Impact of POPulation Medicine Multimorbidity Intervention in Xishui County (POPMIX) on people at high risk for COPD who smoke: Protocol of the POPMIX-Smoking cluster-randomized controlled trial

Impact of POPulation Medicine Multimorbidity Intervention in Xishui County (POPMIX) on people at high risk for COPD who smoke: Protocol of the POPMIX-Smoking cluster-randomized controlled trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of POPulation Medicine Multimorbidity Intervention in Xishui County (POPMIX) on people at high risk for COPD who smoke: Protocol of the POPMIX-Smoking cluster-randomized controlled trial Simiao Chen, Ke Huang, Zhoutao Zheng, Yuhao Liu, Shiyu Zhang, and 23 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8128593/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tobacco use is a major contributor to the burden of chronic obstructive pulmonary disease (COPD) and other non-communicable diseases (NCDs) in China. High-risk smokers—particularly those with pre-existing chronic conditions—often remain underserved by conventional smoking cessation programs. Population medicine offers a promising framework for proactively identifying high-burden diseases, managing multimorbidity, and prioritizing interventions for vulnerable populations. Methods This protocol describes a stratified, two-arm cluster randomized controlled trial (cRCT) conducted in Xishui County, Guizhou Province. A total of 26 townships were stratified by population size and randomly assigned in a 1:1 ratio to receive either a multi-component intervention or usual care. Eligible participants were high-COPD-risk smokers aged 35 years or older, screened using the COPD Screening Questionnaire. The intervention includes digital smoking cessation and mental health support, community-based spirometry, tailored chronic disease management, and a pay-for-population mechanism incentivizing providers. Primary outcomes are smoking amount and nicotine dependence, and secondary outcomes include COPD-related health outcomes, hypertension, diabetes, health risk behaviors, quality of life, healthcare utilization, and productivity loss. Follow-up occurs at three, six, and 12 months. Discussion The trial addresses a critical gap in tobacco-related NCD prevention in rural China. By combining behavioral, clinical, and digital health components, and by integrating incentive-aligned delivery through pay-for-population, the intervention aims to demonstrate a scalable, sustainable population medicine strategy. The focus on multimorbidity and early intervention among high-COPD-risk smokers reflects an essential evolution in rural public health practice. Trial registration This trial was registered at clinicaltrials.gov. ClinicalTrials.gov Identifier: NCT06458205. Registered on June 9, 2024. Epidemiology Pulmonology Health Policy Population medicine Smoking cessation Multimorbidity Chronic obstructive pulmonary disease Digital health Primary care Pay-for-population Figures Figure 1 Figure 2 Figure 3 Strengths and limitations of this study POPMIX-Smoking evaluates a novel, multi-component population medicine intervention that integrates smoking cessation, mental health support, and chronic disease management for high-COPD-risk smokers in a rural, resource-limited Chinese setting. It tests the implementation and effectiveness of a proactive, digitally-enabled modular-approach care model, shifting from a patient-centered, reactive approach to a population-centered, preventive one. The trial will generate evidence on the role of a “pay-for-population” incentive mechanism in aligning primary care providers' goals with public health outcomes, a crucial but understudied enabler for scalable population health strategies. It addresses a critical evidence gap on managing multimorbidity and tobacco dependence concurrently, offering a potentially scalable framework for similar low-resource contexts globally. One limitation is that this is a really large community-based real world cRCT within a mountainous terrain where WiFi is usually disconnected., all stakeholders painstakingly recruited participants to the trial and conducted follow-ups and generated additional costs. Background and rationale Modern medicine is undergoing a paradigm shift from focusing solely on the treatment of standalone conditions in individual patients to embracing the promotion of population health. This transition reflects a growing awareness that many health needs remain undetected and unmet. In this context, population medicine has emerged as a comprehensive discipline integrating knowledge, technology, and practice to improve the long-term health of populations. 1 It emphasizes the identification and management of high-burden, modifiable risk factors at the population level, aiming to deliver interventions that maximize communal health and welfare. Tobacco use is a leading preventable risk factor for non-communicable diseases (NCDs) and a critical public health challenge in China. Accounting for 40% of global cigarette consumption, smoking is responsible for approximately 20% of all deaths among middle-aged Chinese men, underscoring its substantial health burden. 2 Tobacco-related NCDs, including chronic obstructive pulmonary disease (COPD), lung cancer, and cardiovascular disease, account for 24% of all NCD deaths in China, significantly exceeding the global average of 15%. 3 These conditions contribute approximately one year of a 3.5-year life expectancy gap attributable to seven major NCDs and injury-related priority conditions between China and the North Atlantic regions; this represents around 23% of the overall 4.3-year life expectancy gap between the two regions. 4 The economic burden of tobacco-related NCDs is profound, with projections indicating that between 2015 and 2030, these conditions will cost China 16.7 trillion yuan (US $ 2.3 trillion), equivalent to 0.9% of the nation’s annual income. 5 Among tobacco-related NCDs, COPD is particularly burdensome in China, with a prevalence of 13.7% among adults over 40 years of age. 6 China also faces the highest economic impact from COPD globally, with an estimated INT $ 1.36 trillion (uncertainty interval: 1.03–1.80 trillion) in cumulative losses over 2020-2050. 7 Recent global estimates project that the number of individuals affected by COPD will rise to 592 million by 2050, with a disproportionate increase among women and populations in low- and middle-income countries, largely driven by persistent tobacco use and indoor biomass exposure. 8 Smoking is the primary modifiable risk factor for COPD; 80–90% of patients have a smoking history, and up to half of older smokers may eventually develop the disease. 9 , 10 Long-term and heavy smoking not only increases disease risk but also accelerates progression and elevates mortality. 11 A meta-analysis covering 28 countries confirmed that COPD prevalence is significantly higher among smokers (15.4%) and former smokers (10.7%) compared to non-smokers (4.3%). 12 Another study showed that compared with non-smokers, current smokers have a 3.51-times higher relative risk of developing COPD, and former smokers have a 2.89-times higher risk. 11 Evidence indicates that smoking cessation can reduce COPD-related mortality by 32–84%, 13 improve pulmonary function, decrease the frequency of acute exacerbations, and extend life expectancy. Despite these benefits, smoking cessation efforts in China remain insufficient, particularly in rural areas. Services are poorly integrated into primary care, and structured interventions are largely absent in underserved areas. 14 , 15 These gaps contribute to persistently high smoking rates and exacerbate tobacco-related disease burden. 16 Moreover, many primary care providers lack training, resources, and incentives to deliver evidence-based cessation support. 17 A core principle of population medicine is the early identification and proactive management of high-risk individuals before irreversible disease develops. In China, most COPD patients are not diagnosed until advanced symptoms appear, with fewer than 1% of patients aware of their condition prior to symptom appearance and less than 6% having undergone spirometry. 18 Early identification of high-risk individuals is therefore essential. Many long-term smokers already experience respiratory symptoms, impaired quality of life, and increased healthcare use despite lacking a formal diagnosis. 19 These high-COPD-risk smokers represent a critical target for intervention by virtue of carrying a substantial but often overlooked disease burden. Targeting them with preventive strategies may delay or prevent irreversible lung damage and reduce long-term healthcare costs. Current COPD interventions (such as smoking cessation, pharmacotherapy, and psychosocial support) are primarily hospital-based and focus on patients with moderate to severe symptoms. Community-level, integrated interventions targeting earlier stages of the disease remain limited. Moreover, many high-COPD-risk individuals suffer from multimorbidity, with common co-occurring health conditions including hypertension, diabetes, and mental health disorders. 20 , 21 These conditions often arise from shared underlying risk factors—including smoking, aging, air pollution, physical inactivity, and socioeconomic disadvantage—and are increasingly recognized as interrelated rather than independent disease processes. 22 They interact biologically and behaviorally to accelerate disease progression, complicate clinical management, and significantly increase healthcare utilization and costs. 23 , 24 In this context, integrated management becomes essential. By addressing multiple conditions through coordinated, person-centered care, integrated approaches can improve clinical outcomes, streamline resource use, and reduce fragmentation in service delivery. Digital health interventions represent a critical component of integrated management, particularly in resource-limited settings. Traditional smoking cessation methods, such as in-person counseling and pharmacotherapy, are often limited by challenges of accessibility, cost, and patient adherence, which are particularly pronounced in rural and resource-limited settings. 25 Digital smoking cessation interventions have gained prominence as highly effective and flexible alternatives to traditional approaches. 26 , 27 By leveraging mobile technology, these interventions can deliver continuous, tailored support, offering features such as real-time behavior tracking, motivational messaging, and automated reminders. Unlike in-person programs, digital solutions are highly scalable and accessible, overcoming geographical and logistical barriers to care, 28 which is particularly beneficial for individuals in rural or underserved areas. Studies indicate that digital cessation programs not only improve smoking cessation rates but also support sustained abstinence, providing a cost-effective and adaptable solution for diverse populations. 29 , 30 Despite evidence supporting the effectiveness of digital health interventions in promoting smoking cessation, 31,32 their impact on COPD patient remains insufficiently studied. Population medicine calls for physicians to take on expanded responsibilities—evolving beyond their traditional role as clinicians treating individuals to also serve as population health stewards responsible for designing and delivering scalable, preventive strategies in the community. Realizing this broader mandate requires the reform of healthcare incentive structures. Pay-for-population models—i.e., value-based payment mechanisms that reward demonstrable improvements in population-level outcomes—serve as crucial enablers of this transition by aligning provider incentives with public health goals and fostering cross-sector collaboration and accountability. Given the substantial burden of tobacco-related morbidity among high-risk individuals and current gaps in early detection and integrated care delivery, there is a critical need for scalable interventions that leverage digital tools and address multimorbidity in a systematic manner. This study aims to fill that gap by evaluating a population-level, multi-component intervention targeting high-COPD-risk smokers in a rural Chinese context. By integrating smoking cessation, mental health support, chronic disease management, and digital health delivery into a unified care framework, this trial seeks to generate robust evidence for an innovative and context-sensitive model of preventive care that may inform future policy and practice in low-resource settings. Objectives This study leverages a cluster-randomized controlled trial design to assess the effectiveness of a multimorbidity intervention package aimed at high-COPD-risk smokers in Xishui, China. Through this approach, we aim to investigate whether a multi-component multimorbidity intervention package affects the primary outcomes—amount of smoking and smoking dependence—and secondary outcomes, including COPD-related health outcomes, hypertension, diabetes, health risk behaviors, quality of life, healthcare utilization, and productivity loss. (ClinicalTrials.gov Identifier: NCT06458205) Trial design This is a parallel, two-arm stratified cluster randomized controlled trial (cRCT) conducted in Xishui, Guizhou (Fig. 1 ). The protocol was designed according to the guidance of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2025 Statement. 33 The SPIRIT Checklist for this protocol is detailed in Supplementary Material, Additional File 1. Townships in Xishui were stratified based on whether their population size exceeds the average for the county. Each stratum was randomly allocated in a 1:1 ratio to either the intervention or control group using a computer-generated randomization sequence. Participant recruitment was based on a comprehensive roster of permanent residents aged 35 years and above, provided by the local government as of May 10, 2024, with individuals selected from each town for enrollment. This study employed the COPD Screening Questionnaire (COPD-SQ), which was developed in 2013 by Zhou et al., 34 to identify individuals at high risk for COPD. The COPD-SQ has a total score range of 0 to 38 and includes seven items: age, cumulative smoking history, body mass index (BMI), cough, breathlessness, family history of respiratory diseases, and exposure to cooking-related smoke. Higher scores indicate greater risk. The instrument has been validated in multiple epidemiological studies and community-based screenings across China and is widely recommended by primary healthcare institutions as a reliable screening tool for COPD. 35 – 37 Only participants with a total score greater than 16 were included in this study, thereby ensuring the cohort consisted of individuals at elevated risk for COPD. Methods: participants, intervention, and outcomes Setting The study was conducted across 26 townships within Xishui County, Guizhou Province. Located in the mountainous region of northern Guizhou, Xishui County is part of a province that ranks second nationally in smoking prevalence at 37.9%. 38 Designated as a national experimental zone for comprehensive primary healthcare, Xishui County is characterized by innovative policy exploration as well as weak economic foundations (with its 2024 per capita GDP at only 52% of the national average) and limited human capital, making it a representative resource-constrained rural area. Xishui’s dual characteristics of economic underdevelopment and healthcare system experimentation provide a unique and appropriate setting to evaluate scalable multi-disease interventions tailored to populations facing resource scarcity. Trial participants (inclusion and exclusion criteria) To be eligible for this trial, high-COPD-risk smokers were required to meet the following criteria: 1) be aged 35 years or older; 2) be a local resident who stayed within a county township in the previous three months and planned to stay within the same township for the next 12 months; 3) have a COPD-SQ score of 16 or higher; 4) provide written informed consent to participate in the study; and 5) self-report as a current smoker or individual who had quit smoking within the past six months. Participants were excluded if they met any of the following conditions: 1) have a severe cognitive impairment that affects comprehension, decision-making, or the ability to follow study procedures or 2) have complete loss of independent daily living ability, which may interfere with participation in assessments or adherence to the intervention. Interventions Participants assigned to the intervention group received access to the intervention package, which was developed through iterative prototyping and stakeholder consultations. The specific eligibility criteria for each intervention are summarized in Table 2. The strategies comprising the multi-component intervention in this trial have been shown to be cost-effective and are endorsed by the Lancet Commission on Investing in Health. 4 Participants in the control arm were informed of their “at risk for COPD” status and invited to complete a face-to-face interview; no intervention was provided afterward, but these individuals were encouraged to receive usual care. Figure 2 illustrates the overall structure and implementation pathway of the intervention package. Stratified screening was conducted based on population characteristics. Multicomponent interventions were implemented for high-COPD-risk smokers, including digital smoking cessation, mental health education, weight management, hypertension and diabetes care, pulmonary function testing, and referral for treatment. Figure 2. Integrated Pathway of the Multi-Component Intervention Package Note: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; BMI = body mass index; CBT = cognitive behavioral therapy; CT = computed tomography; EmoEase = a CBT-based digital mental health intervention via WeChat; NicQuit = a CBT-based digital smoking cessation intervention via WeChat. In addition to the intervention components and care pathways described in Figure 2, the implementation of the multi-component intervention package was supported by a multi-level delivery structure and performance-linked incentive mechanism. Figure 3 illustrates the organizational structure, population stratification, incentive design, and responsibility distribution across administrative levels (county, township, village, household) for delivering the intervention in Xishui. This figure also specifies the eligibility criteria for each target subpopulation, clarifying how risk stratification and service delivery were operationalized in the field. Figure 3. Implementation Structure and Execution Mechanism of the Multi-Component Intervention Package Note: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; ECRHS = European Community Respiratory Health Survey; FEV1 = forced expiratory volume in one second; FVC = forced vital capacity; BMI = body mass index; PFT = pulmonary function test; PCCM = Pulmonary and Critical Care Medicine; CDC = Center for Disease Control and Prevention. We implemented the following specific interventions as part of the multi-component intervention package: 1. Health Education All permanent residents receive general health education. This education focuses on increasing awareness of chronic disease prevention (e.g., COPD, asthma, diabetes, and hypertension), mental health, and healthy behaviors (e.g., tobacco cessation, physical activity, and healthy diet). Health workers distribute printed educational materials during household visits and community events and conduct brief verbal sessions to inform, educate, and empower community members about relevant health issues. 2. Online screening for COPD All permanent residents aged 35 years and above are invited to complete an online screening questionnaire targeting symptoms of COPD. The digital form, accessed via QR code, includes the validated COPD-SQ. Residents who screen positive (COPD-SQ ≥16) are identified as high-COPD-risk individuals and referred for further interventions. 3. Smoking Cessation Digital Health Interventions NicQuit is a WeChat-based digital smoking cessation intervention that includes cognitive-behavioral therapy (CBT) modules focused on smoking cessation strategies, methods for coping with triggers, and reinforcement techniques to maintain abstinence. It is designed for smokers who are currently smoking or have quit within the last six months. The intervention is specifically targeted at individuals familiar with smartphone technology, ensuring accessibility and usability. Personalized notifications and reminders are delivered through the WeChat platform, encouraging participants to regularly engage with the cessation plan and maintain adherence. 