Population Medicine Multimorbidity Interventions in Xishui on a High-Risk COPD Population: A Cluster Randomized Controlled Trial
Phase: NA
Conditions: Multimorbidity, Population Medicine, Chronic Obstructive Pulmonary Disease
Sponsor: Peking Union Medical College

 
 
Study Protocol: 
Impact of the Population Medicine Multimorbidity 
Intervention in Xishui County (POPMIX) on People 
at High Risk for Chronic Obstructive Pulmonary 
Disease: Protocol for the POPMIX-COPD Cluster 
Randomized Controlled Trial 
 
 
 
 
 
  
Trial Registration:
 
NCT06456996
International Registered Report Identifier (IRRID): DERR1-
10.2196/85597
Date: December 18, 2025

Abstract
Background: Tobacco-related noncommunicable diseases (NCDs) present a major public health challenge in China, requiring
population-level management. Chronic obstructive pulmonary disease (COPD) is the most common and prevalent chronic
respiratory disease associated with tobacco use. In addition, COPD shares risk factors with other NCDs that frequently co-occur,
leading to multimorbidity. This study focuses on the early detection and integrated management of COPD and related multimorbidity
among high-risk populations. Population medicine, an emerging and evolving concept aimed at maximizing population health
and well-being, provides a promising framework for shifting interventions against COPD from an individual patient focus to a
population-level approach.
Objective: This study aims to evaluate the effectiveness of a population medicine–based multimorbidity intervention package
among individuals at high risk for COPD.
Methods: We are conducting a 2-arm, population-based, stratified cluster randomized controlled trial (cRCT). The intervention
integrates community screening, chronic disease management, patient education, digital follow-up, and team-based care. The
trial is being implemented in Xishui County, Guizhou Province, a low-resource county in Southwestern China. Each of the 26
townships in Xishui County was considered a cluster and stratified into large and small townships based on population size. An
equal number of residents from each township stratum (large and small) were randomized to undergo the COPD Screening
Questionnaire. Individuals identified as being at high risk for COPD were considered study participants and were subsequently
enrolled in either the intervention or control arm. The target sample size was approximately 2850 individuals.
Results: Data collection for the POPMIX-COPD trial began in June 2024. Baseline, 3-month, and 6-month assessments have
been completed, and 12-month follow-up assessments are planned to be completed in March 2026. All participants in the
intervention arm are being followed for 1 year, with 1 telephone follow-up at month 3 and in-person follow-ups at months 6 and
12. Primary outcomes for each participant include the number of chronic conditions controlled, receipt of lung function testing,
and forced expiratory volume in 1 second. In addition, secondary outcomes were health-related quality of life, mental and
behavioral health status, health care utilization, knowledge of COPD and asthma, and care cascade indicators for chronic conditions.
Conclusions: This cRCT is the first multimorbidity intervention study designed within the population medicine framework to
target populations at high risk for COPD. It was featured as a case study in the report of the Lancet Commission on Investing in
Health. The results of the trial are expected to inform the next generation of multimorbidity management and population medicine
practices among global health authorities and practitioners.
Trial Registration: ClinicalTrials.gov NCT06456996; https://clinicaltrials.gov/ct2/show/NCT06456996
International Registered Report Identifier (IRRID): DERR1-10.2196/85597
(JMIR Res Protoc 2026;15:e85597) doi: 10.2196/85597
KEYWORDS
population medicine; multimorbidity; tobacco-related NCDs; high-COPD-risk population; cRCT
Introduction
The concept of population medicine focuses on maximizing
health and well-being at the population level—in contrast to
clinical medicine’s traditional focus on the individual
patient—and has been gaining attention in recent literature [1].
Population medicine is a progressive medical discipline that
aims to promote health equity through the following
mechanisms: (1) integrating clinical medicine and public health;
(2) providing comprehensive care that encompasses health
promotion, disease prevention, diagnosis, control, treatment,
and rehabilitation; (3) integrating and applying the knowledge,
academic principles, and technologies of modern medicine with
those of related disciplines; (4) coordinating individual health
behaviors and collective health actions (eg, community-based
smoking cessation campaigns, vaccination programs, and health
education activities); and (5) serving as the medical foundation
of public health practice [1]. To maximize population health,
it is crucial to identify conditions that should be prioritized for
population-level management. Targeting the most prevalent,
preventable, and impactful conditions enables more efficient
allocation of resources and earlier intervention, leading to broad
public health gains.
