of complex cases.

During the first 3 months of implementation, meetings were held twice monthly, and thereafter once monthly, with each session lasting approximately 2 h. Meetings were coordinated by public health physicians from township health centers. Prior to each meeting, cases were selected based on predefined criteria, including changes in risk level, persistently high‐risk status, poor control of key risk factors (e.g., blood pressure or glucose), or concerns regarding treatment adherence or complications. Relevant clinical and risk assessment data were compiled in advance.

Each meeting followed a standardized process, including: (1) review of previously discussed cases and implementation of prior management plans; (2) presentation of population‐level risk profiles and control of key indicators within the team; (3) multidisciplinary discussion of selected high‐risk cases, focusing on treatment optimization, referral needs, and follow‐up strategies; and (4) documentation of agreed management plans and assignment of responsibilities.

During case discussions, team members contributed according to their roles. Public health physicians presented patient risk profiles and management status; GPs and specialists reviewed treatment strategies and clinical decision‐making, including medication adjustment and referral; and village doctors provided information on follow‐up implementation and patient adherence. Through this structured and regular process, individualized management plans were developed, and responsibilities for subsequent care were clearly defined.

2.8.2.2. Team‐Based Blended Payment Mechanism

Financial incentives for participating healthcare practitioners were designed as a combination of pay‐for‐coordination and pay‐for‐performance. Moreover, the incentives will be provided in the form of additional resources (top‐up) to the CTMAs for practitioners in the intervention group. Practitioners would receive payment for providing coordinated services, including consultation meetings and risk monitoring. Within the FDT, remuneration for consultation meetings will be provided to specialists for advice on moderate‐ and high‐risk cases and high‐risk population management, to GPs and village doctors for organization and discussion of case preparation. GPs would be remunerated for CVD‐risk monitoring and CVD‐risk communication. As for village doctors, reimbursement would be provided for conducting education programs and risk measurement.

Apart from the incentives for coordinating services, FDT will also receive financial incentives that are calculated based on service quality and CVD‐related health outcomes at the population level over the study period. Pay for performance incentives will be calculated on a semi‐annual basis according to a set of indicators co‐developed by local health officials and the research team (Table 4). The indicators include compliance with the management pathway, the self‐care ability of patients, control of CVD risk, and related clinical outcomes. Each indicator is weighted according to its importance.

Table 4.

Indicators for calculating incentives.

Domain	Indicator	Subject	Weight (%)	Description
Process stage	High‐risk case discussion rate	Specialist	10	Full mark: 100%
	Completion rate of CVD‐risk follow‐up defined by the pathway	GP and village doctor	10	60%–90%
	Completion rate of the health education course defined by the pathway	Village doctor	10	50%–85%
	CVD‐risk file completion rate	FDT	10	0%–< 85%
	Compliance rate of C‐RISIMA pathway	FDT	10
Outcome stage	Medication adherence rate	FDT	10	Full mark: higher than twice the baseline level
	CVD‐risk control rate	FDT	10	50: higher than the baseline level but lower than twice the baseline level
				20: equal to the baseline level before implementation
				0: lower than the baseline level
	Incidence of CVD	FDT	10	Full mark: lower than the half level of the same period before implementation
	Rate of hospitalization due to CVDs	FDT	10
	Rate of hospitalization due to diabetes or hypertension	FDT	10	50: between the level of the same period before implementation and half of that level
				20: equal to the level of the same period before implementation
				0: higher than the level of the same period before implementation

Note: This table was developed by the authors.

Abbreviations: CVD, cardiovascular disease; C‐RISIMA, Cardiovascular‐Risk‐Stratified Integrated Management; FDT, family doctor team; GP, general practitioner.

2.8.3. Control

No structured interventions will be used in the control arm. During the study, control participants will be provided with existing essential public health services, including quarterly follow‐ups and annual physical examinations by village doctors, and be advised to see their usual provider for care, as appropriate. The treatments prescribed are recorded for each participant. Participants in the control group will not have access to any element of the intervention. Thus, we can eliminate or minimize contamination through our cluster randomization design as opposed to individual randomization.

2.8.4. Study Visits and Data Collection

Study visit and data collection will be conducted by trial researchers masked to intervention allocation. At pre‐randomization group screening, trial researchers will assess each consenting participant and collect physical measurements to determine study eligibility. After the follow‐up intervention and control randomization grouping, trial researchers will repeat the data collection by collecting participants' total cholesterol, triglycerides, low‐density lipoprotein cholesterol, fasting blood glucose, blood pressure, height, weight, and waist measurement at 6 months and 12 months. Furthermore, non‐physical records of participants will be collected, including health education session participation and major adverse cardiovascular events. Study visit data will usually be collected through electronic information systems.