4. Health education to smokers for smoking cessation Participants in the intervention group receive targeted health education to reinforce the importance of smoking cessation. This education focuses on the health risks associated with smoking and the benefits of quitting, providing participants with evidence-based information and practical advice. The health education is delivered through verbal communication by primary healthcare providers and printed posters for distribution. 5. Community-based spirometry pulmonary function tests and result interpretations and health education High-COPD-risk individuals in the intervention group receive real-time pop-up alerts directing them to a community gathering place for spirometry testing, which is conducted using BH-AX-MAPG spirometry equipment. Those who screen positive for COPD are referred to the county hospital for computed tomography (CT) and a formal diagnosis. In addition, they receive health education on the risks of COPD and how to prevent and manage the disease, delivered through verbal communication by primary healthcare providers and supplemented with printed materials for distribution. 6. Mental Health Digital Health Interventions A CBT-based digital mental health intervention, EmoEase, 39 is offered to individuals experiencing mental health symptoms (WEMWBS < 45) who have a smartphone. This WeChat-based program includes psychoeducation, mood tracking, guided CBT exercises, and self-regulation techniques. The program emphasizes the link between mental health and respiratory symptoms, aiming to enhance coping, treatment adherence, and sustained behavior change. 7. Health education to smokers with mental health issues Smokers with co-occurring mental health symptoms are offered specialized health education tailored to the challenges they face. This health education includes guidance on how to manage mental health symptoms and is delivered through verbal communication and supplemented with printed materials for distribution. 8. Encouragement to seek professional medical treatment in superior hospitals for spirometry-defined COPD patients and asthma patients Participants diagnosed with COPD or asthma through spirometry—defined as those with a post-bronchodilator FEV1/FVC ratio of <0.7 for COPD and those with a ≥200 mL and ≥12% improvement in FEV1 post-bronchodilation for asthma—are encouraged to seek professional medical treatment at higher-level hospitals for further diagnosis and management. 9. Hypertension and diabetes management The goal of this intervention is to actively enter smokers whose blood pressure is higher than 140/90 mmHg 40 or whose random blood glucose is higher than 11.1mmol/L (or fasting blood glucose ≥ 7.0 mmol/L) into the National Essential Public Health Service in China. 41 These participants are also provided health education on hypertension and diabetes through verbal counseling by trained community health workers or general practitioners and printed materials for distribution. 10. Weight Abnormality interventions Individuals with BMI < 18.5 (underweight) or BMI ≥ 24.0 (overweight and obesity) are considered to have weight abnormalities. CBT-based motivational interviewing is used to guide participants in self-identifying weight-related barriers. During the intervention, interviewees are asked CBT-informed questions designed to make them actively think about the inconveniences and conveniences of being underweight or overweight. 11. Pay-for-population mechanism A comprehensive incentive strategy, integrating both extrinsic and intrinsic components, has been introduced to motivate primary care providers to actively participate in population health interventions. Extrinsic motivation is provided through results-based financial rewards aligned with four key stages of care: screening, diagnosis, treatment, and control. At the township level, health providers are assessed using four performance indicators: the proportion of residents aged 35 years and above completing pulmonary function testing, the proportion of high-risk individuals identified in the initial screening (defined as COPD-SQ >16) subsequently diagnosed with COPD, the proportion of confirmed COPD patients receiving standardized inhaled treatment, and the proportion of patients who have not experienced acute exacerbations in the past six months. The county hospital’s respiratory department and the county CDC are evaluated using the same set of indicators, with data aggregated across all 13 intervention townships. In addition to offering providers an extrinsic, results-based financial incentive, they are also offered specialized training and capacity-building opportunities in an effort to appeal to their intrinsic motivation, support proactive service delivery, and foster a strong sense of responsibility for population health. Outcomes The primary outcomes of this study focus on assessing changes in smoking behavior and dependence over the course of the intervention. The first primary outcome is the participant’s amount of smoking, measured by the self-reported average number of cigarettes smoked per day at baseline and at the three-, six-, and 12-month follow-ups to capture changes in smoking intensity. The second primary outcome is smoking dependence, assessed using the Chinese version of the Fagerström Test for Nicotine Dependence (FTND), 42,43 which scores nicotine dependence on a scale from 0 to 15, with higher scores indicating greater dependence. The Heaviness of Smoking Index (HSI), 44 which ranges from 0 to 6, is used to supplement the FTND by further quantifying the severity of nicotine addiction. The FTND and HIS were administered at baseline and are again administered at the six- and 12-month follow-ups. Secondary outcomes examine broader health and behavioral changes, providing insights into the intervention’s wider impact. Key measures include self-rated health status using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) scale, a validated tool that translates responses into a health utility value between 0 (death) and 1 (perfect health). 45,46 Other secondary outcomes focus on chronic condition management, including the number of conditions effectively controlled (such as COPD, hypertension, and type 2 diabetes), lung function (measured by forced expiratory volume in one second [FEV 1 ] through spirometry), and treatment adherence to prescribed COPD management plans. Additionally, lifestyle habits such as physical activity, diet, and alcohol consumption are monitored to capture changes in health behaviors associated with the intervention (see Table 3). Timeline This study was formally launched in June 2024, with participant recruitment conducted across the entirety of Xishui County. A longitudinal follow-up design was adopted, in which participants assigned to the intervention group are required to complete four standardized assessments: a baseline assessment (conducted on-site), a three-month follow-up (via telephone), a six-month reassessment (on-site), and a final 12-month evaluation (on-site). During the initial assessment phase, written informed consent was obtained from each participant prior to a comprehensive on-site baseline evaluation. A stratified spirometry testing protocol was developed based on participants’ clinical characteristics. Individuals identified as being at high risk for COPD underwent pre-bronchodilator pulmonary function tests at enrollment and do so again at the 12-month follow-up. For participants with a confirmed diagnosis of COPD, post-bronchodilator spirometry is conducted at the same time points. If a high-risk individual presents a baseline FEV1/FVC ratio below 70%, pre-bronchodilator pulmonary function tests are additionally conducted at six months, in addition to baseline and 12 months. All baseline assessments were performed by uniformly trained research personnel to ensure consistency and accuracy in data collection. The three-month follow-up consists of a structured telephone interview aimed at monitoring adherence to the intervention and tracking changes in health status. Comprehensive on-site reassessments at six and 12 months replicate the baseline evaluation procedures, including standardized questionnaires, clinical examinations, and pulmonary function tests. In contrast, the control group follows a simplified protocol, comprising only the baseline questionnaire and a final on-site assessment at 12 months. A detailed schedule of data collection activities for each study phase is provided in Table 4. Sample size Due to budgetary constraints, the target sample size for high-COPD-risk individuals was set at 7,400. Accounting for an anticipated 20% loss to follow-up and an estimated high-risk prevalence of 25% among the general population, 43 a total of approximately 44,000 individuals were screened. Based on the cRCT design, and assuming a reduction of three cigarettes per day in the intervention group 44,45 (with a standard deviation of nine and an intraclass correlation coefficient [ICC] of 0.05), the final required sample size was calculated to be 4,176 high-risk participants. Power calculations Prior studies suggest that digital smoking cessation interventions can reduce cigarette consumption by three cigarettes per day over six months. 47,48 Given high uncertainty over previous findings and the comparatively long assessment duration in this trial, we adopted an estimate of a 3.0-cigarette reduction per day in the intervention group compared to the control group, with an assumed standard deviation of nine cigarettes per day. The sample size was calculated to ensure 80% power at a 5% significance level, with adjustments for cluster design and a 15% attrition rate. Sensitivity analyses confirmed the robustness of these estimates across varying assumptions of intra-cluster correlation and minimal detectable difference. Table 5 shows the minimal detectable differences (MDD) in amount of smoking among high-COPD-risk smokers for 13 townships in each arm for a range of combinations of values of the intracluster correlation coefficient (lCC). Methods: assignment of intervention In this cluster randomized controlled trial (cRCT), we implemented a multi-component smoking cessation intervention at the township level, employing a geographically stratified randomization approach to ensure balance across communities with differing characteristics. A computer-generated randomization sequence was used to allocate 13 townships to either the intervention or control group, with the allocation conducted by an independent statistician uninvolved in participant recruitment or intervention delivery. Given the nature of the intervention, an open-label design was adopted, whereby healthcare providers and the research team were aware of group assignments. However, to minimize expectation bias, participants were not informed of their specific group allocation, and explicit labels such as "intervention group" were deliberately avoided. The control group received only standard care throughout the study period, with no additional intervention applied. To enhance adherence to the intervention, a structured follow-up protocol was implemented. Follow-up assessments were scheduled at three, six, and 12 months post-baseline. At the three-month mark, participants received supportive telephone consultations, while in-person visits were conducted at six months to reinforce engagement. During each follow-up, participants were provided with personalized feedback based on key health indicators—such as spirometry results and smoking status—with comparative analyses against baseline data to highlight areas of improvement or concern. For participants who did not successfully quit smoking or who disengaged from the program, the research team conducted barrier analyses incorporating baseline data to identify potential challenges (e.g., low motivation or difficulties using digital tools). Adherence was systematically assessed through validated questionnaires, capturing indicators such as app usage frequency and the number of quit attempts. Additionally, automated reminders and tailored counseling were used to address issues of non-adherence. This dynamic follow-up framework aimed to optimize the participant experience at each stage, providing continuous motivation and enabling adaptive strategy adjustments to support sustained engagement over the course of the trial. Methods: data collection, management, and analysis Data collection plan To comprehensively evaluate the effectiveness of the multi-component intervention (see Table 1), a multidimensional data collection framework was established. At the initiation of the study, all enrolled participants were required to complete structured questionnaires covering demographic characteristics, smoking history, nicotine dependence (assessed using the Fagerström Test for Nicotine Dependence), mental health status (measured via PHQ-9 for depression, GAD-7 for anxiety, and the WEMWBS for well-being), daily health practices, and history of chronic diseases. In parallel, participants underwent comprehensive physical examinations to obtain measurements of height, weight, BMI, waist circumference, heart rate, blood pressure, and blood glucose levels. Respiratory function was assessed using spirometry to evaluate pulmonary health and detect airflow limitation. For all high-risk smokers, pre-bronchodilator pulmonary function tests were conducted at baseline and are again conducted at week 24 and week 48 to longitudinally monitor changes in lung function. In participants with a confirmed diagnosis of COPD, additional spirometry assessments—both pre- and post-bronchodilator—were performed at baseline and are again performed at week 48 to enable comparative evaluation. Follow-up assessments are conducted at three key time points: three months (via telephone), six months (on-site), and 12 months (on-site). At each follow-up, core indicators from the baseline assessment are re-evaluated, including self-reported smoking status (e.g., daily cigarette consumption and dependence level), COPD management outcomes (respiratory function via spirometry, frequency of acute exacerbations, and treatment adherence), and chronic disease-related metrics (blood pressure, blood glucose, and BMI). Mental health status is reassessed using the same instruments (PHQ-9, GAD-7, WEMWBS), and health-related quality of life is measured using the EQ-5D-5L scale. Lifestyle factors—including physical activity, alcohol consumption, and dietary habits (with attention to sugar, preserved food, and vegetable intake)—are continuously monitored through self-reported data. Utilization of healthcare services, including outpatient visits and hospital admissions, is also recorded. To ensure high data quality, all information is collected by professionally trained field staff following standardized procedures and entered into an electronic data capture (EDC) system equipped with built-in validation mechanisms to minimize entry errors and enhance data reliability. Data management This study employs a digital data capture platform to enable efficient management of research information, with all data entered electronically in real time and subject to automated validation. Trained field investigators utilize mobile terminal devices to complete standardized electronic case report forms, with collected information immediately transmitted to a centralized database. The platform incorporates multiple built-in data quality control modules, including range checks, logical consistency validation, and missing data alerts, thereby significantly enhancing data accuracy. Any outlier values flagged by the system are subject to manual verification by a dedicated data review team. Electronic forms follow a unified design format with pre-coded response options, ensuring comprehensive documentation of participants’ demographic, clinical, and behavioral data. Physiological parameters—such as pulmonary function, blood pressure, BMI, and blood glucose concentration—are directly entered into the system by trained operators. Notably, spirometry data are transmitted from specialized devices and subjected to automated quality assessment procedures to ensure reliability. All study data are stored in a de-identified format within an encrypted database, with strict safeguards in place to protect participant privacy. The database is governed by a tiered access control system, with access granted only to core personnel such as the principal investigator, collaborating researchers, and data analysts, ensuring secure and compliant data use. Statistical analysis In accordance with the intention-to-treat (ITT) principle, data from all randomized participants will be included in the final analysis. The primary outcome will be analyzed using a generalized linear mixed-effects model (GLMM). To assess the robustness of findings in the presence of missing data, sensitivity analyses will be conducted. To enhance statistical efficiency, both unadjusted and covariate-adjusted effect estimates will be reported. Covariates included in the adjusted model comprise age, sex, smoking status, comorbid conditions, socioeconomic status (SES), and baseline outcome measures. A random intercept at the township level will be incorporated into the model to account for potential cluster effects arising from the hierarchical data structure. Methods: monitoring To ensure the scientific rigor, independence, and safety of the study, an independent Data and Safety Monitoring Board (DSMB) was appointed. An external advisory committee provides additional oversight and contributes international perspectives. The steering committee convened three meetings, with the final meeting held in June 2025. These meetings reviewed the trial’s implementation status—including intervention adherence and logistical issues—the completeness and quality of collected data, and preliminary efficacy assessments of the intervention and control groups submitted by the biostatistical team. Based on these evaluations, the steering committee issued one of the following recommendations: (1) If the intervention shows no effect (i.e., null results), the study should be terminated. (2) If the intervention demonstrates a statistically significant effect and achieves the primary endpoint, the study should also be concluded. (3) If a positive trend is observed but statistical significance is not reached, the trial should continue as planned to determine the robustness of the effect. Patient and public involvement Patients and the public were not involved in the design, or conduct, or reporting, or dissemination plans of our trial. However, as this is a population-based real world cRCT, we recruited as many individuals as possible to spread the awareness of multimorbidity and tobacco-related NCDs. The results of this trial will benefit population health. Ethics and dissemination Research ethics approval This study received ethical approval from the Peking Union Medical College Ethics Committee. To protect participant privacy, all identifying information will be removed from the dataset, and data will be anonymized before analysis. Participants have the right to withdraw from the study at any point, with no consequences for their standard healthcare access. The study is committed to upholding the highest standards of ethical research conduct, ensuring that all participants are treated respectfully and that their health and personal information are safeguarded throughout the research process. Ethics approval has been obtained from the Peking Union Medical College Ethics Committee (approval number: CAMS&PUMC-IEC-2024-042). A continuing ethics review was completed in June 2025, and updated approval was granted under approval number CAMS&PUMC-IEC-2025-063. Plans for communicating important protocol amendments to relevant parties The Ethics Commission of the Medical Faculty of Peking Union Medical College was contacted and notified about any protocol amendments requiring their acceptance. Following the approval of the Ethics Committee and before implementation, all research members were informed about protocol amendments. In this trial, an unavoidable amendment was made to the primary outcome measures. Although outcome modifications are generally discouraged, the change was necessitated by a non-negotiable regulatory restriction. Specifically, we initially planned to measure carbon monoxide (CO) levels as an objective biomarker of smoking status. However, due to government regulations prohibiting the purchase of fixed assets, including CO monitoring devices, we were unable to acquire the necessary equipment. As a result, CO measurement was removed from the primary outcomes, and adjustments were made accordingly. These adjustments are reflected in the updated protocol. This amendment was approved by the Ethics Committee, and all research personnel were notified before implementation to ensure consistency in data collection and analysis. Consent and withdrawal We provided written study information and the informed consent form to eligible individuals. The study manager explained the study’s aims and detailed procedures-in the presence of a witness if required. We provided sufficient time for our participants to decide whether or not to participate in the study. Participants were given the opportunity to inquire about details of the study, and responses were provided. For illiterate participants, we obtained a thumbprint signing and a witness’s signature to document consent before enrolment. We informed eligible participants about the risks and benefits of participation in our trial. A decision not to participate in this study will not bear any further consequences for the individual. Every participant is free to refuse or discontinue data collection at any stage. Importantly, participation does not require relinquishing any concomitant care. While there are no special criteria for modifying or discontinuing allocated interventions, we will document and report reasons for attrition in future publications. Confidentiality Throughout the study, all data have been handled with complete confidentiality, and data collection has conformed to requirements of the national legislations on data protection. We are storing digital data on password-protected files in the Center of the Chinese Academy of Medical Sciences. Data are available exclusively to the research team members for complete confidentiality. Third parties may only receive anonymized data for research purposes. Informed consent forms, laboratory books, and other participant-related documents are safely being stored during the study’s conduct and will be stored subsequently at the Co-Principal Investigator’s office premises. Discussion and policy implications This study protocol outlines a cRCT designed to evaluate the effectiveness of a population medicine multimorbidity intervention aimed at high-COPD-risk smokers in Xishui, China. The intervention seeks to address both the physical and mental health needs of this vulnerable population, integrating multimorbidity management into routine care. This approach is particularly timely and relevant, as tobacco use remains a leading preventable risk factor for NCDs in China, and COPD continues to place a significant burden on the healthcare system. Population medicine emphasizes proactive strategies for identifying and managing high-risk groups. Evidence suggests that smokers with COPD face unique challenges in quitting, including a longer history of smoking, lower self-efficacy, and greater psychological dependence. 