Noncommunicable diseases (NCDs) strongly linked to tobacco
use are enumerated among the 7 NCDs and injury-related
priority conditions that the Lancet Commission on Investing in
Health identified as needing to be addressed to achieve its “50
by 50” goal of halving the probability of premature death by
2050 [2]. Tobacco-related NCDs include chronic obstructive
pulmonary disease (COPD) and certain cancers, as well as
deaths from atherosclerotic cardiovascular disease and
hemorrhagic stroke [2]. In China, tobacco-related NCDs
accounted for approximately 1 year of an estimated 3.5-year
life expectancy gap attributable to the Lancet Commission’s 7
NCDs and injury-related priority conditions, relative to the
North Atlantic regions. Furthermore, the portion attributable to
tobacco-related NCDs represented approximately 23% of the
overall 4.3-year life expectancy gap between the 2 regions [3].
The magnitude of this gap underscores the importance of
managing tobacco-related NCDs at the population level, from
preventive screening to disease management.
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Among all tobacco-related NCDs, COPD is the most common
respiratory disease. COPD is a leading cause of mortality and
morbidity in China and worldwide [4,5]. It is the fourth leading
cause of death and disability-adjusted life years among all
diseases and was responsible for over 67.8 million deaths
worldwide and approximately 11.7 million deaths in China in
2021 [6]. Recent estimates suggest that more than 400 million
people are affected globally, with this number projected to reach
592 million by 2050 [7]. The immense health burden imposed
by COPD also results in a substantial economic burden. It is
estimated that COPD will cost China’s economy US $1.361
trillion (in 2017 international dollars) over the period 2020-50
[8], amounting to the largest economic burden from COPD
faced by any country. The economic value of reducing avoidable
mortality from COPD amounts to 1.4% of annual national
income in 2019 and 0.9% in 2050; the 2019 figure places COPD
as the fourth-ranked cause of death in terms of economic harm,
as well as mortality, in China [9]. Moreover, COPD prevalence
has steadily increased over the past 2 decades. Among the
population aged 40 years and older, prevalence rose from 8%
in 2002-04 to 14% in 2014-15, representing an increase of nearly
70% [10].
Two main challenges in COPD care have been widely discussed
in the literature. First, awareness and early detection of COPD
remain low among both the general and high-risk populations.
Second, multimorbidity is frequently observed in patients with
COPD and in those at high risk of developing the condition.
Recent studies have shown an unmet health care need for COPD
screening, formal diagnosis, treatment, and symptom control
(ie, poor performance of the COPD care cascade) in China
[11,12]. In a large-scale, population-based COPD screening and
management program (the “Happy Breathing” program), it was
estimated that among all patients with COPD who were
spirometry-diagnosed within the program, 41.0% had previously
undergone spirometry testing for the condition, 17.6% had been
formally diagnosed with COPD, and 8.5% were currently
receiving treatment. Among those receiving treatment, 4.6%
had mild or no exacerbations in the prior year, and 3.9% had
experienced no exacerbations [12]. Given the severe unmet
health care need for COPD, increased attention to
population-based COPD screening and interventions is
warranted.
Community-based screening and management in populations
at high risk for COPD represent one potential strategy to
enhance awareness and early detection. For example, the
TargetCOPD trial showed that screening ever smokers aged
40-79 years can lead to a significant increase in the detection
of new COPD cases relative to usual practice; moreover, this
approach has proven cost-effective as an active case-finding
strategy [13]. Another recent study modeled a community-based
COPD 
screening 
scenario 
and 
demonstrated 
its
cost-effectiveness using a 2-step method [14]. Such research
provides a solid scientific basis for conducting population-based
COPD screening and management among individuals at high
risk for COPD.