To minimize potential bias related to differential incentives, both intervention and control groups received standardized training on study procedures, including patient follow‐up and data collection. Data collection activities were largely integrated into routine clinical practice and public health service delivery, thereby limiting additional workload. In addition, the same data quality monitoring and supervision procedures were applied across both groups to ensure consistency and completeness of data.

2.8.5. Outcome

This study will evaluate whether or not the intervention can substantially influence a range of outcomes.

2.8.5.1. Primary Outcome

The primary outcome is the mean difference of 10‐year CVD risk score changes from baseline to 12 months between the intervention and control clusters. The WHO CVD risk laboratory‐based chart for East Asians is a validated tool to estimate the risk of CVD development based on age, sex, systolic blood pressure (SBP), total cholesterol, smoking, and diabetes for the East Asian population, including Chinese. The 10‐year WHO CVD risk score, although originally developed for long‐term risk prediction, is sensitive to short‐term changes in its component risk factors and has been widely used as an integrated surrogate outcome in intervention studies.

2.8.5.2. Secondary Outcome

The secondary outcomes are (1) change in 10‐year CVD risk score between the intervention and control FDTs at 6 months; (2) change in blood pressure control rate between the intervention and control FDTs at 12 months; (3) changes in SBP between the intervention and control FDTs at 12 months; (4) changes in SBP between the intervention and control FDTs at 6 months; (5) change in total cholesterol between the intervention and control FDTs at 12 months; (6) change in fasting blood glucose between the intervention and control FDTs at 12 months; (7) difference of incidence rate of major adverse cardiovascular events (including acute myocardial infarction, stroke, angina, and sudden cardiac death) between the intervention and control FDTs at 12 months; (8) cost‐effectiveness of the intervention compared with usual care at 12 months.

All secondary outcomes, including subgroup analyses and cost‐effectiveness outcomes, will be treated as exploratory and interpreted cautiously given the risk of multiplicity.

2.8.6. Sample Size

An a priori sample size calculation was performed using Stata (version 17.0, StataCorp, College Station, United States) for the primary outcome, defined as the change in the 10‐year WHO CVD risk score. The calculation was based on a two‐sample comparison of means, assuming a two‐sided significance level of 0.05 and 80% statistical power. The standard deviation was set at 6.67, informed by the China‐PAR study. Given that the study population comprised individuals with diagnosed hypertension and/or diabetes, a conservative baseline mean CVD risk score of 8.0 was assumed [27]. Based on previous research, the study was designed to detect a clinically meaningful absolute between‐group difference in the 10‐year CVD risk score at the end of follow‐up [28].

As the trial adopted a cluster randomized design and the number of clusters was fixed at 64 FDTs, with 32 clusters per arm, the required cluster size was estimated accordingly. Clustering at the FDT level was accounted for by assuming an intraclass correlation coefficient of 0.01. Under these assumptions, a minimum of 36 participants per cluster was required to achieve 80% power. Allowing for an anticipated 10% loss to follow‐up, the target cluster size was increased to 40 participants. Accordingly, the total sample size was set at 2560 participants, with 1280 participants in each group.

2.8.7. Evaluation and Control of Contamination

Given that the trial is conducted within the same CTMAs, where FDTs share the same leading hospital, specialist pool, and, in some cases, township health centers, there is a potential risk of contamination between intervention and control groups. To address these risks, several measures have been implemented to minimize potential contamination.

First, specialists involved in the intervention are assigned to specific FDTs on a one‐to‐one basis, and do not provide structured intervention‐related support to control FDTs. This arrangement is intended to prevent crossover of specialist input between study arms.

Second, the intervention is delivered and organized strictly at the FDT level. Key components, including risk stratification, structured case discussions, and related management tools, are restricted to intervention FDTs and are not introduced into control teams. Control FDTs continue to provide usual care according to existing service protocols without access to intervention‐specific processes or materials.

Third, case discussion meetings and associated clinical decision‐making processes are conducted only within intervention teams, with participation limited to designated team members. Documentation and monitoring procedures are implemented to ensure adherence to these arrangements.

While some degree of informal information exchange within the same CTMA cannot be entirely excluded, these measures are expected to minimize contamination. Any residual contamination is likely to bias the estimated intervention effect towards the null, resulting in a conservative estimate of effectiveness.