49 These factors make it more difficult for COPD smokers to quit compared to smokers without the condition. By combining digital health tools with personalized, multifaceted support, this study aims to provide a more effective intervention for high-COPD-risk smokers, addressing the unique physical and psychological challenges they face. High-risk smokers not only face elevated risks of COPD but are also disproportionately affected by hypertension, type 2 diabetes, depression, anxiety, and other modifiable chronic diseases. These conditions often interact synergistically, amplifying disease burden and complicating clinical management. Traditional vertical disease-specific programs are ill-equipped to manage this complexity, particularly in rural communities where care is fragmented and specialist access is limited. By targeting multiple chronic conditions within a unified care framework, our intervention aligns with emerging global strategies that call for integrated, person-centered approaches to multimorbidity management. A key feature of this intervention is its alignment with a broader shift in healthcare—from treating isolated illnesses in individual patients to managing health holistically at the population level. This evolving perspective calls for the early identification of high-burden conditions and a focused effort on those most at risk, such as individuals living with chronic diseases in rural areas. Realizing this vision also depends on enabling physicians to act not only as caregivers to individuals, but also as stewards of community health—supported by systems like pay-for-population that align provider incentives with measurable improvements in population health. The integrated nature of this intervention, which combines behavioral, medical, and digital health strategies, holds promise for addressing the multifaceted needs of high-COPD-risk smokers. Smoking cessation remains one of the most effective strategies for reducing COPD-related mortality and morbidity. 50 By offering both digital interventions like NicQuit and EmoEase alongside in-person health education and support, this study aligns with emerging trends in utilizing mobile health technologies to improve health outcomes in resource-limited settings. The adoption of digital tools has shown to be an effective method for increasing accessibility to smoking cessation resources, especially in rural communities where traditional healthcare infrastructure may be limited. One of the major strengths of this study is its comprehensive, community-based intervention, which combines medical management with health behavior change strategies to target both the underlying causes and the clinical manifestations of COPD. The use of a cRCT design allows for robust evaluation of the intervention's impact at the population level, with the potential to inform public health strategies in rural areas of China. This is particularly important in regions like Xishui, where healthcare access may be limited and where public health interventions targeting high-risk populations are essential for improving outcomes. Furthermore, this study addresses a significant gap in the existing literature, as interventions for high-COPD-risk smokers in China remain scarce. The integration of a multimorbidity intervention could lead to more sustainable improvements in health outcomes, not just for COPD, but also for related comorbidities that disproportionately affect smokers. However, there are potential challenges both to implementing the intervention and evaluating its effectiveness. First, the intervention's success depends on participant engagement, particularly among older populations who may struggle with technology or behavioral change. The digital components of the intervention, if not adequately tailored, may face resistance or low uptake, especially in rural settings. Furthermore, the study’s results may be influenced by local contextual factors such as healthcare infrastructure and socioeconomic status, which could limit the generalizability of the findings beyond Xishui. Additionally, while the study's design is rigorous, it is important to acknowledge that achieving high compliance rates for both the intervention and follow-up assessments could be challenging, especially in a community setting. Addressing barriers to adherence will be crucial for ensuring that the study's findings are meaningful and reliable. This protocol outlines a promising intervention aimed at reducing the burden of COPD and tobacco-related diseases in China, particularly among high-risk smokers in Xishui. This study is timely, as it directly supports China’s national public health strategy by focusing on the prevention and management of COPD, which was integrated into the National Essential Public Health Service in 2024. 51 By targeting multiple health outcomes through a multi-component, community-level intervention, this study offers a potentially scalable model for addressing the needs of individuals with COPD and related co-occurring conditions. The findings from this trial could have significant implications for public health policy and the management of chronic diseases in underserved populations, especially in rural areas. The intervention could provide evidence for incorporating similar multimorbidity management strategies into routine care for COPD patients in China and beyond. This study also has the potential to inform broader efforts to reduce smoking rates and improve mental health outcomes in high-risk populations, ultimately contributing to the reduction of the substantial health and economic burden caused by tobacco-related diseases. Future research should focus on expanding this intervention to other regions, particularly those with different healthcare contexts, to validate its effectiveness across diverse settings. Additionally, further evaluation of the cost-effectiveness of this intervention will be essential for determining its scalability and sustainability in resource-limited areas. Ultimately, this study represents an important step toward improving the health outcomes of high-COPD-risk smokers and contributing to the ongoing fight against tobacco-related diseases in China. Building on these findings, we hope this study will catalyze a paradigm shift in the role of primary and community health workers. In conventional health systems, frontline providers often operate passively, waiting for patients to present at clinics. This trial introduces mechanisms such as pay-for-population, performance-linked process metrics, and home-based outreach to actively engage providers in population screening, health promotion, and continuous care. This transformation—from a patient-centered, reactive model to a proactive, population-centered approach—is at the heart of population medicine. We believe this shift has the potential to reshape public health governance not only in China but also in other low- and middle-income countries striving for equitable, sustainable health system reform. Conclusion This protocol presents a multicomponent intervention for high-COPD-risk smokers, combining smoking cessation, COPD management, mental health support and weight and chronic disease management. Using a cRCT design, the study aims to provide robust evidence on the effectiveness of this multimorbidity intervention in rural China. If successful, it could serve as a model for future population medicine strategies and inform policy decisions for managing COPD and tobacco-related diseases. Further research should explore the cost-effectiveness and scalability of this intervention to determine its long-term impact and broader applicability across different healthcare contexts. Abbreviations ACT Asthma Control Test BMI Body Mass Index CAT COPD Assessment Test CBT cognitive behavioral therapy CDC Center for Disease Control and Prevention CO Carbon Monoxide COPD chronic obstructive pulmonary disease COPD-SQ chronic obstructive pulmonary disease screening questionnaire cRCT cluster-randomized controlled trial CT computed tomography DALY diasability-adjusted life years DSMB data and safety monitoring board ECRHS European Community Respiratory Health Survey EDC electronic data capture EQ-5D 5L The 5-dimension, 5-level version of EuroQol FEV1 forced expiratory volume in 1 second FTND Fagerström Test for Nicotine Dependence FVC forced vital capacity GAD-7 General Anxiety Disorder-7 HBP high blood pressure HSI Heaviness of Smoking Index ICC intraclass correlation coefficient INT $ international dollar ITT intention-to-treat MDD minimum detectable differences mMRC Modified Medical Research Council NCD non-communicable disease PAKQ Patient-completed Asthma Knowledge Questionnaire PCCM Pulmonary and Critical Care Medicine PFT pulmonary function test PHQ-9 Patient Health Questionnaire-9 items PI principal investigator POPMIX Population Medicine Multimorbidity Interventions in Xishui SES socioeconomic status SGRQ Saint George Respiratory Questionnaire SPIRIT Standard Protocol Items: Recommendations for Interventional Trials T2DM Type 2 diabetes mellitus WEMWBS Warwick-Edinburgh Mental Well-being Scale WPAI-GH Work Productivity and Activity Impairment-General Health Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of Peking Union Medical College (Approval No.: CAMS&PUMC-IEC-2024-042). A continuing ethics review was completed and approved in June 2025 under updated approval number CAMS&PUMC-IEC-2025-063. All procedures were carried out in accordance with relevant national and international guidelines and regulations, including the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Not applicable. Trial Status The trial status is active at the moment of submitting the protocol. Recruitment started on June 17, 2024, data collection of the trial will end on December 31, 2025. Competing interests We declare no competing interests. Funding This study was supported by the CAMS Innovation Fund for Medical Sciences (Project Number 2023-I2M-2-001), the National Science and Technology Major Project of Ministry of Science and Technology of China (Project Number 2023ZD0506002). AstraZeneca China (Project No. 2024-HX-22), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2022-ZHCH330-01), and the State Key Laboratory Special Fund (Project Number 2060204). Further funding was contributed by the Tencent Sustainable Social Value Inclusive Health Lab (Project Number SSVPJ202307060001), the EU Horizon Europe Programme (HORIZON-MSCA-2021-SE-01; Project Number 101086139-PoPMeD-SuSDeV), and the China Medical Board (Grant #22-469 to SC). Authors' contributions SC, KH, ZZ, and YL are equally contributed and jointly listed as co-first authors. TB, SC, TY, and CW are listed as the co-senior authors. CW, SC, and TY are listed as corresponding authors. CW, TY, and SC conceived the idea of the trial. CW, TY, SC, KH, ZZ, and YL devised the study design and methodology and initiated the project. YL, ZZ, SZ, YW, TZ, XYT, ZC, JZ, LH, LJ, YCL, QL, XLyu, RD executed all field activities in Xishui County, Guizhou Province, China with input from CW, TY, SC, and LT. KH, XLT, YL, and ZZ managed and prepared study data for analysis. KH, YL, WC, and SZ developed to sub-study on COPD, mental health, and asthma. YL and ZZ wrote the first draft of the manuscript with input from CW, SC, TY, KH, TB, QC, AB, SV, PG, DJ, and all co-authors. All authors contributed to revising the manuscript. All authors have contributed to this protocol manuscript according to the International Committee of Medical Journal Editors’ guidelines. Author Information School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Simiao Chen, Zhoutao Zheng, Yuhao Liu, Wenjin Chen, Liu He, Till Bärnighausen & Chen Wang Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany Simiao Chen, Zhong Cao, Jinghan Zhao, Aditi, Bunker & Till Bärnighausen State Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China Simiao Chen, Ke Huang, Ting Yang & Chen Wang Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China Ke Huang, Xingyao Tang, Ting Yang & Chen Wang National Center for Respiratory Medicine, Beijing, China Ke Huang, Xingyao Tang, Ting Yang & Chen Wang School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China Shiyu Zhang Guizhou Medical University, Guiyang, Guizhou, China Lei Tang Department of Pulmonary and Critical Care Medicine, National Center of Gerontology, Beijing Hospital, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China Xunliang Tong Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA Lirui Jiao Center for Disease Control and Prevention of Xishui County, Zunyi, Guizhou, China Yingping Wang, Tianying Zhao & Yingchi Luo Department of Pulmonary and Critical Care Medicine, People’s Hospital of Xishui County, Zunyi, Guizhou, China Qiande Lai & Xiangqin Lyu The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, State College, PA, USA Qiushi Chen Department of Economics and Centre for Modern Indian Studies, University of Goettingen, Göttingen, Germany Sebastian Vollmer Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA Pascal Geldsetzer Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA Pascal Geldsetzer Department of Epidemiology and Biostatistics and Institute for Global Health Sciences, University of California, San Francisco, CA, USA Dean Jamison Department of Global and Population Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA Till Bärnighausen Acknowledgements This work was supported by the People's Government of Xishui County, Health Bureau of Xishui County, Youth League Committee of Guizhou Medical University, Medieco Group Co. einPaper Team, Finance Bureau of Xishui County, Health Insurance Bureau of Xishui County, Bureau of Sports and Education of Xishui County, Bureau of Statistics of Xishui County, Xishui Sub-Bureau of Ecology and Environment Bureau of Zunyi City, Water Authority of Xishui County, Bureau of Housing and Urban-Rural Development of Xishui County, Meteorological Bureau of Xishui County, Center for Disease Control and Prevention of Xishui County (Health Surveillance Station of Xishui County), People's Hospital of Xishui County, Traditional Chinese Medicine Hospital of Xishui County, Maternal and Child Health Care Hospital of Xishui county, and the People's Governments (Offices) of 26 Townships (Subdistricts) and Village Committees of Xishui County. YL acknowledges the support of the China Scholarship Council Program (Project ID: 202406210195). We thank the participants of the POPMIX study. For continuous support, assistance, and cooperation, we thank all field workers from Xishui County: Chun Zhang, Yuzhu Ye, Mingqiang Hou, Shengbo Liao, Qiande Lai, Xiangqin Lyu, Yuan Zhou, Dong Liu, Chentao Zhong, Yushuang Zhao, Hongxia Mu, Min Liu, Qiong Lyu, Zhengyu Zhang, Weiwei Li (People's Hospital of Xishui County); Jian Wang, Tiansheng Lan, Tingyu Liu (Traditional Chinese Medicine Hospital of Xishui County); Xin Zhou (Maternal and Child Health Care Hospital of Xishui County); Bie Yu, Xianping Wang, Yingchi Luo, Yingping Wang, Tianying Zhao, Yansong Luo, Wenwu Jiang, Wen Zhang, Dongmei Wu, Qingping Luo, Dajun Rao, Cunkun Yang, Feng Zhao, Qin Wang, Linlin Zhang, Can Li, Min Chen, Yufeng Huang (Center for Disease Control and Prevention of Xishui County); Guanghai Jian, Li Wang, Yunjiang Zheng, Jianghong Linghu, Li Zhong, Xiaoling Ma, Bin Gui, Yan Cheng, Yu Yang, Jing Yan, Xixi Liao, Mingguo Xu, Zhijun Zhao, Qian Han, Panyan Wu, Dandan Tang, Qingqing Wang (Community Health Service Center of Jiulong Street, Xishui County); Li Feng, Lilan Wang, Lei Si, Peng Zeng, Jianxiu Tian, Weili Fan, Xin Luo, Sumei Feng, Rong Ding, Huadan Yi, Fengxue Lyu, Ludan Yuan, Yuan Liu, Xueshuang Wang, Aihong Duan, Yang Zhao, Yuping Luo, Yicheng Li (Community Health Service Center of Shanwang Street, Xishui County); Anchun Liang, Chengmei Li, Hu Tang, Anfeng Liu, Xu Wang, Yi Zhong, Wen Zheng, Benwei Zhao, Qianmei Zhang, Xiaojing Tu, Huanhuan Lu, Xiumei Zhao, Yan Chen, Xuemei Yuan, Yanhong Mu, Yao Zhao, Juan Zhang, Qinhui Li, Fuhui Wang, Xiaomin Mu, Yan Hu, Liuyang Chen (Community Health Service Center of Donghuang Street, Xishui County); Gang Lei, Zhufei Hu, Xiangyong Tang, Yan Luo, Shunxia Zhou, Ting Pan, Yuanyin Zhou, Xiaoqin Ren, Yaling Zhou, Jing Zeng, Keping Hu, Xiaoyu Liu, Deguo Yang, Qiong Wan, Cai Zhang, Heng He (Health Service Center of Malin Street, Xishui County); Chengping Wang (Village Clinics of Xiangyang, Malin Street, Xishui County); Yuanzhong Wang, Shuyin Wang (Village Clinics of Mianshan, Malin Street, Xishui County); Xuhong Lei (Village Clinics of Linfeng, Malin Street, Xishui County); Yushen Yuan (Village Clinics of Wuyi, Malin Street, Xishui County); Tailiang Luo (Village Clinics of Shanghua, Malin Street, Xishui County); Dazhou Zhu (Village Clinics of Miaoping, Malin Street, Xishui County); Anrong Luo (Village Clinics of MiaopingLinping, Malin Street, Xishui County); Xianmei Hu (Village Clinics of Wuyi, Malin Street, Xishui County); Ya Zhang, Ling Yuan, Senlin Luo, Weili Ma, Yonghua Cao, Yunpeng Cao, Taojin Chen, Wen Chen, Zhiqun Chen, Zhongyi Chen, Huagang Cheng, Xianpan Feng, Taimei Gao, Lizhu Jian, Renxian Jiang, Shengmei Kang, Hang Lei, Shanshan Liu, Ruxun Lu, Xiaojiao Luo, Dongshun Lyu, Guanghong Meng, Jianping Mu, Xuerou Mu, Hongxia Qian, Huimin Ren, Juan Wang, Li Wang, Loutao Wang, Qin Wang, Wei Wang, Ya Wang, Yan Wang, Dongjie Wu, Xiaoxia Xu, Jing Yang, Qian Yu, Dan Yuan, Yanlu Yuan, Yu Yuan, Hongying Yue, Yuanmin Yue, Li Zhang, Zhizhong Zhang, Jiaqin Zhao, Yuanxin Zhao, Yue Zhong, Lejie Zhou, Lufang Yang, Xue Chen, Lihui Ren (Health Center of Guandian Town, Xishui County); Guixiang Gong, Ping Shi, Jiangshun Tan, Zhonghang Yu, Lijun Mu, Meilin Chen, Su Yu, Lin Li, Xianbi Mu, Jianxia Wu, Mei Zhao, Jiangmei Chen, Jing Huang, Yanfang Li, Huayan Yang, Jiaheng Yang, Xingqi Yuan, Yunhao Zhou, Mengting Ye, Junlin Zhang, Taijun Luo, Shuai Wang, Ju Hu (Health Center of Zhaiba Town, Xishui County); Gang Yuan (Village Clinics of Shangba, Zhaiba Town, Xishui County); Shili Mu (Village Clinics of Youyi, Zhaiba Town, Xishui County); Zhongxiang Yuan, Huili He (Village Clinics of Fenghuang, Zhaiba Town, Xishui County); Jiali Li (Village Clinics of Guiyuan, Zhaiba Town, Xishui County); Chunlan Liu (Village Clinics of Yongsheng, Zhaiba Town, Xishui County); Kailiang Cheng (Village Clinics of Hexin, Zhaiba Town, Xishui County); Quan Liu (Village Clinics of Tiaotai, Zhaiba Town, Xishui County); Huade Yang (Village Clinics of Fuxing, Zhaiba Town, Xishui County); Shaojun Zhang (Village Clinics of Xiyuan, Zhaiba Town, Xishui County); Bo Chen (Village Clinics of Sanlian, Zhaiba Town, Xishui