Multimorbidity among patients with COPD is extremely
common, as these individuals often face behavioral risk factors,
such as smoking, and tend to be older (indeed, nearly 50% of
all adults suffer from multimorbidity at baseline) [15]. The
reported prevalences of other chronic conditions among patients
with COPD are high: more than 50% have hypertension [16],
roughly 10%-60% experience anxiety and depression [17], 20%
have diabetes [18], about 30% are obese and about 17% are
underweight [16], 19% have chronic heart failure [18,19], 20%
have ischemic heart disease [18,20], and 5% have had a stroke
[18]. The high prevalence of coexisting conditions presents
significant challenges for health care delivery, particularly with
respect to designing care models that address multiple conditions
simultaneously. A key challenge in delivering effective care to
this population lies in how health care providers are incentivized.
In many settings, clinician incentives remain aligned with
passive diagnosis and treatment of individual diseases, rather
than with providing proactive, inclusive care that holistically
addresses multimorbidity.
In response to the substantial burden of COPD and associated
multimorbidity, a comprehensive, population-based integrated
model of screening and long-term care has been launched.
However, there has been limited prior research evaluating the
effectiveness of integrated management interventions within a
population medicine framework that emphasizes proactive case
identification and management among high-risk groups, rather
than relying solely on reactive clinical care. Therefore, an
important goal of this study is to evaluate the effectiveness of
the newly launched multimorbidity care model in addressing
the growing challenges of COPD and multimorbidity. In this
protocol, we implement an integrated multimorbidity
intervention package to evaluate its impact on relevant health
outcomes among individuals diagnosed with COPD or at high
risk of COPD. We also introduce a performance-linked
“pay-for-population” scheme that ties financial incentives for
health care workers to concrete population-level indicators, such
as spirometry coverage, diagnosis rate, treatment initiation rate,
and reduction in acute exacerbations. The design of the
integrated package and the decision to evaluate its effects as a
whole are based on consensus with the local authority to ensure
feasibility in a real-world setting. This combined strategy is
intended to shift provider behavior from passive disease
response to proactive, integrated care delivery in a manner
aligned with the principles of population medicine. This study
is part of the Population Medicine Multimorbidity Intervention
in Xishui County (POPMIX) project, which aims to develop
and evaluate evidence-based, multicomponent strategies for
managing chronic diseases and their comorbidities in a
resource-limited rural county in China. We aim to investigate
whether 
a 
population-based 
integrated 
multimorbidity
intervention package affects our primary outcomes, including
the number of chronic diseases controlled, whether participants
have received lung function testing, and forced expiratory
volume in 1 second (FEV1), as well as our secondary outcomes,
such as health-related quality of life, mental health, behavioral
risk factors, health care utilization, productivity loss, knowledge
of COPD and asthma, and care cascade indicators for COPD
and other chronic diseases.
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Methods
Trial Design
This is a parallel, 2-arm, stratified cluster randomized controlled
trial (cRCT) conducted in Xishui, Guizhou, China (Figure 1).
The protocol was designed in accordance with the Standard
Protocol Items: Recommendations for Interventional Trials
(SPIRIT) 2025 Statement. The SPIRIT checklist for this protocol
is provided in Multimedia Appendix 1.
Figure 1. Trial flowchart. COPD: chronic obstructive pulmonary disease; COPD-SQ: COPD Screening Questionnaire. See Multimedia Appendix 4
for a higher resolution version of this figure.
Our clusters are the townships of Xishui County. The
demographic characteristics of Xishui County, including the
number and size of townships, are summarized in Table 1. The
townships were stratified according to whether their population
size exceeded the mean size of all townships. Within each
stratum, townships were randomly allocated in a 1:1 ratio to
either 
the 
intervention 
or 
control 
arm 
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 township for enrollment. Permanent residents were defined
as individuals who had resided in Xishui County for at least 3
months and were expected to remain within the same township
for the subsequent year at the time the government collected
the information.
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Table 1. Characteristics of Xishui County, Guizhou.
Xishui County
Characteristics
30.20a
Total population size (10,000a), n
22
Rural townships, n
210
Villages within rural townships, n
9518
Average rural township size (ie, number of people), n
4
Urban townships, n
47
Communities within urban townships, n
23,150
Average urban township size (ie, number of people), n
aThe total population size listed in Table 1 was obtained from the Xishui authority. The figures here reflect permanent residents as of May 10, 2024,
and are limited to individuals who had already stayed in Xishui County for 3 months in 2024 and would continue to stay within the same township for
the next year.