2.8.8. Analyses

The analysis and reporting of results will adhere to the Consolidated Standards of Reporting Trials guidelines for cluster‐randomized controlled trials. The analysis of both primary and secondary outcomes will account for the clustering effect by employing a mixed‐effects model at the FDT level. The primary outcome will be considered confirmatory. No formal adjustment for multiple testing will be applied to secondary outcomes. These analyses, including subgroup analyses and cost‐effectiveness outcomes, will be regarded as exploratory and interpreted cautiously given the risk of multiplicity.

To address missing data for all outcomes, multiple imputation strategies based on baseline characteristics will be implemented. For the primary outcome, multiple imputation will be performed at the level of individual component variables (i.e., age, sex, SBP, total cholesterol, smoking status, and diabetes status), and the 10‐year CVD risk score will be derived after imputation. An intention‐to‐treat analysis will be conducted. Sensitivity analyses will be pre‐specified to assess the robustness of the findings, including complete‐case analysis and analysis under alternative missing‐not‐at‐random assumptions using delta‐adjustment approaches, in which imputed outcome values for participants with missing data are systematically shifted to reflect potentially worse or better outcomes.

All statistical tests will be performed using two‐sided tests with a significance level of 0.05. Final results will be presented as means with associated two‐sided 95% confidence intervals and p values. Subgroup analyses will be conducted for the primary outcome only and will be considered exploratory. The consistency of treatment effects on the primary outcome will be examined within predefined subgroups, including socioeconomic status, education, gender, age, risk score level, CTMAs, and THCs.

2.8.9. Ethics and Dissemination

The study has been approved by the Peking University Institutional Review Board (No. IRB00001052‐23175). Other participating hospitals or institutions have accepted the ethical approval granted by the Peking University Institutional Review Board. The study findings will be disseminated through multiple channels, including a final report, publications in peer‐reviewed journals, and evidence‐based recommendations tailored to healthcare providers, health service commissioners, and policymakers.

2.8.10. Data Management, Quality, Security

The research will employ a dual‐operator, dual‐computer input structure to input data from the electronic case report form into the respective database system. The database will undergo double data entry. Researchers will be promptly alerted and asked to address any discrepancies arising during this process. Queries and responses during data entry will be documented using a standardized question sheet format.

The approach to data verification and management involves the data administrator generating a comprehensive database inspection report. This report encompasses an assessment of study completion status, including a list of participants who dropped out, as well as inspections of inclusion/exclusion criteria, completeness, logical consistency, adverse events, and outlier data. Review meetings will be arranged to assess those reports.

After data entry and review, questionnaires will be archived and organized for verification. Electronic files, including databases, analysis protocols, and codebooks, will be securely stored on separate disks. Original documents will be retained as required. Anonymized data will be password‐protected to maintain confidentiality, preventing unauthorized access or transmission.

2.8.11. Health Economic Evaluation

Although this study is not designed to observe significant reductions in clinical events, we will model the impact of the differences in risk factors to project the differences in clinical events. It is expected that the long‐term clinical benefits of this intervention would be at least cost‐neutral and potentially cost‐saving when compared to current practice.

A cohort‐based modelling framework will be developed to simulate the progression from cardiovascular risk profiles to clinical events. Changes in individual risk factors observed in the trial (e.g., SBP, total cholesterol, smoking status, and diabetes‐related risk profiles) will be translated into projected cardiovascular event risks using validated risk equations for East Asian populations, including the WHO CVD risk model, supplemented by published epidemiological evidence where necessary.

In the base‐case analysis, the between‐group differences observed at 12 months will be used as the initial treatment effect. It will be assumed that this effect will be maintained for 5 years and then attenuate gradually over time. Age‐related increases in baseline cardiovascular risk will also be incorporated into the model.

The model will adopt a 10‐year time horizon, consistent with the WHO CVD risk estimation framework. Sensitivity analyses will explore alternative assumptions, including shorter (1 year) and longer (lifetime) durations of treatment effect and time horizons.

The economic evaluation will be conducted from the perspectives of both healthcare payers and patients. Costs will include (1) intervention delivery costs associated with the C‐RISIMA programme (including training, coordination, and health education), (2) routine care costs in both intervention and control groups (including medication costs and outpatient visits costs for hypertension and/or diabetes), and (3) downstream healthcare costs related to cardiovascular events, including hospitalizations and follow‐up care. Cost data will be obtained from trial records and routine health system data sources where available.