County); Guishan Ying (Village Clinics of Lingxianhe, Zhaiba Town, Xishui County); Xumei Zhao (Village Clinics of Xianfeng Jiedao, Zhaiba Town, Xishui County); Zhi He (Village Clinics of Tiaotai, Zhaiba Town, Xishui County); Gan Feng, Huanhuan Guo, Hongling Yuan, Fuyou Yuan, Wei Wang, Lang Chen, Liping Zhao, Jiajun Ning, Jie Feng, Yuxian Chen, Jin Chen, Jing Qi, Yuanting Li, Lingli Zhao, Guo Wang, Ziyi Wang, Huaishu Zhong (Health Center of Erlang Town, Xishui County); Jie Wang, Gang Chen, Xueqin Mu, Yuanfeng Gong, Xinan Lu, Zhengli Chen, Tianwei Chen, Xiaoping Chen, Runqin Mu, Xiaoyi Deng, Yu Duan, Xiujuan Dong, Wangyong Yan (Health Center of Niba Township, Xishui County); Jiayong Rao (Village Clinics of Feilongshan, Niba Township, Xishui County); Mingfang Wang (Village Clinics of Nantianmen, Niba Township, Xishui County); Xiangcai Chen (Village Clinics of Baziqiao, Niba Township, Xishui County); Zhongquan Wang (Village Clinics of Xiaoguchi, Niba Township, Xishui County); Lanfang Zhang (Health Center of Niba Township, Xishui County); Jianghua Luo, Kaijie Huang, Ye Chen, Jin'e Li, Li Huang, Ju Chen, Hao Huang, Qingqun Long, Yan Chen, Ling Jiang, Man Ding, Xiaolin Yu, Tongjie Wei, Zhengqin Liu (Health Center of Xijiu Town, Xishui County); Kaifu Si, Yuxian Mu, Yan Huang, Qian Yang, Tao Xu, Xiaorong Yuan, Cailun Zhao, Minli Chen, Rongfang Luo, Ling Chen, Ying Zhang, Guiwei Yuan, Qianyi Shui, Yong Wu, Qian Huang, Changli Wu, Nan Zhang, Chaojiang Wu, Defei Chen, Wu Xu, Linhui He, Wanlun Zhang, Jinsong Chen, Jin Yuan (Health Center of Sanchahe Town, Xishui County); Rujun Li, Minghe Wang, Chunmei Zhang, Yueshan Wang, Lang Liao, Anqian Feng, Dengyang Wang, Zelin Deng, Hupiao Yang, Kaimeng You, Lijuan Deng, Qiong Huang, Xiaoqing Zhang, Zhengqian Zhu (Health Center of Yong'an Town, Xishui County); Xiaoyan Liao, Yongbo Song, Yushui Yuan, Qinghong Cai, Lu Liu, Hong Yu, Xue Luo, Xiaoshan Huang, Qin Hu, Dian Yuan, Zengye Zhao, Jiemin Zeng, Zhaoyan Huang, Peng Luo, Taibo Luo, Wenwu Wu, Guangni Liu, Gengning Zhang (Health Center of Tongmin Town, Xishui County); Mingyuan Ren (Village Clinics of Chaya, Tongmin Town, Xishui County); Mei Yang (Village Clinics of Shengli, Tongmin Town, Xishui County); Zhongjin Wang (Village Clinics of Hongqi, Tongmin Town, Xishui County); Rongping Yuan (Village Clinics of Linjiang, Tongmin Town, Xishui County); Yunfeng Yuan (Village Clinics of Shengli, Tongmin Town, Xishui County); Anquan Yuan (Agricultural Machinery Station Clinic of Tongmin Village, Tongmin Town, Xishui County); Yunqiang Chen (Tongxin Clinic of Tongmin Village, Tongmin Town, Xishui County); Hongxian Liu (Village Clinics of Tongmin Village, Tongmin Town, Xishui County); Xingyu He (Changhong Clinic of Tongmin Village, Tongmin Town, Xishui County); Tongxiang Zhang (Village Clinics of Xinglong Village, Tongmin Town, Xishui County); Mingkai Xu (Taiping Clinic of Chang'an Village, Tongmin Town, Xishui County); Minqing Hu (Village Clinics of Chang'an Village, Tongmin Town, Xishui County); Chunyi Qian, Yunfen Zhang (Committee of Chang'an Village, Tongmin Town, Xishui County); Yueguang Lei, Jun Wang, Xiaoju Yuan, Changxian Yang, Ai Xiang, Minjing Zhao, Xiaomei Ren, Guimei Li, Huiying Ren (Health Center of Liangcun Town, Xishui County); Ju Teng, Yu Chen, Jing Yuan, Shunfeng Mu, Xiangyu Zhang, Ye Li, Yuwei Sun, Shangjing Wang, Fangping He (Health Center of Sangmu Town, Xishui County); Guoxiang Wang (Village Clinics of Dangba, Sangmu Town, Xishui County); Wei Cai (Village Clinics of Shangba, Sangmu Town, Xishui County); Weijiang Tian (Village Clinics of Tuhe, Sangmu Town, Xishui County); Tingfen Cai (Village Clinics of Dashan, Sangmu Town, Xishui County); Zhao Chen (Village Clinics of Heshan, Sangmu Town, Xishui County); Dakai Mu (Baimu Clinic of Gonghe Village, Sangmu Town, Xishui County); Yufu Mu (Village Clinics of Gonghe Village, Sangmu Town, Xishui County); Cai Cheng (Village Clinics of Xiangshu Village, Sangmu Town, Xishui County); Xiaofang Pan (Village Clinics of Yinchang Village, Sangmu Town, Xishui County); Fang Wang (Village Clinics of Senlin Village, Sangmu Town, Xishui County); Mingqian Zhao (Village Clinics of Tongjuan Village, Sangmu Town, Xishui County); Qijun Liu, Bangqi Yang, Ye Yu, Ting Min, Xiaoya Wang, Jun Li, Yang Zhang, Yan Wang, Lihong Zhao, Zhihua Luo, Hao Yuan, Huihua Ruan, Jian Wu, Chengfen Wang, Dezhi Zhang, Yunfang Zhang (Health Center of Minhua Town, Xishui County); Weining Ao, Pan Zhao, Yunxia Liang, Yicheng Zhao, Xiaosong Mu, Lyufang Luo, Ziyi Zhang, Wei Huang, Jun Cao, Huan Li, Jun Liu, Liye Yuan, Na Zhao, Ju Wang, Qiuyan Duan, Weizhong Chen, Lirong Zhao, Jiangbo Wu, Liqin Deng (Health Center of Shuanglong Township, Xishui County); Kunyu Wang, Zhengguo Yan, Chaolian Wang, Can Liu, Quan Cao, Gang Ma, Youyong Lu, Yuan Zhang, Kaijing Xiang, Yixiang Wang, Xingye Zhu, Jiamei Wang, Demin Hu, Dongmin Li, Peng Chen, Chao Tian, Lu Wang, Mei Wang, Lulu Xiao, Lingling Yu, Min Wei, Chengcheng Yang, Limei Zhang, Rong Zhao, Chongguang Zhong, Yan Xiao (Health Center of Xianyuan Town, Xishui County); Jian Luo, Hongfa Chen, Pengju Dai, Jin Liu, Yingtao Luo, Guangjie Ma, Shiyou Mu, Zhengli Rao, Deqin Wen, Guowang Wu (Village Clinics of Xianyuan Town, Xishui County); Yong Hu, Xiaorong Shi, Shaofen Huang, Ya Chen, Wenmao Zhang, Min Chen, Xin Luo, Hongqiong Ruan, Liming Liu, Lihui Fan, Xiaoyan Zou, Huizhi He, Xinggao Yang (Health Center of Longxing Town, Xishui County); Linfei Wang (Village Clinics of Xinguang, Longxing Town, Xishui County); Hong Wang (Village Clinics of Linchan, Longxing Town, Xishui County); Wenxiang Li (Village Clinics of Yongsheng, Longxing Town, Xishui County); Jialian Wu (Village Clinics of Taoguan, Longxing Town, Xishui County); Jinyong Yang (Community Clinic of Longxing, Longxing Town, Xishui County); Jianying Wu (Village Clinics of Taoguan, Longxing Town, Xishui County); Guangliang Liu (Village Clinics of Linchan, Longxing Town, Xishui County); Xiaolin Xiong (Village Clinics of Binjiang, Longxing Town, Xishui County); Gang Yang (Village Clinics of Yongsheng, Longxing Town, Xishui County); Ping Feng (Village Clinics of Gantian, Longxing Town, Xishui County); Cheng Luo (Village Clinics of Longxi, Longxing Town, Xishui County); Qiwei Zhang (Village Clinics of Xinguang, Longxing Town, Xishui County); Qihui Yang, Taoshan Ni, Rensong Tian, Li Zhang, Yuanmei Zhang, Hui Bi, Bijue Huang, Yu Yan, Shan Chen, Xinghui Chen, Jiangfei Wang, Guanglun Zou, Junting Liu, Rongjiang Zhao, Li Yuan (Health Center of Taolin Town, Xishui County); Ruyin Yuan, Jianbing Li, Aiping Wang, Daijie Wang, Mei Yang, Kaiyan Si, Peng Wang, Yan Linghu, Tuye Yuan, Zhiyuan Yuan, Weiwei Qian, Daimei Wang, Rongqin Ren, Dong Dai, Qianhua Deng, Yan Xu, Jiali Zhang, Qingyan Bai, Yingwen Zhang, Xingke Wang, Qiang Liu, Liyuan Feng, Fancha Wang, Shiwen Wang, Xiangguang Liu, Runping Liu, Xiaoying Wang, Daizhi Wang, Huihai Cao, Jiang Yuan, Qinhui Cao, Min Luo (Health Center of Xingmin Town, Xishui County); Hao Hou, Lu Kong, Zhu Ren, Yu Zhou, Xiaoyan Yuan, Hong Liu, Shixian Gong, Yonghong Zhao, Rusheng Wang, Ji Chen (Health Center of Dapo Town, Xishui County); Fuping Bai, Yang Zhou, Wenbi Chen, Yu Sun, Qunying Xu, Gang Zheng, Yong Wang, Juan Chen, Ling Zhong, Ji Ren, Qiling Zhu, Zheng Yang, Yongsong Chen, Zhonghui Huang, Xiaojiang Zhang, Junyan Yu, Panting Yu, Qun Wang, Qimei Yu, Hao Zhou, Yuanmei Lu, Yiyu Wang, Min Huang, Feng Wu (Health Center of Erli Town, Xishui County); Jiangyu Yuan, Guangjun Xiong, Jian Zhang, Hong Zhou, Fenfang Liu, Yuqin Chen, Xiaoli Ye, Qingli Luo, Qijie Dai, Yunxian Feng, Tianmei Huang, Yunfeng Xia, Lei Zhao, Anshui Wang, Fuxiu Yang, Minqin Hu, Tingting Yuan, Song Zhou (Health Center of Tucheng Town, Xishui County); Lin Yuan (Community Clinic of Changzheng, Tucheng Town, Xishui County); Mingjian Yuan (Community Clinic of Tuanjie, Tucheng Town, Xishui County); Maozhao Wen (Village Clinics of Huangjinwan, Tucheng Town, Xishui County); Hongming Zhao (Village Clinics of Gaoping, Tucheng Town, Xishui County); Hongbo Zhao (Village Clinics of Qunfeng, Tucheng Town, Xishui County); Yuanchao Zhao (Village Clinics of Shuishiba, Tucheng Town, Xishui County); Binyuan Yuan (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Hua Xiong (Village Clinics of Wuxing, Tucheng Town, Xishui County); Qiyong Yuan (Village Clinics of Qixin, Tucheng Town, Xishui County); Qin Yuan (Village Clinics of Xingfu, Tucheng Town, Xishui County); Xingqin Zhao (Village Clinics of Hongwei, Tucheng Town, Xishui County); Qifu Luo (Village Clinics of Hongwei, Tucheng Town, Xishui County); Qigui Luo (Village Clinics of Tongxin, Tucheng Town, Xishui County); Tulin Yuan (Village Clinics of Changba, Tucheng Town, Xishui County); Xianglin Huang (Village Clinics of Honghua, Tucheng Town, Xishui County); Chun Luo (Village Clinics of Jiulongtun, Tucheng Town, Xishui County); Dehong Wang (Village Clinics of Tianxingqiao, Tucheng Town, Xishui County); Rongwei Zhang (Village Clinics of Qianyan, Tucheng Town, Xishui County); Xingji Chen (Village Clinics of Tongyi, Tucheng Town, Xishui County); Zhong'en Wang (Village Clinics of Tianxingqiao, Tucheng Town, Xishui County); Deping Yuan (Village Clinics of Wuxing, Tucheng Town, Xishui County); Qihua Luo (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Huahua Li (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Yueming Wang, Jian Chen, Wei Teng, Jisheng Wang, Mingli He, Shan Wu, Junjun Xia, Tingxian Mao, Tingmin Luo, Sihong Yi, Ruixue Li, Kang Yang (Health Center of Chengzhai Town, Xishui County); Chengshang Ao, Xu Zhong, Yang Liu, Langsha Chen, Huan Luo, Qingxia Wang, Chen Chen, Wenqiang Wu, Yumei Wu, Limei Feng, Caixian Lei, Kai Li, Youqun Chen (Health Center of Huilong Town, Xishui County); Lulu Zhao, Han Ren, Juhang Yang, Shanshan Luo, Xiaoyi Zhang, Huiqin Yuan, Li Chen, Guishuang Liu, Jiakuan Ren, Dongqiao Jiang, Yi Yuan, Dengmin Ren, Xiaojiang Mu, Xue Yang (Health Center of Wenshui Town, Xishui County). We also showed sincere gratitude to field workers who dedicated to data quality controls from Guizhou Medical University: Wuyao He, Wanyi Hu, Piaopiao Huang, Guijiang Li, Jinman Li, Suosuo Li, Yiling Lu, Yuqi Lu, Huan Luo, Qingqing Luo, Wenqin Mu, Daolin Qin, Jinlan Quan, Yingying Tang, Junbao Wang, Lingling Wang, Rui Xu, Chenxi Yang, Dengqin Yang, Liqin Yang, Qingting Zhang, Lei Zhao, Lin Zhao, Jingmei Zhou, Lijuan Zuo, Xiaoning Xia, Yujia Xie, Hangyu Chen, Li Yang, Yu Zhao, Sisi Li, Juan Pan, Ju Yang, Minjia Zhao, Meiyuan Zhou, Guimei Hu, Chunqin Huang, Haiyan Long, Zeyuan Luo, Donghua Pan, Xuelang Ying, Yanyan Zhang, Yuting Zhang, Hong Zhao, Yunli Zhou, Bijin Zhu (School of Public Health, Guizhou Medical University); Kang Chen, Kaidi Ding, Jiacheng Dong, Tai Fu, Qin Li, Zaijing Liu, Siqi Huang, Lingli Ou, Zhuoting Tan, Yan Tang, Qingzhong Wu, Xinyi Xu, Bingyang Xu, Yulin Zhang, Shuangshuang Zhou, Zena Zhu, Jiancao Zuo, Guangxianfeng Chen, Jiahui Luo, Zhiqi Duan, Shangrong Gu, Binyao Ou, Xingtai Zhao (School of Clinical Medicine, Guizhou Medical University); Yanran He, Xingli Liao, Shunxin Shi, Cheng Wang, Jiayu Wang, Rongmei Wang, Ruirui Zhang, Yuhan Zhou, Serena Luo, Jiwei Jin, Xiaoqi Shi, Ziqianqian Yang, Xiaoxue An, Jiwei Jin, Mingli Huang, Yinghua Mu, Xiaozheng Wang, Songlin Zhang, Xue Zhou, Ruirui Ao, Shaohuan Bao, Yating Jiang, Yijun Liu, Zhirui Nie, Dingyuan Rao, Jiangqin Wan, Xue Wang, Die Wei, Lijin Xie, Jianan Xu, Xuerong Zhang, Die Deng, Jingyu Fu, Shunli Guo, Binhan He, Sitong Liu, Lin Liu, Linqu Qin, Teng Tian, Kehao Wang, Qirui Wu, Laitian Ye, Xiao Zhang, Tiantian Zhou (School of Basic Medicine, Guizhou Medical University); Sitong Zhou, Jiajia Liu, Huali Yin, Yue Luo (School of Anesthesiology, Guizhou Medical University); Jianxiu Bai, Yuchan Li, Xinglian Zhang, Shenghui Zhu, Xue Chen, Dan Jiang (School of Nursing, Guizhou Medical University); Xiaoqin Yang, Changnan Zhao, Fangjing Geng (School of Medical Imaging, Guizhou Medical University); Jia Li, Jie Wang, Wenfan Hu (School of Stomatology, Guizhou Medical University); Xingling Li, Yumei Lu, Zhuying Long, Junxue Qian, Guoxian Shi, Ruoxi Wang, Guangjian Wu, Guangyan Zhang (School of Pharmaceutical Sciences, Guizhou Medical University); Rongrong Zhou, Hongyu Hu, Zhaoyi Ren, Lingxuan Shi, Meng Zeng, Shengsheng Li, Ying Qin, Yi Zhou (School of Biology and Engineering [School of Health Medicine Modern Industry]‌, Guizhou Medical University); Tianyu Liu (School of Marxism, Guizhou Medical University); Jia He, Jinan Li, Mingyuan Mao, Jiayi Wang (School of Medicine and Health Management, Guizhou Medical University); Ronghui Ma (School of Humanities, Guizhou Medical University); Bin Li (School of Forensic Medicine, Guizhou Medical University). 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Respiration 90(3):211–219 Global Initiative for Chronic Obstructive Lung Disease (2024) Global strategy for prevention, diagnosis and management of COPD: 2025 Report General Office of the National Health Commission. Notice of the General Office of the National Health Commission on the Issuance of the Health Care Standards for Chronic (2024) Obstructive Pulmonary Disease Patients (Trial Implementation). https://www.gov.cn/zhengce/zhengceku/202409/content_6974437.htm Tables Table 1 to 5 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files TableSmoking.docx Tables of the Protocol Appendix1SPIRIT2025smoking.docx SPIRIT 2025 Checklist Cite Share Download PDF Status: Posted 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. 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLACxgYGBn4kNpFaJBtI1mJwgFgtBsfPHn7xc4dNnvHtHtMNPxhsZDccYH72AK+WM3lplr1n0orN7pwxu9nDkGa84QCbuQE+LWYHcsyMGdsOJ267kWN2m4HhcOKGAzxsEni1nH8D0vI/cfMMsJb/RGi5kWP8mLHtQOIGCbCWA4S12N94Y8bY25acOOPOsbKbPQbJxjMPs5nh1SLZn2P84WebXWL/7OZtN35U2Mn2HW9+hlcLEECdASZBQcVMQD1IyQeEllEwCkbBKBgFWAAArtdQVxins7kAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":545837071,"identity":"f592ee26-81bd-4985-9c37-b72bef5e04aa","order_by":27,"name":"POPMIX Study Group","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"POPMIX","middleName":"Study","lastName":"Group","suffix":""}],"badges":[],"createdAt":"2025-11-16 16:52:19","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8128593/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8128593/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96252897,"identity":"14ac8f1d-aab7-4b50-a1dd-8aede0a75aba","added_by":"auto","created_at":"2025-11-19 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1","display":"","copyAsset":false,"role":"figure","size":163272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of POPMIX-Smoking Trial\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage128.png","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/eadade7ef81bf8304060f7d6.png"},{"id":96202739,"identity":"0e5c3db6-1045-4cd3-a53d-f3da5fe9d200","added_by":"auto","created_at":"2025-11-18 16:45:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5361519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated Pathway of the Multi-Component Intervention Package\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; BMI = body mass index; CBT = cognitive behavioral therapy; CT = computed tomography; EmoEase = a CBT-based digital mental health intervention via WeChat; NicQuit = a CBT-based digital smoking cessation intervention via WeChat.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage215.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/a73b9c5aaf8a1f211251dda5.jpeg"},{"id":96202744,"identity":"94fcf6ff-4b62-4a08-a4fd-4fbf05b99f89","added_by":"auto","created_at":"2025-11-18 16:46:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":825757,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImplementation Structure and Execution Mechanism of the Multi-Component Intervention Package\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; ECRHS = European Community Respiratory Health Survey; FEV1 = forced expiratory volume in one second; FVC = forced vital capacity; BMI = body mass index; PFT = pulmonary function test; PCCM = Pulmonary and Critical Care Medicine; CDC = Center for Disease Control and Prevention.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/43bd8ba9fb80850720455b58.jpeg"},{"id":96257165,"identity":"f17c4794-4352-4023-bf02-ac0a4c596b11","added_by":"auto","created_at":"2025-11-19 07:51:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7445445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/602312d1-de8f-446e-96bb-310ddf7d2a3b.pdf"},{"id":96202737,"identity":"c3bd91a1-078e-4da7-8285-c24ad8812fd5","added_by":"auto","created_at":"2025-11-18 16:45:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35132,"visible":true,"origin":"","legend":"\u003cp\u003eTables of the Protocol\u003c/p\u003e","description":"","filename":"TableSmoking.docx","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/2e1376d03da522e36ad31bc1.docx"},{"id":96202746,"identity":"86feb667-6b1e-4181-95b6-585b918ccb99","added_by":"auto","created_at":"2025-11-18 16:46:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29757,"visible":true,"origin":"","legend":"\u003cp\u003eSPIRIT 2025 Checklist\u003c/p\u003e","description":"","filename":"Appendix1SPIRIT2025smoking.docx","url":"https://assets-eu.researchsquare.com/files/rs-8128593/v1/237a0855ca18c12948ced1f1.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eImpact of POPulation Medicine Multimorbidity Intervention in Xishui County (POPMIX) on people at high risk for COPD who smoke: Protocol of the POPMIX-Smoking cluster-randomized controlled trial\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Strengths and limitations of this study","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003ePOPMIX-Smoking evaluates a novel, multi-component population medicine intervention that integrates smoking cessation, mental health support, and chronic disease management for high-COPD-risk smokers in a rural, resource-limited Chinese setting.\u003c/li\u003e\n \u003cli\u003eIt tests the implementation and effectiveness of a proactive, digitally-enabled modular-approach care model, shifting from a patient-centered, reactive approach to a population-centered, preventive one.\u003c/li\u003e\n \u003cli\u003eThe trial will generate evidence on the role of a \u0026ldquo;pay-for-population\u0026rdquo; incentive mechanism in aligning primary care providers\u0026apos; goals with public health outcomes, a crucial but understudied enabler for scalable population health strategies.\u003c/li\u003e\n \u003cli\u003eIt addresses a critical evidence gap on managing multimorbidity and tobacco dependence concurrently, offering a potentially scalable framework for similar low-resource contexts globally.\u003c/li\u003e\n \u003cli\u003eOne limitation is that this is a really large community-based real world cRCT within a mountainous terrain where WiFi is usually disconnected., all stakeholders painstakingly recruited participants to the trial and conducted follow-ups and generated additional costs.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background and rationale","content":"\u003cp\u003eModern medicine is undergoing a paradigm shift from focusing solely on the treatment of standalone conditions in individual patients to embracing the promotion of population health. This transition reflects a growing awareness that many health needs remain undetected and unmet. In this context, population medicine has emerged as a comprehensive discipline integrating knowledge, technology, and practice to improve the long-term health of populations.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e It emphasizes the identification and management of high-burden, modifiable risk factors at the population level, aiming to deliver interventions that maximize communal health and welfare.\u003c/p\u003e\u003cp\u003eTobacco use is a leading preventable risk factor for non-communicable diseases (NCDs) and a critical public health challenge in China. Accounting for 40% of global cigarette consumption, smoking is responsible for approximately 20% of all deaths among middle-aged Chinese men, underscoring its substantial health burden.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Tobacco-related NCDs, including chronic obstructive pulmonary disease (COPD), lung cancer, and cardiovascular disease, account for 24% of all NCD deaths in China, significantly exceeding the global average of 15%.\u003csup\u003e3\u003c/sup\u003e These conditions contribute approximately one year of a 3.5-year life expectancy gap attributable to seven major NCDs and injury-related priority conditions between China and the North Atlantic regions; this represents around 23% of the overall 4.3-year life expectancy gap between the two regions.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The economic burden of tobacco-related NCDs is profound, with projections indicating that between 2015 and 2030, these conditions will cost China 16.7 trillion yuan (US\u003cspan\u003e$\u003c/span\u003e2.3 trillion), equivalent to 0.9% of the nation\u0026rsquo;s annual income.