The COPD Screening Questionnaire (COPD-SQ) was used to
identify individuals at high risk for COPD. The COPD-SQ
includes 7 items: age, cumulative smoking history, BMI, cough,
breathlessness, family history of respiratory diseases, and
exposure to cooking-related smoke [21]. The total score ranges
from 0 to 38, with higher scores indicating a greater risk of
developing COPD. This questionnaire has been validated
through various epidemiological surveys and community health
screenings and has been recommended for COPD screening in
primary care settings in China [21]. Participants with a
COPD-SQ score of 16 or higher were considered for inclusion
in the study, ensuring that the cohort comprised a population at
high risk for COPD.
Setting
This study was implemented across 26 township-level clusters
in Xishui County, a mountainous area in northern Guizhou
Province, China, where the prevalence of smoking was 37.9%
in 2018-2019, the second highest among all provinces [22]. A
rural area designated as China’s National Comprehensive
Primary Health Experimental Area, Xishui presents a unique
combination of policy-driven innovation and socioeconomic
challenges. With a gross domestic product per capita (48%)
below the national average in 2024 and limited human capital
development, the county exemplifies a resource-constrained
rural setting. These conditions, combined with its experimental
role in health system transformation, have established Xishui
as an ideal setting for evaluating scalable multimorbidity
interventions tailored to underserved populations.
Trial Participants (Inclusion and Exclusion Criteria)
All research participants were required to be at least 35 years
old. In addition, eligible participants had to meet the following
criteria: (1) a COPD-SQ score of 16 or higher, corresponding
to individuals at high risk for COPD; (2) residence in a township
of Xishui County for the prior 3 months and plans to remain in
the same township for the upcoming year; and (3) provision of
informed consent.
Individuals with severe cognitive disorders that significantly
impair comprehension, decision-making, or adherence to the
intervention were excluded. Similarly, those who had completely
lost the ability to perform activities of daily living independently
were not eligible for participation. These criteria ensure that the
study includes individuals who can actively engage in the
intervention and follow-up assessments while minimizing
confounding variables.
Research Ethics Approval
This study received ethical approval from the Ethics Committee
of Peking Union Medical College. To protect participant
privacy, all identifying information has been removed from the
dataset, and data are anonymized before analysis. Participants
have the right to withdraw from the study at any point, without
consequences for their standard health care 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. This study was
initially approved by the Ethics Committee of Peking Union
M e d i c a l  
C o l l eg e  
( a p p r ova l  
n u m b e r
CAMS&PUMC-IEC-2024-040). A continuing ethics review
was completed in June 2025, and updated approval was granted
under approval number CAMS&PUMC-IEC-2025-061.
Intervention
Participants assigned to the intervention arm received access to
the population medicine multicomponent intervention package,
which was developed through iterative prototyping and
stakeholder consultations. The specific eligibility criteria for
each intervention are summarized in Table 2. Participants in
the control arm were notified of their COPD risk status and
encouraged to complete a face-to-face interview; they were not
provided with any interventions after the interview, but were
encouraged to receive usual care.
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Table 2. Eligibility criteria for interventions.