Health outcomes will be expressed in quality‐adjusted life years, derived from EuroQol 5‐Dimension 5‐Level data collected during the trial and mapped to utility weights based on validated Chinese population tariffs [29]. Beyond the trial period, utility values will be assigned according to modelled health states informed by published literature. Incremental cost‐effectiveness ratios will be calculated to compare the intervention with usual care.

Costs and health outcomes will be discounted at an annual rate of 3% in the base case. Uncertainty will be assessed through deterministic and probabilistic sensitivity analyses. Bootstrapping techniques will be used to estimate confidence intervals around cost and effectiveness estimates, and results will be presented using cost‐effectiveness acceptability curves.

2.8.12. Process Evaluation

Systematic assessments of emerging healthcare programs are imperative for informing both practice and policy formulation. In addition to analyzing quantitative trial outcomes, we are undertaking a comprehensive nested process evaluation. This comprehensive study, utilizing mixed methods, seeks to elucidate the mechanisms underlying the intervention's efficacy or ineffectiveness. Particular attention is given to the integration component, including how integration is implemented and experienced by different members of the FDT, and how it may contribute to improved service delivery and care continuity. It aims to pinpoint contextually appropriate strategies for successful implementation while identifying challenges encountered in the adoption, delivery, and sustainability of the intervention. Importantly, this process evaluation will also demonstrate how the intervention is functionally implemented in real‐world settings and assess the development and operation of collaborative networks among healthcare providers. A detailed protocol for the process evaluation has also been published [30].

2.8.13. Trial Status

This study has randomized 64 FDTs (2560 participants). The enrolment of the first participant took place on May 20, 2024. We anticipate finishing all follow‐up visits by the end of 2025. At the end of the study, participants will be transitioned to continue current or comparable medication therapy as is available in local villages and will receive guidance from local care providers.

3. Discussion

This study is the first, to our knowledge, to integrate individualized CVD risk assessment, dynamic risk‐based intervention adjustments, and blended economic incentives within a multidisciplinary, PC‐centered framework. It addresses limitations of existing approaches by enabling timely clinical decision‐making, improving cross‐level collaboration, and aligning financial incentives to support integrated care. The design considers local healthcare system contexts—including CTMA operations, workforce capacity, and infrastructure—ensuring contextual adaptability. Additionally, the study introduces a differentiated clinical pathway and examines how risk stratification and payment reforms can improve healthcare resource allocation, service delivery, and preventive outcomes. A concurrent process evaluation further enriches understanding of implementation mechanisms.

Several limitations should be noted. First, the 1‐year intervention period may be insufficient to detect long‐term outcomes such as major adverse cardiovascular events or assess the sustainability of effects. To address this, risk‐based modelling approaches (e.g., Markov models) will be used to project long‐term clinical outcomes by translating observed changes in cardiovascular risk into expected event rates over extended time horizons. Second, due to the pragmatic design, participant and provider blinding is not feasible, which may introduce bias. Third, all participants—including those in the control group—receive baseline risk screening and health education advice, potentially diluting the intervention effect. Lastly, the study is conducted in only two counties, limiting generalizability due to regional variations in governance, economic conditions, and CTMA implementation.

Despite these limitations, this trial has the potential to generate novel evidence to inform the design and scale‐up of integrated care models for CVD prevention in LMICs.

Author Contributions

Weijia Lu: conceptualization, methodology, project administration, visualization, writing – original draft, writing – review and editing, investigation. Xinxin Xia: methodology, project administration, visualization, writing – review and editing, investigation. Ziming Wang: project administration, writing – review and editing, investigation. Yaoyun Zhang: project administration, investigation, writing – review and editing. Yiting Lin: investigation, project administration, writing – review and editing. Wuxiang Xie: conceptualization, methodology, writing – review and editing. Jin Xu: conceptualization, methodology, supervision, writing – original draft, writing – review and editing, funding acquisition, project administration.

Ethics Statement

This study has received approval from the Peking University Institutional Review Board (No. IRB00001052‐23175), and it was compliant with the Helsinki Declaration and its subsequent revisions. Other participating hospitals or institutions have accepted the ethical approval granted by the Peking University Institutional Review Board.

Consent

All patients provided written informed consent at the time of entering this study by themselves.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1

Supporting File 2

Data Availability Statement

The data supporting this study's findings are not publicly available due to privacy and confidentiality concerns. Access to the data may be granted upon reasonable request to the corresponding author, subject to ethical and legal considerations.