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAmong tobacco-related NCDs, COPD is particularly burdensome in China, with a prevalence of 13.7% among adults over 40 years of age.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e China also faces the highest economic impact from COPD globally, with an estimated INT\u003cspan\u003e$\u003c/span\u003e1.36 trillion (uncertainty interval: 1.03\u0026ndash;1.80 trillion) in cumulative losses over 2020-2050.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Recent global estimates project that the number of individuals affected by COPD will rise to 592\u0026nbsp;million by 2050, with a disproportionate increase among women and populations in low- and middle-income countries, largely driven by persistent tobacco use and indoor biomass exposure.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Smoking is the primary modifiable risk factor for COPD; 80\u0026ndash;90% of patients have a smoking history, and up to half of older smokers may eventually develop the disease.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Long-term and heavy smoking not only increases disease risk but also accelerates progression and elevates mortality.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e A meta-analysis covering 28 countries confirmed that COPD prevalence is significantly higher among smokers (15.4%) and former smokers (10.7%) compared to non-smokers (4.3%).\u003csup\u003e12\u003c/sup\u003e Another study showed that compared with non-smokers, current smokers have a 3.51-times higher relative risk of developing COPD, and former smokers have a 2.89-times higher risk.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Evidence indicates that smoking cessation can reduce COPD-related mortality by 32\u0026ndash;84%,\u003csup\u003e13\u003c/sup\u003e improve pulmonary function, decrease the frequency of acute exacerbations, and extend life expectancy. Despite these benefits, smoking cessation efforts in China remain insufficient, particularly in rural areas. Services are poorly integrated into primary care, and structured interventions are largely absent in underserved areas.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e These gaps contribute to persistently high smoking rates and exacerbate tobacco-related disease burden.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Moreover, many primary care providers lack training, resources, and incentives to deliver evidence-based cessation support.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA core principle of population medicine is the early identification and proactive management of high-risk individuals before irreversible disease develops. In China, most COPD patients are not diagnosed until advanced symptoms appear, with fewer than 1% of patients aware of their condition prior to symptom appearance and less than 6% having undergone spirometry.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Early identification of high-risk individuals is therefore essential. Many long-term smokers already experience respiratory symptoms, impaired quality of life, and increased healthcare use despite lacking a formal diagnosis.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e These high-COPD-risk smokers represent a critical target for intervention by virtue of carrying a substantial but often overlooked disease burden. Targeting them with preventive strategies may delay or prevent irreversible lung damage and reduce long-term healthcare costs. Current COPD interventions (such as smoking cessation, pharmacotherapy, and psychosocial support) are primarily hospital-based and focus on patients with moderate to severe symptoms. Community-level, integrated interventions targeting earlier stages of the disease remain limited.\u003c/p\u003e\u003cp\u003eMoreover, many high-COPD-risk individuals suffer from multimorbidity, with common co-occurring health conditions including hypertension, diabetes, and mental health disorders.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e These conditions often arise from shared underlying risk factors\u0026mdash;including smoking, aging, air pollution, physical inactivity, and socioeconomic disadvantage\u0026mdash;and are increasingly recognized as interrelated rather than independent disease processes.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e They interact biologically and behaviorally to accelerate disease progression, complicate clinical management, and significantly increase healthcare utilization and costs.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e In this context, integrated management becomes essential. By addressing multiple conditions through coordinated, person-centered care, integrated approaches can improve clinical outcomes, streamline resource use, and reduce fragmentation in service delivery.\u003c/p\u003e\u003cp\u003eDigital health interventions represent a critical component of integrated management, particularly in resource-limited settings. Traditional smoking cessation methods, such as in-person counseling and pharmacotherapy, are often limited by challenges of accessibility, cost, and patient adherence, which are particularly pronounced in rural and resource-limited settings.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Digital smoking cessation interventions have gained prominence as highly effective and flexible alternatives to traditional approaches.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e By leveraging mobile technology, these interventions can deliver continuous, tailored support, offering features such as real-time behavior tracking, motivational messaging, and automated reminders. Unlike in-person programs, digital solutions are highly scalable and accessible, overcoming geographical and logistical barriers to care,\u003csup\u003e28\u003c/sup\u003e which is particularly beneficial for individuals in rural or underserved areas. Studies indicate that digital cessation programs not only improve smoking cessation rates but also support sustained abstinence, providing a cost-effective and adaptable solution for diverse populations.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Despite evidence supporting the effectiveness of digital health interventions in promoting smoking cessation,\u003csup\u003e31,32\u003c/sup\u003e their impact on COPD patient remains insufficiently studied.\u003c/p\u003e\u003cp\u003ePopulation medicine calls for physicians to take on expanded responsibilities\u0026mdash;evolving beyond their traditional role as clinicians treating individuals to also serve as population health stewards responsible for designing and delivering scalable, preventive strategies in the community. Realizing this broader mandate requires the reform of healthcare incentive structures. Pay-for-population models\u0026mdash;i.e., value-based payment mechanisms that reward demonstrable improvements in population-level outcomes\u0026mdash;serve as crucial enablers of this transition by aligning provider incentives with public health goals and fostering cross-sector collaboration and accountability.\u003c/p\u003e\u003cp\u003eGiven the substantial burden of tobacco-related morbidity among high-risk individuals and current gaps in early detection and integrated care delivery, there is a critical need for scalable interventions that leverage digital tools and address multimorbidity in a systematic manner. This study aims to fill that gap by evaluating a population-level, multi-component intervention targeting high-COPD-risk smokers in a rural Chinese context. By integrating smoking cessation, mental health support, chronic disease management, and digital health delivery into a unified care framework, this trial seeks to generate robust evidence for an innovative and context-sensitive model of preventive care that may inform future policy and practice in low-resource settings.\u003c/p\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cp\u003eThis study leverages a cluster-randomized controlled trial design to assess the effectiveness of a multimorbidity intervention package aimed at high-COPD-risk smokers in Xishui, China. Through this approach, we aim to investigate whether a multi-component multimorbidity intervention package affects the primary outcomes\u0026mdash;amount of smoking and smoking dependence\u0026mdash;and secondary outcomes, including COPD-related health outcomes, hypertension, diabetes, health risk behaviors, quality of life, healthcare utilization, and productivity loss. (ClinicalTrials.gov Identifier: NCT06458205)\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eTrial design\u003c/h2\u003e\u003cp\u003eThis is a parallel, two-arm stratified cluster randomized controlled trial (cRCT) conducted in Xishui, Guizhou (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The protocol was designed according to the guidance of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2025 Statement.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e The SPIRIT Checklist for this protocol is detailed in Supplementary Material, Additional File 1.\u003c/p\u003e\u003cp\u003eTownships in Xishui were stratified based on whether their population size exceeds the average for the county. Each stratum was randomly allocated in a 1:1 ratio to either the intervention or control group using a computer-generated randomization sequence. Participant recruitment was based on a comprehensive roster of permanent residents aged 35 years and above, provided by the local government as of May 10, 2024, with individuals selected from each town for enrollment.\u003c/p\u003e\u003cp\u003eThis study employed the COPD Screening Questionnaire (COPD-SQ), which was developed in 2013 by Zhou et al.,\u003csup\u003e34\u003c/sup\u003e to identify individuals at high risk for COPD. The COPD-SQ has a total score range of 0 to 38 and includes seven items: age, cumulative smoking history, body mass index (BMI), cough, breathlessness, family history of respiratory diseases, and exposure to cooking-related smoke. Higher scores indicate greater risk. The instrument has been validated in multiple epidemiological studies and community-based screenings across China and is widely recommended by primary healthcare institutions as a reliable screening tool for COPD.\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Only participants with a total score greater than 16 were included in this study, thereby ensuring the cohort consisted of individuals at elevated risk for COPD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods: participants, intervention, and outcomes","content":"\u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted across 26 townships within Xishui County, Guizhou Province. Located in the mountainous region of northern Guizhou, Xishui County is part of a province that ranks second nationally in smoking prevalence at 37.9%.\u003csup\u003e38\u003c/sup\u003e Designated as a national experimental zone for comprehensive primary healthcare, Xishui County is characterized by innovative policy exploration as well as weak economic foundations (with its 2024 per capita GDP at only 52% of the national average) and limited human capital, making it a representative resource-constrained rural area. Xishui\u0026rsquo;s dual characteristics of economic underdevelopment and healthcare system experimentation provide a unique and appropriate setting to evaluate scalable multi-disease interventions tailored to populations facing resource scarcity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial participants (inclusion and exclusion criteria)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo be eligible for this trial, high-COPD-risk smokers were required to meet the following criteria: 1) be aged 35 years or older; 2) be a local resident who stayed within a county township in the previous three months and planned to stay within the same township for the next 12 months; 3) have a COPD-SQ score of 16 or higher; 4) provide written informed consent to participate in the study; and 5) self-report as a current smoker or individual who had quit smoking within the past six months. Participants were excluded if they met any of the following conditions: 1) have a severe cognitive impairment that affects comprehension, decision-making, or the ability to follow study procedures or 2) have complete loss of independent daily living ability, which may interfere with participation in assessments or adherence to the intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterventions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants assigned to the intervention group received access to the intervention package, which was developed through iterative prototyping and stakeholder consultations. The specific eligibility criteria for each intervention are summarized in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTable 2. The strategies comprising the multi-component intervention in this trial have been shown to be cost-effective and are endorsed by the \u003cem\u003eLancet\u003c/em\u003e Commission on Investing in Health.\u003csup\u003e4\u003c/sup\u003e Participants in the control arm were informed of their \u0026ldquo;at risk for COPD\u0026rdquo; status and invited to complete a face-to-face interview; no intervention was provided afterward, but these individuals were encouraged to receive usual care. Figure 2 illustrates the overall structure and implementation pathway of the intervention package. Stratified screening was conducted based on population characteristics. Multicomponent interventions were implemented for high-COPD-risk smokers, including digital smoking cessation, mental health education, weight management, hypertension and diabetes care, pulmonary function testing, and referral for treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Integrated Pathway of the Multi-Component Intervention Package\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; BMI = body mass index; CBT = cognitive behavioral therapy; CT = computed tomography; EmoEase = a CBT-based digital mental health intervention via WeChat; NicQuit = a CBT-based digital smoking cessation intervention via WeChat.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the intervention components and care pathways described in Figure 2, the implementation of the multi-component intervention package was supported by a multi-level delivery structure and performance-linked incentive mechanism. Figure 3 illustrates the organizational structure, population stratification, incentive design, and responsibility distribution across administrative levels (county, township, village, household) for delivering the intervention in Xishui. This figure also specifies the eligibility criteria for each target subpopulation, clarifying how risk stratification and service delivery were operationalized in the field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Implementation Structure and Execution Mechanism of the Multi-Component Intervention Package\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: COPD = chronic obstructive pulmonary disease; COPD-SQ = COPD Screening Questionnaire; WEMWBS = Warwick-Edinburgh Mental Well-being Scale; ECRHS = European Community Respiratory Health Survey; FEV1 = forced expiratory volume in one second; FVC = forced vital capacity; BMI = body mass index; PFT = pulmonary function test; PCCM = Pulmonary and Critical Care Medicine; CDC = Center for Disease Control and Prevention.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe implemented the following specific interventions as part of the multi-component intervention package:\u003c/p\u003e\n\u003cp\u003e1. \u003cem\u003eHealth Education\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll permanent residents receive general health education. This education focuses on increasing awareness of chronic disease prevention (e.g., COPD, asthma, diabetes, and hypertension), mental health, and healthy behaviors (e.g., tobacco cessation, physical activity, and healthy diet). Health workers distribute printed educational materials during household visits and community events and conduct brief verbal sessions to inform, educate, and empower community members about relevant health issues.\u003c/p\u003e\n\u003cp\u003e2. \u003cem\u003eOnline screening for COPD\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll permanent residents aged 35 years and above are invited to complete an online screening questionnaire targeting symptoms of COPD. The digital form, accessed via QR code, includes the validated COPD-SQ. Residents who screen positive (COPD-SQ \u0026ge;16) are identified as high-COPD-risk individuals and referred for further interventions.\u003c/p\u003e\n\u003cp\u003e3. \u003cem\u003eSmoking Cessation Digital Health Interventions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNicQuit is a WeChat-based digital smoking cessation intervention that includes cognitive-behavioral therapy (CBT) modules focused on smoking cessation strategies, methods for coping with triggers, and reinforcement techniques to maintain abstinence. It is designed for smokers who are currently smoking or have quit within the last six months. The intervention is specifically targeted at individuals familiar with smartphone technology, ensuring accessibility and usability. Personalized notifications and reminders are delivered through the WeChat platform, encouraging participants to regularly engage with the cessation plan and maintain adherence.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4. Health education to smokers for smoking cessation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants in the intervention group receive targeted health education to reinforce the importance of smoking cessation. This education focuses on the health risks associated with smoking and the benefits of quitting, providing participants with evidence-based information and practical advice. The health education is delivered through verbal communication by primary healthcare providers and printed posters for distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5. Community-based spirometry pulmonary function tests and result interpretations and health education\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHigh-COPD-risk individuals in the intervention group receive real-time pop-up alerts directing them to a community gathering place for spirometry testing, which is conducted using BH-AX-MAPG spirometry equipment. Those who screen positive for COPD are referred to the county hospital for computed tomography (CT) and a formal diagnosis. In addition, they receive health education on the risks of COPD and how to prevent and manage the disease, delivered through verbal communication by primary healthcare providers and supplemented with printed materials for distribution.\u003c/p\u003e\n\u003cp\u003e6. \u003cem\u003eMental Health Digital Health Interventions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA CBT-based digital mental health intervention, EmoEase,\u003csup\u003e39\u003c/sup\u003e is offered to individuals experiencing mental health symptoms (WEMWBS\u0026nbsp;\u0026lt; 45) who have a smartphone. This\u0026nbsp;WeChat-based program includes psychoeducation, mood tracking, guided CBT exercises, and self-regulation techniques. The program emphasizes the link between mental health and respiratory symptoms, aiming to enhance coping, treatment adherence, and sustained behavior change.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7. Health education to smokers with mental health issues\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSmokers with co-occurring mental health symptoms are offered specialized health education tailored to the challenges they face. This health education includes guidance on how to manage mental health symptoms and is delivered through verbal communication and supplemented with printed materials for distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e8. Encouragement to seek professional medical treatment in superior hospitals for spirometry-defined COPD patients and asthma patients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants diagnosed with COPD or asthma through spirometry\u0026mdash;defined as those with a post-bronchodilator FEV1/FVC ratio of \u0026lt;0.7 for COPD and those with a\u0026nbsp;\u0026ge;200 mL and\u0026nbsp;\u0026ge;12% improvement in FEV1 post-bronchodilation for asthma\u0026mdash;are encouraged to seek professional medical treatment at higher-level hospitals for further diagnosis and management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e9. Hypertension and diabetes management\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe goal of this intervention is to actively enter smokers whose blood pressure is higher than 140/90 mmHg\u003csup\u003e40\u003c/sup\u003e or whose random blood glucose is higher than 11.1mmol/L (or fasting blood glucose \u0026ge; 7.0 mmol/L) into the National Essential Public Health Service in China.