Eligibility criteria
Intervention
Population level and target population
General population
Permanent residents
Health education
General population
Permanent residents aged 35 years and
above
Online screening with the COPDa-SQ
General population
Patients with COPD or high-COPD-risk population
Individuals with COPD-SQb score ≥16
Community-based spirometry pulmonary
function tests, interpretation of results, and
health education
High-COPD-risk population
Currently smoking or have quit within the
last 6 months
Health education for smokers to support
smoking cessation
Smokers within high-COPD-risk population
Currently smoking or have quit within the
last 6 months and own a smartphone
Digital health interventions for smoking
cessation
Smokers within high-COPD-risk population
Individuals with Warwick Edinburgh Men-
tal Well-being Scale score <45 who own a
smartphone
Mental health digital health interventions
High-COPD-risk population with depression and
anxiety symptoms
Individuals with Warwick Edinburgh Men-
tal Well-being Scale score <45
Health education for smokers with mental
health issues
High-COPD-risk population with mental health
symptoms
Individuals with BMI <18.5 kg/m2 or BMI
≥ 24.0 kg/m2
Weight abnormality interventions
High-COPD-risk population with abnormal weight
Three consecutive measurements with aver-
age systolic blood pressure ≥140 mmHg or
diastolic blood pressure ≥90 mmHg or both
Hypertension management and education
High-COPD-risk population with hypertension
Fasting blood glucose ≥7.0 mmol/L or ran-
dom blood glucose ≥11.1 mmol/L
Diabetes management and education
High-COPD-risk population with type 2 diabetes
mellitus
Individuals with FEV1
c/FVCd <0.7 after
bronchodilation, or high-COPD-risk popu-
lation who fail to conduct a pulmonary
function test for any reason
Encouragement to conduct computed tomog-
raphy scan and seek professional medical
treatment
Patients with COPD or high-COPD-risk population
who fail to conduct pulmonary function tests
Health providers
Primary care providers who engage with
the investigation and intervention
Intrinsic incentive mechanism
Health providers
Primary care providers who engage with
the investigation and intervention
Extrinsic incentive mechanism
Health providers
aCOPD: chronic obstructive pulmonary disease.
bCOPD-SQ: COPD Screening Questionnaire.
cFEV1: forced expiratory volume in 1 second.
dFVC: forced vital capacity.
The strategies included in the multicomponent intervention
package within this trial have been proven cost-effective and
are advocated by the Lancet Commission on Investing in Health
[2]. Figure 2 illustrates the full integration pathway of the
intervention package implemented in this trial. The structure
comprises 4 layers: the screening package, the targeted
population, the integrated intervention components, and the
long-term management package. The figure shows how the
general population is first screened and stratified into risk
groups, followed by targeted interventions (eg, spirometry,
mental health support, and smoking cessation) and intensive
follow-up. It visualizes the conceptual transition from individual
treatment to proactive population health management.
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Figure 2. Integrated pathway of the multicomponent intervention package. CBT: cognitive behavioral therapy; COPD: chronic obstructive pulmonary
disease; COPD-SQ: COPD Screening Questionnaire; CT: computed tomography; WEMWBS: Warwick-Edinburgh Mental Well-Being Scale. See
Multimedia Appendix 4 for a higher resolution version of this figure.
In addition to the intervention components and care pathways
described in Figure 2, the implementation of the multicomponent
intervention package was supported by a multilevel delivery
structure and a performance-linked incentive mechanism. Figure
3
illustrates 
the 
organizational 
structure, 
population
stratification, 
incentive 
design, 
and 
distribution 
of
responsibilities across administrative levels (county, township,
village, household) for delivering the intervention in Xishui.
The 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 multicomponent intervention package. CDC: Centers for Disease Control and
Prevention; COPD: chronic obstructive pulmonary disease; COPD-SQ: COPD Screening Questionnaire; ECRHS: European Community Respiratory
Health Survey; FEV1: forced expiratory volume in 1 second; FVC: forced vital capacity; PCCM: pulmonary and critical care medicine; PFT: pulmonary
function test; WEMWBS: Warwick-Edinburgh Mental Well-Being Scale. See Multimedia Appendix 4 for a higher resolution version of this figure.
Specific Interventions
Overview
Specific interventions as part of the multicomponent intervention
package are described in the following sections.
Health Education
Health education is provided to all permanent residents,
regardless of their risk level. This intervention aims to increase
population-wide 
awareness 
of 
tobacco-related 
NCDs,
particularly COPD, and to promote early health-seeking
behaviors. Health education is delivered through multiple
channels, including face-to-face sessions led by trained primary
care workers, printed brochures, posters in public areas, and
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short videos/messages distributed via WeChat (Tencent
Holdings Limited). Educational content covers the importance
of lung health, risk factors such as smoking and indoor air
pollution, the purpose of spirometry screening, and strategies
for chronic disease prevention and lifestyle modification.
Online Screening for COPD
As part of the screening package, all representative permanent
residents aged 35 years or older are invited to complete an online
screening survey via a QR code–linked digital platform. The
survey incorporates the COPD-SQ and is designed to allow
broad dissemination and efficient data collection. Upon
submission, participants receive automated feedback on their
risk level and are guided to appropriate follow-up actions, such
as spirometry appointments or health counseling sessions.