\u003csup\u003e41\u003c/sup\u003e These participants are also provided health education on hypertension and diabetes through verbal counseling by trained community health workers or general practitioners and printed materials for distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e10. Weight Abnormality interventions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals with BMI \u0026lt; 18.5 (underweight) or BMI\u0026nbsp;\u0026ge;\u0026nbsp;24.0 (overweight and obesity) are considered to have weight abnormalities. CBT-based motivational interviewing is used to guide participants in self-identifying weight-related barriers. During the intervention, interviewees are asked CBT-informed questions designed to make them actively think about the inconveniences and conveniences of being underweight or overweight.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e11. Pay-for-population mechanism\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive incentive strategy, integrating both extrinsic and intrinsic components, has been introduced to motivate primary care providers to actively participate in population health interventions. Extrinsic motivation is provided through results-based financial rewards aligned with four key stages of care: screening, diagnosis, treatment, and control. At the township level, health providers are assessed using four performance indicators: the proportion of residents aged 35 years and above completing pulmonary function testing, the proportion of high-risk individuals identified in the initial screening (defined as COPD-SQ \u0026gt;16) subsequently diagnosed with COPD, the proportion of confirmed COPD patients receiving standardized inhaled treatment, and the proportion of patients who have not experienced acute exacerbations in the past six months. The county hospital\u0026rsquo;s respiratory department and the county CDC are evaluated using the same set of indicators, with data aggregated across all 13 intervention townships. In addition to offering providers an extrinsic, results-based financial incentive, they are also offered specialized training and capacity-building opportunities in an effort to appeal to their intrinsic motivation, support proactive service delivery, and foster a strong sense of responsibility for population health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcomes of this study focus on assessing changes in smoking behavior and dependence over the course of the intervention. The first primary outcome is the participant\u0026rsquo;s amount of smoking, measured by the self-reported average number of cigarettes smoked per day at baseline and at the three-, six-, and 12-month follow-ups to capture changes in smoking intensity. The second primary outcome is smoking dependence, assessed using the Chinese version of the Fagerstr\u0026ouml;m Test for Nicotine Dependence (FTND),\u003csup\u003e42,43\u003c/sup\u003e which scores nicotine dependence on a scale from 0 to 15, with higher scores indicating greater dependence. The Heaviness of Smoking Index (HSI),\u003csup\u003e44\u003c/sup\u003e which ranges from 0 to 6, is used to supplement the FTND by further quantifying the severity of nicotine addiction. The FTND and HIS were administered at baseline and are again administered at the six- and 12-month follow-ups. Secondary outcomes examine broader health and behavioral changes, providing insights into the intervention\u0026rsquo;s wider impact. Key measures include self-rated health status using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) scale, a validated tool that translates responses into a health utility value between 0 (death) and 1 (perfect health).\u003csup\u003e45,46\u003c/sup\u003e Other secondary outcomes focus on chronic condition management, including the number of conditions effectively controlled (such as COPD, hypertension, and type 2 diabetes), lung function (measured by forced expiratory volume in one second [FEV\u003csub\u003e1\u003c/sub\u003e] through spirometry), and treatment adherence to prescribed COPD management plans. Additionally, lifestyle habits such as physical activity, diet, and alcohol consumption are monitored to capture changes in health behaviors associated with the intervention (see Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTimeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was formally launched in June 2024, with participant recruitment conducted across the entirety of Xishui County. A longitudinal follow-up design was adopted, in which participants assigned to the intervention group are required to complete four standardized assessments: a baseline assessment (conducted on-site), a three-month follow-up (via telephone), a six-month reassessment (on-site), and a final 12-month evaluation (on-site). During the initial assessment phase, written informed consent was obtained from each participant prior to a comprehensive on-site baseline evaluation.\u003c/p\u003e\n\u003cp\u003eA stratified spirometry testing protocol was developed based on participants\u0026rsquo; clinical characteristics. Individuals identified as being at high risk for COPD underwent pre-bronchodilator pulmonary function tests at enrollment and do so again at the 12-month follow-up. For participants with a confirmed diagnosis of COPD, post-bronchodilator spirometry is conducted at the same time points. If a high-risk individual presents a baseline FEV1/FVC ratio below 70%, pre-bronchodilator pulmonary function tests are additionally conducted at six months, in addition to baseline and 12 months. All baseline assessments were performed by uniformly trained research personnel to ensure consistency and accuracy in data collection.\u003c/p\u003e\n\u003cp\u003eThe three-month follow-up consists of a structured telephone interview aimed at monitoring adherence to the intervention and tracking changes in health status. Comprehensive on-site reassessments at six and 12 months replicate the baseline evaluation procedures, including standardized questionnaires, clinical examinations, and pulmonary function tests. In contrast, the control group follows a simplified protocol, comprising only the baseline questionnaire and a final on-site assessment at 12 months. A detailed schedule of data collection activities for each study phase is provided in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to budgetary constraints, the target sample size for high-COPD-risk individuals was set at 7,400. Accounting for an anticipated 20% loss to follow-up and an estimated high-risk prevalence of 25% among the general population,\u003csup\u003e43\u003c/sup\u003e a total of approximately 44,000 individuals were screened. Based on the cRCT design, and assuming a reduction of three cigarettes per day in the intervention group\u003csup\u003e44,45\u003c/sup\u003e (with a standard deviation of nine and an intraclass correlation coefficient [ICC] of 0.05), the final required sample size was calculated to be 4,176 high-risk participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior studies suggest that digital smoking cessation interventions can reduce cigarette consumption by three cigarettes per day over six months.\u003csup\u003e47,48\u003c/sup\u003e Given high uncertainty over previous findings and the comparatively long assessment duration in this trial, we adopted an estimate of a 3.0-cigarette reduction per day in the intervention group compared to the control group, with an assumed standard deviation of nine cigarettes per day. The sample size was calculated to ensure 80% power at a 5% significance level, with adjustments for cluster design and a 15% attrition rate. Sensitivity analyses confirmed the robustness of these estimates across varying assumptions of intra-cluster correlation and minimal detectable difference. Table 5 shows the minimal detectable differences (MDD) in amount of smoking among high-COPD-risk smokers for 13 townships in each arm for a range of combinations of values of the intracluster correlation coefficient (lCC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: assignment of intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this cluster randomized controlled trial (cRCT), we implemented a multi-component smoking cessation intervention at the township level, employing a geographically stratified randomization approach to ensure balance across communities with differing characteristics. A computer-generated randomization sequence was used to allocate 13 townships to either the intervention or control group, with the allocation conducted by an independent statistician uninvolved in participant recruitment or intervention delivery. Given the nature of the intervention, an open-label design was adopted, whereby healthcare providers and the research team were aware of group assignments. However, to minimize expectation bias, participants were not informed of their specific group allocation, and explicit labels such as \u0026quot;intervention group\u0026quot; were deliberately avoided. The control group received only standard care throughout the study period, with no additional intervention applied.\u003c/p\u003e\n\u003cp\u003eTo enhance adherence to the intervention, a structured follow-up protocol was implemented. Follow-up assessments were scheduled at three, six, and 12 months post-baseline. At the three-month mark, participants received supportive telephone consultations, while in-person visits were conducted at six months to reinforce engagement. During each follow-up, participants were provided with personalized feedback based on key health indicators\u0026mdash;such as spirometry results and smoking status\u0026mdash;with comparative analyses against baseline data to highlight areas of improvement or concern. For participants who did not successfully quit smoking or who disengaged from the program, the research team conducted barrier analyses incorporating baseline data to identify potential challenges (e.g., low motivation or difficulties using digital tools). Adherence was systematically assessed through validated questionnaires, capturing indicators such as app usage frequency and the number of quit attempts. Additionally, automated reminders and tailored counseling were used to address issues of non-adherence. This dynamic follow-up framework aimed to optimize the participant experience at each stage, providing continuous motivation and enabling adaptive strategy adjustments to support sustained engagement over the course of the trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: data collection, management, and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection plan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo comprehensively evaluate the effectiveness of the multi-component intervention (see Table 1), a multidimensional data collection framework was established. At the initiation of the study, all enrolled participants were required to complete structured questionnaires covering demographic characteristics, smoking history, nicotine dependence (assessed using the Fagerstr\u0026ouml;m Test for Nicotine Dependence), mental health status (measured via PHQ-9 for depression, GAD-7 for anxiety, and the WEMWBS for well-being), daily health practices, and history of chronic diseases. In parallel, participants underwent comprehensive physical examinations to obtain measurements of height, weight, BMI, waist circumference, heart rate, blood pressure, and blood glucose levels. Respiratory function was assessed using spirometry to evaluate pulmonary health and detect airflow limitation. For all high-risk smokers, pre-bronchodilator pulmonary function tests were conducted at baseline and are again conducted at week 24 and week 48 to longitudinally monitor changes in lung function. In participants with a confirmed diagnosis of COPD, additional spirometry assessments\u0026mdash;both pre- and post-bronchodilator\u0026mdash;were performed at baseline and are again performed at week 48 to enable comparative evaluation. Follow-up assessments are conducted at three key time points: three months (via telephone), six months (on-site), and 12 months (on-site).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt each follow-up, core indicators from the baseline assessment are re-evaluated, including self-reported smoking status (e.g., daily cigarette consumption and dependence level), COPD management outcomes (respiratory function via spirometry, frequency of acute exacerbations, and treatment adherence), and chronic disease-related metrics (blood pressure, blood glucose, and BMI). Mental health status is reassessed using the same instruments (PHQ-9, GAD-7, WEMWBS), and health-related quality of life is measured using the EQ-5D-5L scale. Lifestyle factors\u0026mdash;including physical activity, alcohol consumption, and dietary habits (with attention to sugar, preserved food, and vegetable intake)\u0026mdash;are continuously monitored through self-reported data. Utilization of healthcare services, including outpatient visits and hospital admissions, is also recorded. To ensure high data quality, all information is collected by professionally trained field staff following standardized procedures and entered into an electronic data capture (EDC) system equipped with built-in validation mechanisms to minimize entry errors and enhance data reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employs a digital data capture platform to enable efficient management of research information, with all data entered electronically in real time and subject to automated validation. Trained field investigators utilize mobile terminal devices to complete standardized electronic case report forms, with collected information immediately transmitted to a centralized database. The platform incorporates multiple built-in data quality control modules, including range checks, logical consistency validation, and missing data alerts, thereby significantly enhancing data accuracy. Any outlier values flagged by the system are subject to manual verification by a dedicated data review team. Electronic forms follow a unified design format with pre-coded response options, ensuring comprehensive documentation of participants\u0026rsquo; demographic, clinical, and behavioral data.\u003c/p\u003e\n\u003cp\u003ePhysiological parameters\u0026mdash;such as pulmonary function, blood pressure, BMI, and blood glucose concentration\u0026mdash;are directly entered into the system by trained operators. Notably, spirometry data are transmitted from specialized devices and subjected to automated quality assessment procedures to ensure reliability. All study data are stored in a de-identified format within an encrypted database, with strict safeguards in place to protect participant privacy. The database is governed by a tiered access control system, with access granted only to core personnel such as the principal investigator, collaborating researchers, and data analysts, ensuring secure and compliant data use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the intention-to-treat (ITT) principle, data from all randomized participants will be included in the final analysis. The primary outcome will be analyzed using a generalized linear mixed-effects model (GLMM). To assess the robustness of findings in the presence of missing data, sensitivity analyses will be conducted.\u003c/p\u003e\n\u003cp\u003eTo enhance statistical efficiency, both unadjusted and covariate-adjusted effect estimates will be reported. Covariates included in the adjusted model comprise age, sex, smoking status, comorbid conditions, socioeconomic status (SES), and baseline outcome measures. A random intercept at the township level will be incorporated into the model to account for potential cluster effects arising from the hierarchical data structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the scientific rigor, independence, and safety of the study, an independent Data and Safety Monitoring Board (DSMB) was appointed. An external advisory committee provides additional oversight and contributes international perspectives.\u003c/p\u003e\n\u003cp\u003eThe steering committee convened three meetings, with the final meeting held in June 2025. These meetings reviewed the trial\u0026rsquo;s implementation status\u0026mdash;including intervention adherence and logistical issues\u0026mdash;the completeness and quality of collected data, and preliminary efficacy assessments of the intervention and control groups submitted by the biostatistical team.\u003c/p\u003e\n\u003cp\u003eBased on these evaluations, the steering committee issued one of the following recommendations:\u003c/p\u003e\n\u003cp\u003e(1) If the intervention shows no effect (i.e., null results), the study should be terminated.\u003c/p\u003e\n\u003cp\u003e(2) If the intervention demonstrates a statistically significant effect and achieves the primary endpoint, the study should also be concluded.\u003c/p\u003e\n\u003cp\u003e(3) If a positive trend is observed but statistical significance is not reached, the trial should continue as planned to determine the robustness of the effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and the public were not involved in the design, or conduct, or reporting, or dissemination plans of our trial. However, as this is a population-based real world cRCT, we recruited as many individuals as possible to spread the awareness of multimorbidity and tobacco-related NCDs. The results of this trial will benefit population health. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and dissemination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch ethics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Peking Union Medical College Ethics Committee. To protect participant privacy, all identifying information will be removed from the dataset, and data will be anonymized before analysis. Participants have the right to withdraw from the study at any point, with no consequences for their standard healthcare access. The study is committed to upholding the highest standards of ethical research conduct, ensuring that all participants are treated respectfully and that their health and personal information are safeguarded throughout the research process. Ethics approval has been obtained from the Peking Union Medical College Ethics Committee (approval number: CAMS\u0026amp;PUMC-IEC-2024-042). A continuing ethics review was completed in June 2025, and updated approval was granted under approval number CAMS\u0026amp;PUMC-IEC-2025-063.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlans for communicating important protocol amendments to relevant parties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Commission of the Medical Faculty of Peking Union Medical College was contacted and notified about any protocol amendments requiring their acceptance. Following the approval of the Ethics Committee and before implementation, all research members were informed about protocol amendments.\u003c/p\u003e\n\u003cp\u003eIn this trial, an unavoidable amendment was made to the primary outcome measures. Although outcome modifications are generally discouraged, the change was necessitated by a non-negotiable regulatory restriction. Specifically, we initially planned to measure carbon monoxide (CO) levels as an objective biomarker of smoking status. However, due to government regulations prohibiting the purchase of fixed assets, including CO monitoring devices, we were unable to acquire the necessary equipment. As a result, CO measurement was removed from the primary outcomes, and adjustments were made accordingly. These adjustments are reflected in the updated protocol. This amendment was approved by the Ethics Committee, and all research personnel were notified before implementation to ensure consistency in data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent and withdrawal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe provided written study information and the informed consent form to eligible individuals. The study manager explained the study\u0026rsquo;s aims and detailed procedures-in the presence of a witness if required. We provided sufficient time for our participants to decide whether or not to participate in the study. Participants were given the opportunity to inquire about details of the study, and responses were provided. For illiterate participants, we obtained a thumbprint signing and a witness\u0026rsquo;s signature to document consent before enrolment.\u003c/p\u003e\n\u003cp\u003eWe informed eligible participants about the risks and benefits of participation in our trial. A decision not to participate in this study will not bear any further consequences for the individual. Every participant is free to refuse or discontinue data collection at any stage. Importantly, participation does not require relinquishing any concomitant care. While there are no special criteria for modifying or discontinuing allocated interventions, we will document and report reasons for attrition in future publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfidentiality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the study, all data have been handled with complete confidentiality, and data collection has conformed to requirements of the national legislations on data protection. We are storing digital data on password-protected files in the Center of the Chinese Academy of Medical Sciences. Data are available exclusively to the research team members for complete confidentiality. Third parties may only receive anonymized data for research purposes. Informed consent forms, laboratory books, and other participant-related documents are safely being stored during the study\u0026rsquo;s conduct and will be stored subsequently at the Co-Principal Investigator\u0026rsquo;s office premises.\u003c/p\u003e"},{"header":"Discussion and policy implications","content":"\u003cp\u003eThis study protocol outlines a cRCT designed to evaluate the effectiveness of a population medicine multimorbidity intervention aimed at high-COPD-risk smokers in Xishui, China. The intervention seeks to address both the physical and mental health needs of this vulnerable population, integrating multimorbidity management into routine care. This approach is particularly timely and relevant, as tobacco use remains a leading preventable risk factor for NCDs in China, and COPD continues to place a significant burden on the healthcare system.\u003c/p\u003e\n\u003cp\u003ePopulation medicine emphasizes proactive strategies for identifying and managing high-risk groups. Evidence suggests that smokers with COPD face unique challenges in quitting, including a longer history of smoking, lower self-efficacy, and greater psychological dependence.\u003csup\u003e49\u003c/sup\u003e These factors make it more difficult for COPD smokers to quit compared to smokers without the condition. By combining digital health tools with personalized, multifaceted support, this study aims to provide a more effective intervention for high-COPD-risk smokers, addressing the unique physical and psychological challenges they face.\u003c/p\u003e\n\u003cp\u003eHigh-risk smokers not only face elevated risks of COPD but are also disproportionately affected by hypertension, type 2 diabetes, depression, anxiety, and other modifiable chronic diseases. These conditions often interact synergistically, amplifying disease burden and complicating clinical management. Traditional vertical disease-specific programs are ill-equipped to manage this complexity, particularly in rural communities where care is fragmented and specialist access is limited. By targeting multiple chronic conditions within a unified care framework, our intervention aligns with emerging global strategies that call for integrated, person-centered approaches to multimorbidity management.\u003c/p\u003e\n\u003cp\u003eA key feature of this intervention is its alignment with a broader shift in healthcare\u0026mdash;from treating isolated illnesses in individual patients to managing health holistically at the population level. This evolving perspective calls for the early identification of high-burden conditions and a focused effort on those most at risk, such as individuals living with chronic diseases in rural areas. Realizing this vision also depends on enabling physicians to act not only as caregivers to individuals, but also as stewards of community health\u0026mdash;supported by systems like pay-for-population that align provider incentives with measurable improvements in population health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe integrated nature of this intervention, which combines behavioral, medical, and digital health strategies, holds promise for addressing the multifaceted needs of high-COPD-risk smokers. Smoking cessation remains one of the most effective strategies for reducing COPD-related mortality and morbidity.\u003csup\u003e50\u003c/sup\u003e By offering both digital interventions like NicQuit and EmoEase alongside in-person health education and support, this study aligns with emerging trends in utilizing mobile health technologies to improve health outcomes in resource-limited settings. The adoption of digital tools has shown to be an effective method for increasing accessibility to smoking cessation resources, especially in rural communities where traditional healthcare infrastructure may be limited.\u003c/p\u003e\n\u003cp\u003eOne of the major strengths of this study is its comprehensive, community-based intervention, which combines medical management with health behavior change strategies to target both the underlying causes and the clinical manifestations of COPD. The use of a cRCT design allows for robust evaluation of the intervention\u0026apos;s impact at the population level, with the potential to inform public health strategies in rural areas of China. This is particularly important in regions like Xishui, where healthcare access may be limited and where public health interventions targeting high-risk populations are essential for improving outcomes. Furthermore, this study addresses a significant gap in the existing literature, as interventions for high-COPD-risk smokers in China remain scarce. The integration of a multimorbidity intervention could lead to more sustainable improvements in health outcomes, not just for COPD, but also for related comorbidities that disproportionately affect smokers.\u003c/p\u003e\n\u003cp\u003eHowever, there are potential challenges both to implementing the intervention and evaluating its effectiveness. First, the intervention\u0026apos;s success depends on participant engagement, particularly among older populations who may struggle with technology or behavioral change. The digital components of the intervention, if not adequately tailored, may face resistance or low uptake, especially in rural settings. Furthermore, the study\u0026rsquo;s results may be influenced by local contextual factors such as healthcare infrastructure and socioeconomic status, which could limit the generalizability of the findings beyond Xishui. Additionally, while the study\u0026apos;s design is rigorous, it is important to acknowledge that achieving high compliance rates for both the intervention and follow-up assessments could be challenging, especially in a community setting. Addressing barriers to adherence will be crucial for ensuring that the study\u0026apos;s findings are meaningful and reliable.\u003c/p\u003e\n\u003cp\u003eThis protocol outlines a promising intervention aimed at reducing the burden of COPD and tobacco-related diseases in China, particularly among high-risk smokers in Xishui. This study is timely, as it directly supports China\u0026rsquo;s national public health strategy by focusing on the prevention and management of COPD, which was integrated into the National Essential Public Health Service in 2024.\u003csup\u003e51\u003c/sup\u003e By targeting multiple health outcomes through a multi-component, community-level intervention, this study offers a potentially scalable model for addressing the needs of individuals with COPD and related\u0026nbsp;co-occurring conditions. The findings from this trial could have significant implications for public health policy and the management of chronic diseases in underserved populations, especially in rural areas. The intervention could provide evidence for incorporating similar multimorbidity management strategies into routine care for COPD patients in China and beyond. This study also has the potential to inform broader efforts to reduce smoking rates and improve mental health outcomes in high-risk populations, ultimately contributing to the reduction of the substantial health and economic burden caused by tobacco-related diseases.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on expanding this intervention to other regions, particularly those with different healthcare contexts, to validate its effectiveness across diverse settings. Additionally, further evaluation of the cost-effectiveness of this intervention will be essential for determining its scalability and sustainability in resource-limited areas. Ultimately, this study represents an important step toward improving the health outcomes of high-COPD-risk smokers and contributing to the ongoing fight against tobacco-related diseases in China.\u003c/p\u003e\n\u003cp\u003eBuilding on these findings, we hope this study will catalyze a paradigm shift in the role of primary and community health workers. In conventional health systems, frontline providers often operate passively, waiting for patients to present at clinics. This trial introduces mechanisms such as pay-for-population, performance-linked process metrics, and home-based outreach to actively engage providers in population screening, health promotion, and continuous care. This transformation\u0026mdash;from a patient-centered, reactive model to a proactive, population-centered approach\u0026mdash;is at the heart of population medicine. We believe this shift has the potential to reshape public health governance not only in China but also in other low- and middle-income countries striving for equitable, sustainable health system reform.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis protocol presents a multicomponent intervention for high-COPD-risk smokers, combining smoking cessation, COPD management, mental health support and weight and chronic disease management. Using a cRCT design, the study aims to provide robust evidence on the effectiveness of this multimorbidity intervention in rural China. If successful, it could serve as a model for future population medicine strategies and inform policy decisions for managing COPD and tobacco-related diseases. Further research should explore the cost-effectiveness and scalability of this intervention to determine its long-term impact and broader applicability across different healthcare contexts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACT Asthma Control Test\u003c/p\u003e\n\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\n\u003cp\u003eCAT COPD Assessment Test\u003c/p\u003e\n\u003cp\u003eCBT cognitive behavioral therapy\u003c/p\u003e\n\u003cp\u003eCDC Center for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCO Carbon Monoxide\u003c/p\u003e\n\u003cp\u003eCOPD chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eCOPD-SQ chronic obstructive pulmonary disease screening questionnaire\u003c/p\u003e\n\u003cp\u003ecRCT cluster-randomized controlled trial\u003c/p\u003e\n\u003cp\u003eCT computed tomography\u003c/p\u003e\n\u003cp\u003eDALY diasability-adjusted life years\u003c/p\u003e\n\u003cp\u003eDSMB data and safety monitoring board\u003c/p\u003e\n\u003cp\u003eECRHS European Community Respiratory Health Survey\u003c/p\u003e\n\u003cp\u003eEDC electronic data capture\u003c/p\u003e\n\u003cp\u003eEQ-5D 5L The 5-dimension, 5-level version of EuroQol\u003c/p\u003e\n\u003cp\u003eFEV1 forced expiratory volume in 1 second\u003c/p\u003e\n\u003cp\u003eFTND Fagerstr\u0026ouml;m Test for Nicotine Dependence\u003c/p\u003e\n\u003cp\u003eFVC forced vital capacity\u003c/p\u003e\n\u003cp\u003eGAD-7 General Anxiety Disorder-7\u003c/p\u003e\n\u003cp\u003eHBP high blood pressure\u003c/p\u003e\n\u003cp\u003eHSI Heaviness of Smoking Index\u003c/p\u003e\n\u003cp\u003eICC intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003eINT\u003cspan\u003e$\u003c/span\u003e international dollar\u003c/p\u003e\n\u003cp\u003eITT intention-to-treat\u003c/p\u003e\n\u003cp\u003eMDD minimum detectable differences\u003c/p\u003e\n\u003cp\u003emMRC Modified Medical Research Council\u003c/p\u003e\n\u003cp\u003eNCD non-communicable disease\u003c/p\u003e\n\u003cp\u003ePAKQ Patient-completed Asthma Knowledge Questionnaire\u003c/p\u003e\n\u003cp\u003ePCCM Pulmonary and Critical Care Medicine\u003c/p\u003e\n\u003cp\u003ePFT pulmonary function test\u003c/p\u003e\n\u003cp\u003ePHQ-9 Patient Health Questionnaire-9 items\u003c/p\u003e\n\u003cp\u003ePI principal investigator\u003c/p\u003e\n\u003cp\u003ePOPMIX Population Medicine Multimorbidity Interventions in Xishui\u003c/p\u003e\n\u003cp\u003eSES socioeconomic status\u003c/p\u003e\n\u003cp\u003eSGRQ Saint George Respiratory Questionnaire\u003c/p\u003e\n\u003cp\u003eSPIRIT Standard Protocol Items: Recommendations for Interventional Trials\u003c/p\u003e\n\u003cp\u003eT2DM Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eWEMWBS Warwick-Edinburgh Mental Well-being Scale\u003c/p\u003e\n\u003cp\u003eWPAI-GH Work Productivity and Activity Impairment-General Health\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Peking Union Medical College (Approval No.: CAMS\u0026amp;PUMC-IEC-2024-042). A continuing ethics review was completed and approved in June 2025 under updated approval number CAMS\u0026amp;PUMC-IEC-2025-063. All procedures were carried out in accordance with relevant national and international guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trial status is active at the moment of submitting the protocol. Recruitment started on June 17, 2024, data collection of the trial will end on December 31, 2025.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the CAMS Innovation Fund for Medical Sciences (Project Number 2023-I2M-2-001), the National Science and Technology Major Project of Ministry of Science and Technology of China (Project Number 2023ZD0506002). AstraZeneca China (Project No. 2024-HX-22), the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2022-ZHCH330-01), and the State Key Laboratory Special Fund (Project Number 2060204). Further funding was contributed by the Tencent Sustainable Social Value Inclusive Health Lab (Project Number SSVPJ202307060001), the EU Horizon Europe Programme (HORIZON-MSCA-2021-SE-01; Project Number 101086139-PoPMeD-SuSDeV), and the China Medical Board (Grant #22-469 to SC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSC, KH, ZZ, and YL are equally contributed and jointly listed as co-first authors. TB, SC, TY, and CW are listed as the co-senior authors. CW, SC, and TY are listed as corresponding authors. CW, TY, and SC conceived the idea of the trial. CW, TY, SC, KH, ZZ, and YL devised the study design and methodology and initiated the project. YL, ZZ, SZ, YW, TZ, XYT, ZC, JZ, LH, LJ, YCL, QL, XLyu, RD executed all field activities in Xishui County, Guizhou Province, China with input from CW, TY, SC, and LT. KH, XLT, YL, and ZZ managed and prepared study data for analysis. KH, YL, WC, and SZ developed to sub-study on COPD, mental health, and asthma. YL and ZZ wrote the first draft of the manuscript with input from CW, SC, TY, KH, TB, QC, AB, SV, PG, DJ, and all co-authors. All authors contributed to revising the manuscript. All authors have contributed to this protocol manuscript according to the International Committee of Medical Journal Editors\u0026rsquo; guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College, Beijing, China\u003c/p\u003e\n\u003cp\u003eSimiao Chen, Zhoutao Zheng, Yuhao Liu, Wenjin Chen, Liu He, Till B\u0026auml;rnighausen \u0026amp; Chen Wang\u003c/p\u003e\n\u003cp\u003eHeidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany\u003c/p\u003e\n\u003cp\u003eSimiao Chen, Zhong Cao, Jinghan Zhao, Aditi, Bunker \u0026amp; Till B\u0026auml;rnighausen\u003c/p\u003e\n\u003cp\u003eState Key Laboratory of Respiratory Health and Multimorbidity, Beijing, China\u003c/p\u003e\n\u003cp\u003eSimiao Chen, Ke Huang, Ting Yang \u0026amp; Chen Wang\u003c/p\u003e\n\u003cp\u003eDepartment of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China\u003c/p\u003e\n\u003cp\u003eKe Huang, Xingyao Tang, Ting Yang \u0026amp; Chen Wang\u003c/p\u003e\n\u003cp\u003eNational Center for Respiratory Medicine, Beijing, China\u003c/p\u003e\n\u003cp\u003eKe Huang, Xingyao Tang, Ting Yang \u0026amp; Chen Wang\u003c/p\u003e\n\u003cp\u003eSchool of Health Policy and Management, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College, Beijing, China\u003c/p\u003e\n\u003cp\u003eShiyu Zhang\u003c/p\u003e\n\u003cp\u003eGuizhou Medical University, Guiyang, Guizhou, China\u003c/p\u003e\n\u003cp\u003eLei Tang\u003c/p\u003e\n\u003cp\u003eDepartment of Pulmonary and Critical Care Medicine, National Center of Gerontology, Beijing Hospital, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China\u003c/p\u003e\n\u003cp\u003eXunliang Tong\u003c/p\u003e\n\u003cp\u003eDepartment of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA\u003c/p\u003e\n\u003cp\u003eLirui Jiao\u003c/p\u003e\n\u003cp\u003eCenter for Disease Control and Prevention of Xishui County, Zunyi, Guizhou, China\u003c/p\u003e\n\u003cp\u003eYingping Wang, Tianying Zhao \u0026amp; Yingchi Luo\u003c/p\u003e\n\u003cp\u003eDepartment of Pulmonary and Critical Care Medicine, People\u0026rsquo;s Hospital of Xishui County, Zunyi, Guizhou, China\u003c/p\u003e\n\u003cp\u003eQiande Lai \u0026amp; Xiangqin Lyu\u003c/p\u003e\n\u003cp\u003eThe Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, State College, PA, USA\u003c/p\u003e\n\u003cp\u003eQiushi Chen\u003c/p\u003e\n\u003cp\u003eDepartment of Economics and Centre for Modern Indian Studies, University of Goettingen, G\u0026ouml;ttingen, Germany\u003c/p\u003e\n\u003cp\u003eSebastian Vollmer\u003c/p\u003e\n\u003cp\u003eDivision of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA\u003c/p\u003e\n\u003cp\u003ePascal Geldsetzer\u003c/p\u003e\n\u003cp\u003eDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA, USA\u003c/p\u003e\n\u003cp\u003ePascal Geldsetzer\u003c/p\u003e\n\u003cp\u003eDepartment of Epidemiology and Biostatistics and Institute for Global Health Sciences, University of California, San Francisco, CA, USA\u003c/p\u003e\n\u003cp\u003eDean Jamison\u003c/p\u003e\n\u003cp\u003eDepartment of Global and Population Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA\u003c/p\u003e\n\u003cp\u003eTill B\u0026auml;rnighausen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the People\u0026apos;s Government of Xishui County, Health Bureau of Xishui County, Youth League Committee of Guizhou Medical University, Medieco Group Co. einPaper Team, Finance Bureau of Xishui County, Health Insurance Bureau of Xishui County, Bureau of Sports and Education of Xishui County, Bureau of Statistics of Xishui County, Xishui Sub-Bureau of Ecology and Environment Bureau of Zunyi City, Water Authority of Xishui County, Bureau of Housing and Urban-Rural Development of Xishui County, Meteorological Bureau of Xishui County, Center for Disease Control and Prevention of Xishui County (Health Surveillance Station of Xishui County), People\u0026apos;s Hospital of Xishui County, Traditional Chinese Medicine Hospital of Xishui County, Maternal and Child Health Care Hospital of Xishui county, and the People\u0026apos;s Governments (Offices) of 26 Townships (Subdistricts) and Village Committees of Xishui County. YL acknowledges the support of the China Scholarship Council Program (Project ID: 202406210195).\u003c/p\u003e\n\u003cp\u003eWe thank the participants of the POPMIX study. For continuous support, assistance, and cooperation, we thank all field workers from Xishui County: Chun Zhang, Yuzhu Ye, Mingqiang Hou, Shengbo Liao, Qiande Lai, Xiangqin Lyu, Yuan Zhou, Dong Liu, Chentao Zhong, Yushuang Zhao, Hongxia Mu, Min Liu, Qiong Lyu, Zhengyu Zhang, Weiwei Li (People\u0026apos;s Hospital of Xishui County); Jian Wang, Tiansheng Lan, Tingyu Liu (Traditional Chinese Medicine Hospital of Xishui County); Xin Zhou (Maternal and Child Health Care Hospital of Xishui County); Bie Yu, Xianping Wang, Yingchi Luo, Yingping Wang, Tianying Zhao, Yansong Luo, Wenwu Jiang, Wen Zhang, Dongmei Wu, Qingping Luo, Dajun Rao, Cunkun Yang, Feng Zhao, Qin Wang, Linlin Zhang, Can Li, Min Chen, Yufeng Huang (Center for Disease Control and Prevention of Xishui County); Guanghai Jian, Li Wang, Yunjiang Zheng, Jianghong Linghu, Li Zhong, Xiaoling Ma, Bin Gui, Yan Cheng, Yu Yang, Jing Yan, Xixi Liao, Mingguo Xu, Zhijun Zhao, Qian Han, Panyan Wu, Dandan Tang, Qingqing Wang (Community Health Service Center of Jiulong Street, Xishui County); Li Feng, Lilan Wang, Lei Si, Peng Zeng, Jianxiu Tian, Weili Fan, Xin Luo, Sumei Feng, Rong Ding, Huadan Yi, Fengxue Lyu, Ludan Yuan, Yuan Liu, Xueshuang Wang, Aihong Duan, Yang Zhao, Yuping Luo, Yicheng Li (Community Health Service Center of Shanwang Street, Xishui County); Anchun Liang, Chengmei Li, Hu Tang, Anfeng Liu, Xu Wang, Yi Zhong, Wen Zheng, Benwei Zhao, Qianmei Zhang, Xiaojing Tu, Huanhuan Lu, Xiumei Zhao, Yan Chen, Xuemei Yuan, Yanhong Mu, Yao Zhao, Juan Zhang, Qinhui Li, Fuhui Wang, Xiaomin Mu, Yan Hu, Liuyang Chen (Community Health Service Center of Donghuang Street, Xishui County); Gang Lei, Zhufei Hu, Xiangyong Tang, Yan Luo, Shunxia Zhou, Ting Pan, Yuanyin Zhou, Xiaoqin Ren, Yaling Zhou, Jing Zeng, Keping Hu, Xiaoyu Liu, Deguo Yang, Qiong Wan, Cai Zhang, Heng He (Health Service Center of Malin Street, Xishui County); Chengping Wang (Village Clinics of Xiangyang, Malin Street, Xishui County); Yuanzhong Wang, Shuyin Wang (Village Clinics of Mianshan, Malin Street, Xishui County); Xuhong Lei (Village Clinics of Linfeng, Malin Street, Xishui County); Yushen Yuan (Village Clinics of Wuyi, Malin Street, Xishui County); Tailiang Luo (Village Clinics of Shanghua, Malin Street, Xishui County); Dazhou Zhu (Village Clinics of Miaoping, Malin Street, Xishui County); Anrong Luo (Village Clinics of MiaopingLinping, Malin Street, Xishui County); Xianmei Hu (Village Clinics of Wuyi, Malin Street, Xishui County); Ya Zhang, Ling Yuan, Senlin Luo, Weili Ma, Yonghua Cao, Yunpeng Cao, Taojin Chen, Wen Chen, Zhiqun Chen, Zhongyi Chen, Huagang Cheng, Xianpan Feng, Taimei Gao, Lizhu Jian, Renxian Jiang, Shengmei Kang, Hang Lei, Shanshan Liu, Ruxun Lu, Xiaojiao Luo, Dongshun Lyu, Guanghong Meng, Jianping Mu, Xuerou Mu, Hongxia Qian, Huimin Ren, Juan Wang, Li Wang, Loutao Wang, Qin Wang, Wei Wang, Ya Wang, Yan Wang, Dongjie Wu, Xiaoxia Xu, Jing Yang, Qian Yu, Dan Yuan, Yanlu Yuan, Yu Yuan, Hongying Yue, Yuanmin Yue, Li Zhang, Zhizhong Zhang, Jiaqin Zhao, Yuanxin Zhao, Yue Zhong, Lejie Zhou, Lufang Yang, Xue Chen, Lihui Ren (Health Center of Guandian Town, Xishui County); Guixiang Gong, Ping Shi, Jiangshun Tan, Zhonghang Yu, Lijun Mu, Meilin Chen, Su Yu, Lin Li, Xianbi Mu, Jianxia Wu, Mei Zhao, Jiangmei Chen, Jing Huang, Yanfang Li, Huayan Yang, Jiaheng Yang, Xingqi Yuan, Yunhao Zhou, Mengting Ye, Junlin Zhang, Taijun Luo, Shuai Wang, Ju Hu (Health Center of Zhaiba Town, Xishui County); Gang Yuan (Village Clinics of Shangba, Zhaiba Town, Xishui County); Shili Mu (Village Clinics of Youyi, Zhaiba Town, Xishui County); Zhongxiang Yuan, Huili He (Village Clinics of Fenghuang, Zhaiba Town, Xishui County); Jiali Li (Village Clinics of Guiyuan, Zhaiba Town, Xishui County); Chunlan Liu (Village Clinics of Yongsheng, Zhaiba Town, Xishui County); Kailiang Cheng (Village Clinics of Hexin, Zhaiba Town, Xishui County); Quan Liu (Village Clinics of Tiaotai, Zhaiba Town, Xishui County); Huade Yang (Village Clinics of Fuxing, Zhaiba Town, Xishui County); Shaojun Zhang (Village Clinics of Xiyuan, Zhaiba Town, Xishui County); Bo Chen (Village Clinics of Sanlian, Zhaiba Town, Xishui County); Guishan Ying (Village Clinics of Lingxianhe, Zhaiba Town, Xishui County); Xumei Zhao (Village Clinics of Xianfeng Jiedao, Zhaiba Town, Xishui County); Zhi He (Village Clinics of Tiaotai, Zhaiba Town, Xishui County); Gan Feng, Huanhuan Guo, Hongling Yuan, Fuyou Yuan, Wei Wang, Lang Chen, Liping Zhao, Jiajun Ning, Jie Feng, Yuxian Chen, Jin Chen, Jing Qi, Yuanting Li, Lingli Zhao, Guo Wang, Ziyi Wang, Huaishu Zhong (Health Center of Erlang Town, Xishui County); Jie Wang, Gang Chen, Xueqin Mu, Yuanfeng Gong, Xinan Lu, Zhengli Chen, Tianwei Chen, Xiaoping Chen, Runqin Mu, Xiaoyi Deng, Yu Duan, Xiujuan Dong, Wangyong Yan (Health Center of Niba Township, Xishui County); Jiayong Rao (Village Clinics of Feilongshan, Niba Township, Xishui County); Mingfang Wang (Village Clinics of Nantianmen, Niba Township, Xishui County); Xiangcai Chen (Village Clinics of Baziqiao, Niba Township, Xishui County); Zhongquan Wang (Village Clinics of Xiaoguchi, Niba Township, Xishui County); Lanfang Zhang (Health Center of Niba Township, Xishui County); Jianghua Luo, Kaijie Huang, Ye Chen, Jin\u0026apos;e Li, Li Huang, Ju Chen, Hao Huang, Qingqun Long, Yan Chen, Ling Jiang, Man Ding, Xiaolin Yu, Tongjie Wei, Zhengqin Liu (Health Center of Xijiu Town, Xishui County); Kaifu Si, Yuxian Mu, Yan Huang, Qian Yang, Tao Xu, Xiaorong Yuan, Cailun Zhao, Minli Chen, Rongfang Luo, Ling Chen, Ying Zhang, Guiwei Yuan, Qianyi Shui, Yong Wu, Qian Huang, Changli Wu, Nan Zhang, Chaojiang Wu, Defei Chen, Wu Xu, Linhui He, Wanlun Zhang, Jinsong Chen, Jin Yuan (Health Center of Sanchahe Town, Xishui County); Rujun Li, Minghe Wang, Chunmei Zhang, Yueshan Wang, Lang Liao, Anqian Feng, Dengyang Wang, Zelin Deng, Hupiao Yang, Kaimeng You, Lijuan Deng, Qiong Huang, Xiaoqing Zhang, Zhengqian Zhu (Health Center of Yong\u0026apos;an Town, Xishui County); Xiaoyan Liao, Yongbo Song, Yushui Yuan, Qinghong Cai, Lu Liu, Hong Yu, Xue Luo, Xiaoshan Huang, Qin Hu, Dian Yuan, Zengye Zhao, Jiemin Zeng, Zhaoyan Huang, Peng Luo, Taibo Luo, Wenwu Wu, Guangni Liu, Gengning Zhang (Health Center of Tongmin Town, Xishui County); Mingyuan Ren (Village Clinics of Chaya, Tongmin Town, Xishui County); Mei Yang (Village Clinics of Shengli, Tongmin Town, Xishui County); Zhongjin Wang (Village Clinics of Hongqi, Tongmin Town, Xishui County); Rongping Yuan (Village Clinics of Linjiang, Tongmin Town, Xishui County); Yunfeng Yuan (Village Clinics of Shengli, Tongmin Town, Xishui County); Anquan Yuan (Agricultural Machinery Station Clinic of Tongmin Village, Tongmin Town, Xishui County); Yunqiang Chen (Tongxin Clinic of Tongmin Village, Tongmin Town, Xishui County); Hongxian Liu (Village Clinics of Tongmin Village, Tongmin Town, Xishui County); Xingyu He (Changhong Clinic of Tongmin Village, Tongmin Town, Xishui County); Tongxiang Zhang (Village Clinics of Xinglong Village, Tongmin Town, Xishui County); Mingkai Xu (Taiping Clinic of Chang\u0026apos;an Village, Tongmin Town, Xishui County); Minqing Hu (Village Clinics of Chang\u0026apos;an Village, Tongmin Town, Xishui County); Chunyi Qian, Yunfen Zhang (Committee of Chang\u0026apos;an Village, Tongmin Town, Xishui County); Yueguang Lei, Jun Wang, Xiaoju Yuan, Changxian Yang, Ai Xiang, Minjing Zhao, Xiaomei Ren, Guimei Li, Huiying Ren (Health Center of Liangcun Town, Xishui County); Ju Teng, Yu Chen, Jing Yuan, Shunfeng Mu, Xiangyu Zhang, Ye Li, Yuwei Sun, Shangjing Wang, Fangping He (Health Center of Sangmu Town, Xishui County); Guoxiang Wang (Village Clinics of Dangba, Sangmu Town, Xishui County); Wei Cai (Village Clinics of Shangba, Sangmu Town, Xishui County); Weijiang Tian (Village Clinics of Tuhe, Sangmu Town, Xishui County); Tingfen Cai (Village Clinics of Dashan, Sangmu Town, Xishui County); Zhao Chen (Village Clinics of Heshan, Sangmu Town, Xishui County); Dakai Mu (Baimu Clinic of Gonghe Village, Sangmu Town, Xishui County); Yufu Mu (Village Clinics of Gonghe Village, Sangmu Town, Xishui County); Cai Cheng (Village Clinics of Xiangshu Village, Sangmu Town, Xishui County); Xiaofang Pan (Village Clinics of Yinchang Village, Sangmu Town, Xishui County); Fang Wang (Village Clinics of Senlin Village, Sangmu Town, Xishui County); Mingqian Zhao (Village Clinics of Tongjuan Village, Sangmu Town, Xishui County); Qijun Liu, Bangqi Yang, Ye Yu, Ting Min, Xiaoya Wang, Jun Li, Yang Zhang, Yan Wang, Lihong Zhao, Zhihua Luo, Hao Yuan, Huihua Ruan, Jian Wu, Chengfen Wang, Dezhi Zhang, Yunfang Zhang (Health Center of Minhua Town, Xishui County); Weining Ao, Pan Zhao, Yunxia Liang, Yicheng Zhao, Xiaosong Mu, Lyufang Luo, Ziyi Zhang, Wei Huang, Jun Cao, Huan Li, Jun Liu, Liye Yuan, Na Zhao, Ju Wang, Qiuyan Duan, Weizhong Chen, Lirong Zhao, Jiangbo Wu, Liqin Deng (Health Center of Shuanglong Township, Xishui County); Kunyu Wang, Zhengguo Yan, Chaolian Wang, Can Liu, Quan Cao, Gang Ma, Youyong Lu, Yuan Zhang, Kaijing Xiang, Yixiang Wang, Xingye Zhu, Jiamei Wang, Demin Hu, Dongmin Li, Peng Chen, Chao Tian, Lu Wang, Mei Wang, Lulu Xiao, Lingling Yu, Min Wei, Chengcheng Yang, Limei Zhang, Rong Zhao, Chongguang Zhong, Yan Xiao (Health Center of Xianyuan Town, Xishui County); Jian Luo, Hongfa Chen, Pengju Dai, Jin Liu, Yingtao Luo, Guangjie Ma, Shiyou Mu, Zhengli Rao, Deqin Wen, Guowang Wu (Village Clinics of Xianyuan Town, Xishui County); Yong Hu, Xiaorong Shi, Shaofen Huang, Ya Chen, Wenmao Zhang, Min Chen, Xin Luo, Hongqiong Ruan, Liming Liu, Lihui Fan, Xiaoyan Zou, Huizhi He, Xinggao Yang (Health Center of Longxing Town, Xishui County); Linfei Wang (Village Clinics of Xinguang, Longxing Town, Xishui County); Hong Wang (Village Clinics of Linchan, Longxing Town, Xishui County); Wenxiang Li (Village Clinics of Yongsheng, Longxing Town, Xishui County); Jialian Wu (Village Clinics of Taoguan, Longxing Town, Xishui County); Jinyong Yang (Community Clinic of Longxing, Longxing Town, Xishui County); Jianying Wu (Village Clinics of Taoguan, Longxing Town, Xishui County); Guangliang Liu (Village Clinics of Linchan, Longxing Town, Xishui County); Xiaolin Xiong (Village Clinics of Binjiang, Longxing Town, Xishui County); Gang Yang (Village Clinics of Yongsheng, Longxing Town, Xishui County); Ping Feng (Village Clinics of Gantian, Longxing Town, Xishui County); Cheng Luo (Village Clinics of Longxi, Longxing Town, Xishui County); Qiwei Zhang (Village Clinics of Xinguang, Longxing Town, Xishui County); Qihui Yang, Taoshan Ni, Rensong Tian, Li Zhang, Yuanmei Zhang, Hui Bi, Bijue Huang, Yu Yan, Shan Chen, Xinghui Chen, Jiangfei Wang, Guanglun Zou, Junting Liu, Rongjiang Zhao, Li Yuan (Health Center of Taolin Town, Xishui County); Ruyin Yuan, Jianbing Li, Aiping Wang, Daijie Wang, Mei Yang, Kaiyan Si, Peng Wang, Yan Linghu, Tuye Yuan, Zhiyuan Yuan, Weiwei Qian, Daimei Wang, Rongqin Ren, Dong Dai, Qianhua Deng, Yan Xu, Jiali Zhang, Qingyan Bai, Yingwen Zhang, Xingke Wang, Qiang Liu, Liyuan Feng, Fancha Wang, Shiwen Wang, Xiangguang Liu, Runping Liu, Xiaoying Wang, Daizhi Wang, Huihai Cao, Jiang Yuan, Qinhui Cao, Min Luo (Health Center of Xingmin Town, Xishui County); Hao Hou, Lu Kong, Zhu Ren, Yu Zhou, Xiaoyan Yuan, Hong Liu, Shixian Gong, Yonghong Zhao, Rusheng Wang, Ji Chen (Health Center of Dapo Town, Xishui County); Fuping Bai, Yang Zhou, Wenbi Chen, Yu Sun, Qunying Xu, Gang Zheng, Yong Wang, Juan Chen, Ling Zhong, Ji Ren, Qiling Zhu, Zheng Yang, Yongsong Chen, Zhonghui Huang, Xiaojiang Zhang, Junyan Yu, Panting Yu, Qun Wang, Qimei Yu, Hao Zhou, Yuanmei Lu, Yiyu Wang, Min Huang, Feng Wu (Health Center of Erli Town, Xishui County); Jiangyu Yuan, Guangjun Xiong, Jian Zhang, Hong Zhou, Fenfang Liu, Yuqin Chen, Xiaoli Ye, Qingli Luo, Qijie Dai, Yunxian Feng, Tianmei Huang, Yunfeng Xia, Lei Zhao, Anshui Wang, Fuxiu Yang, Minqin Hu, Tingting Yuan, Song Zhou (Health Center of Tucheng Town, Xishui County); Lin Yuan (Community Clinic of Changzheng, Tucheng Town, Xishui County); Mingjian Yuan (Community Clinic of Tuanjie, Tucheng Town, Xishui County); Maozhao Wen (Village Clinics of Huangjinwan, Tucheng Town, Xishui County); Hongming Zhao (Village Clinics of Gaoping, Tucheng Town, Xishui County); Hongbo Zhao (Village Clinics of Qunfeng, Tucheng Town, Xishui County); Yuanchao Zhao (Village Clinics of Shuishiba, Tucheng Town, Xishui County); Binyuan Yuan (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Hua Xiong (Village Clinics of Wuxing, Tucheng Town, Xishui County); Qiyong Yuan (Village Clinics of Qixin, Tucheng Town, Xishui County); Qin Yuan (Village Clinics of Xingfu, Tucheng Town, Xishui County); Xingqin Zhao (Village Clinics of Hongwei, Tucheng Town, Xishui County); Qifu Luo (Village Clinics of Hongwei, Tucheng Town, Xishui County); Qigui Luo (Village Clinics of Tongxin, Tucheng Town, Xishui County); Tulin Yuan (Village Clinics of Changba, Tucheng Town, Xishui County); Xianglin Huang (Village Clinics of Honghua, Tucheng Town, Xishui County); Chun Luo (Village Clinics of Jiulongtun, Tucheng Town, Xishui County); Dehong Wang (Village Clinics of Tianxingqiao, Tucheng Town, Xishui County); Rongwei Zhang (Village Clinics of Qianyan, Tucheng Town, Xishui County); Xingji Chen (Village Clinics of Tongyi, Tucheng Town, Xishui County); Zhong\u0026apos;en Wang (Village Clinics of Tianxingqiao, Tucheng Town, Xishui County); Deping Yuan (Village Clinics of Wuxing, Tucheng Town, Xishui County); Qihua Luo (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Huahua Li (Village Clinics of Qinggangpo, Tucheng Town, Xishui County); Yueming Wang, Jian Chen, Wei Teng, Jisheng Wang, Mingli He, Shan Wu, Junjun Xia, Tingxian Mao, Tingmin Luo, Sihong Yi, Ruixue Li, Kang Yang (Health Center of Chengzhai Town, Xishui County); Chengshang Ao, Xu Zhong, Yang Liu, Langsha Chen, Huan Luo, Qingxia Wang, Chen Chen, Wenqiang Wu, Yumei Wu, Limei Feng, Caixian Lei, Kai Li, Youqun Chen (Health Center of Huilong Town, Xishui County); Lulu Zhao, Han Ren, Juhang Yang, Shanshan Luo, Xiaoyi Zhang, Huiqin Yuan, Li Chen, Guishuang Liu, Jiakuan Ren, Dongqiao Jiang, Yi Yuan, Dengmin Ren, Xiaojiang Mu, Xue Yang (Health Center of Wenshui Town, Xishui County).\u003c/p\u003e\n\u003cp\u003eWe also showed sincere gratitude to field workers who dedicated to data quality controls from Guizhou Medical University: Wuyao He, Wanyi Hu, Piaopiao Huang, Guijiang Li, Jinman Li, Suosuo Li, Yiling Lu, Yuqi Lu, Huan Luo, Qingqing Luo, Wenqin Mu, Daolin Qin, Jinlan Quan, Yingying Tang, Junbao Wang, Lingling Wang, Rui Xu, Chenxi Yang, Dengqin Yang, Liqin Yang, Qingting Zhang, Lei Zhao, Lin Zhao, Jingmei Zhou, Lijuan Zuo, Xiaoning Xia, Yujia Xie, Hangyu Chen, Li Yang, Yu Zhao, Sisi Li, Juan Pan, Ju Yang, Minjia Zhao, Meiyuan Zhou, Guimei Hu, Chunqin Huang, Haiyan Long, Zeyuan Luo, Donghua Pan, Xuelang Ying, Yanyan Zhang, Yuting Zhang, Hong Zhao, Yunli Zhou, Bijin Zhu (School of Public Health, Guizhou Medical University); Kang Chen, Kaidi Ding, Jiacheng Dong, Tai Fu, Qin Li, Zaijing Liu, Siqi Huang, Lingli Ou, Zhuoting Tan, Yan Tang, Qingzhong Wu, Xinyi Xu, Bingyang Xu, Yulin Zhang, Shuangshuang Zhou, Zena Zhu, Jiancao Zuo, Guangxianfeng Chen, Jiahui Luo, Zhiqi Duan, Shangrong Gu, Binyao Ou, Xingtai Zhao (School of Clinical Medicine, Guizhou Medical University); Yanran He, Xingli Liao, Shunxin Shi, Cheng Wang, Jiayu Wang, Rongmei Wang, Ruirui Zhang, Yuhan Zhou, Serena Luo, Jiwei Jin, Xiaoqi Shi, Ziqianqian Yang, Xiaoxue An, Jiwei Jin, Mingli Huang, Yinghua Mu, Xiaozheng Wang, Songlin Zhang, Xue Zhou, Ruirui Ao, Shaohuan Bao, Yating Jiang, Yijun Liu, Zhirui Nie, Dingyuan Rao, Jiangqin Wan, Xue Wang, Die Wei, Lijin Xie, Jianan Xu, Xuerong Zhang, Die Deng, Jingyu Fu, Shunli Guo, Binhan He, Sitong Liu, Lin Liu, Linqu Qin, Teng Tian, Kehao Wang, Qirui Wu, Laitian Ye, Xiao Zhang, Tiantian Zhou (School of Basic Medicine, Guizhou Medical University); Sitong Zhou, Jiajia Liu, Huali Yin, Yue Luo (School of Anesthesiology, Guizhou Medical University); Jianxiu Bai, Yuchan Li, Xinglian Zhang, Shenghui Zhu, Xue Chen, Dan Jiang (School of Nursing, Guizhou Medical University); Xiaoqin Yang, Changnan Zhao, Fangjing Geng (School of Medical Imaging, Guizhou Medical University); Jia Li, Jie Wang, Wenfan Hu (School of Stomatology, Guizhou Medical University); Xingling Li, Yumei Lu, Zhuying Long, Junxue Qian, Guoxian Shi, Ruoxi Wang, Guangjian Wu, Guangyan Zhang (School of Pharmaceutical Sciences, Guizhou Medical University); Rongrong Zhou, Hongyu Hu, Zhaoyi Ren, Lingxuan Shi, Meng Zeng, Shengsheng Li, Ying Qin, Yi Zhou (School of Biology and Engineering [School of Health Medicine Modern Industry]\u0026zwnj;, Guizhou Medical University); Tianyu Liu (School of Marxism, Guizhou Medical University); Jia He, Jinan Li, Mingyuan Mao, Jiayi Wang (School of Medicine and Health Management, Guizhou Medical University); Ronghui Ma (School of Humanities, Guizhou Medical University); Bin Li (School of Forensic Medicine, Guizhou Medical University).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang C, Chen S, Shan G, Leng Z, Barnighausen T, Yang W (2022) Strengthening population medicine to promote public health. 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Lancet Reg Health West Pac 24:100464\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen S, Chen W, Li Y et al (2024) Effectiveness and cost-effectiveness of an integrated digital psychological intervention (EmoEase) in Chinese chronic obstructive pulmonary disease patients: Study protocol of a randomized controlled trial. Digit Health 10:20552076241277650\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO (2010) Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. World Health Organ\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO (2006) Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeatherton TF, Kozlowski LT, Frecker RC, FAGERSTROM KO (1991) The Fagerstr\u0026ouml;m test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86(9):1119\u0026ndash;1127\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang CL, Lin HH, Wang HH (2006) Psychometric evaluation of the Chinese version of the Fagerstrom Tolerance Questionnaire as a measure of cigarette dependence. J Adv Nurs 55(5):596\u0026ndash;603\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeatherton TF, Kozlowski LT, Frecker RC, Rickert W, Robinson J (1989) Measuring the heaviness of smoking: using self-reported time to the first cigarette of the day and number of cigarettes smoked per day. Br J Addict 84(7):791\u0026ndash;800\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo N, Li M, Liu GG, Lloyd A, de Charro F, Herdman M (2013) Developing the Chinese version of the new 5-level EQ-5D descriptive system: the response scaling approach. 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A comparative study of smokers with and without COPD regarding factors associated with tobacco smoking. Respiration 90(3):211\u0026ndash;219\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Chronic Obstructive Lung Disease (2024) Global strategy for prevention, diagnosis and management of COPD: 2025 Report\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeneral Office of the National Health Commission. Notice of the General Office of the National Health Commission on the Issuance of the Health Care Standards for Chronic (2024) Obstructive Pulmonary Disease Patients (Trial Implementation). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/zhengce/zhengceku/202409/content_6974437.htm\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/zhengce/zhengceku/202409/content_6974437.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"School of Population Medicine and Public Health, Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Population medicine, Smoking cessation, Multimorbidity, Chronic obstructive pulmonary disease, Digital health, Primary care, Pay-for-population","lastPublishedDoi":"10.21203/rs.3.rs-8128593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8128593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTobacco use is a major contributor to the burden of chronic obstructive pulmonary disease (COPD) and other non-communicable diseases (NCDs) in China. High-risk smokers\u0026mdash;particularly those with pre-existing chronic conditions\u0026mdash;often remain underserved by conventional smoking cessation programs. Population medicine offers a promising framework for proactively identifying high-burden diseases, managing multimorbidity, and prioritizing interventions for vulnerable populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis protocol describes a stratified, two-arm cluster randomized controlled trial (cRCT) conducted in Xishui County, Guizhou Province. A total of 26 townships were stratified by population size and randomly assigned in a 1:1 ratio to receive either a multi-component intervention or usual care. Eligible participants were high-COPD-risk smokers aged 35 years or older, screened using the COPD Screening Questionnaire. The intervention includes digital smoking cessation and mental health support, community-based spirometry, tailored chronic disease management, and a pay-for-population mechanism incentivizing providers. Primary outcomes are smoking amount and nicotine dependence, and secondary outcomes include COPD-related health outcomes, hypertension, diabetes, health risk behaviors, quality of life, healthcare utilization, and productivity loss. Follow-up occurs at three, six, and 12 months.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e\u003cp\u003eThe trial addresses a critical gap in tobacco-related NCD prevention in rural China. By combining behavioral, clinical, and digital health components, and by integrating incentive-aligned delivery through pay-for-population, the intervention aims to demonstrate a scalable, sustainable population medicine strategy. The focus on multimorbidity and early intervention among high-COPD-risk smokers reflects an essential evolution in rural public health practice.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eThis trial was registered at clinicaltrials.gov. ClinicalTrials.gov Identifier: NCT06458205. Registered on June 9, 2024.\u003c/p\u003e","manuscriptTitle":"Impact of POPulation Medicine Multimorbidity Intervention in Xishui County (POPMIX) on people at high risk for COPD who smoke: Protocol of the POPMIX-Smoking cluster-randomized controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 16:45:55","doi":"10.21203/rs.3.rs-8128593/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61642f25-9308-4ddd-9068-004ce86eed13","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58068937,"name":"Epidemiology"},{"id":58068938,"name":"Pulmonology"},{"id":58068939,"name":"Health Policy"}],"tags":[],"updatedAt":"2025-11-18T16:45:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 16:45:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8128593","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8128593","identity":"rs-8128593","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}