Community-Based Spirometry Pulmonary Function
Tests, Results Interpretation, and Health Education
The population at high risk of COPD in the intervention arm
receives alerts directing them to a community gathering place
for spirometry testing, which is conducted using the
BH-AX-MAPG 
spirometry 
equipment 
(BreathHome).
Participants screened for COPD are referred to the county
hospital for computed tomography and formal diagnosis. In
addition, they receive further health education on the risks of
COPD and strategies for disease prevention and management,
delivered verbally by primary health care providers and
supplemented with printed materials for distribution.
Health Education for Smokers to Support Smoking
Cessation
Participants in the intervention group receive targeted health
education to reinforce the importance of smoking cessation.
This education emphasizes the health risks associated with
smoking and the benefits of quitting, providing participants
with evidence-based information and practical guidance. It is
delivered through verbal communication by primary health care
providers and supplemented with printed posters for distribution.
Digital Health Interventions for Smoking Cessation
NicQuit is a WeChat-based digital smoking cessation
intervention that includes cognitive behavioral therapy (CBT)
modules focused on smoking cessation strategies, coping with
triggers, and reinforcement techniques to maintain abstinence.
It is designed for smokers who are currently smoking or have
quit within the past 6 months [23]. The intervention specifically
targets individuals familiar with smartphone technology,
ensuring accessibility and usability. Personalized notifications
and reminders are delivered through the WeChat platform to
encourage participants to engage regularly with the cessation
plan and maintain adherence.
Mental Health Digital Health Interventions
A CBT-based digital mental health intervention, EmoEase [24],
is offered to individuals experiencing mental health symptoms
(Warwick-Edinburgh Mental Well-Being Scale [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.
Health Education for High-COPD-Risk Individuals With
Mental Health Symptoms
Specialized health education is offered to high-COPD-risk
individuals with coexisting mental health symptoms, tailored
to their specific challenges. This education includes guidance
on managing mental health symptoms and is delivered verbally
by trained community health workers or general practitioners,
supplemented with printed materials for distribution.
Weight Abnormality Interventions
Individuals with a BMI<18.5 kg/m2 (underweight) or ≥24.0
kg/m2 (overweight or obese) are considered to have weight
abnormalities. CBT-based motivational interviewing is used to
help participants self-identify weight-related barriers before
providing tailored health education. During the intervention
session, participants are asked CBT-informed questions designed
to prompt reflection on the disadvantages and advantages of
being underweight or overweight.
Hypertension and Diabetes Management
The goal of this intervention is to actively enroll smokers whose
blood pressure exceeds 140/90 mmHg [25] or whose random
blood glucose exceeds 11.1 mmol/L (or fasting blood glucose
≥ 7.0 mmol/L) into the National Essential Public Health Service
in China [26]. These participants also receive health education
on hypertension and diabetes through verbal counseling by
trained community health workers or general practitioners,
supplemented with printed materials for distribution.
Encouragement to Seek Professional Medical Treatment
in Superior Hospitals
Patients with a postbronchodilator FEV1-to-forced vital capacity
(FVC) ratio of <0.7 are diagnosed with COPD and are
encouraged to seek professional medical care at higher-level
hospitals for further diagnosis and management. Some
participants at high risk of COPD failed to complete pulmonary
function testing due to contraindications, lack of understanding
of the procedure, failure to meet spirometry quality-control
standards, or unwillingness to undergo the tests. High-risk
participants who do not complete pulmonary function testing
for any reason are encouraged to undergo a computed
tomography scan and seek formal diagnosis and medical
treatment at a superior hospital.
Pay-for-Population Mechanism
A novel pay-for-population mechanism is introduced to motivate
primary care providers to actively engage with the intervention
by aligning compensation with population health goals. This
approach ties provider payment to 4 key stages of care:
screening, diagnosis, treatment, and control. Health providers
in township hospitals are rewarded based on 4 performance
indicators. The first is the proportion of individuals completing
pulmonary function testing among township residents aged 35
years and above. The second is the proportion of individuals
diagnosed with COPD among those identified as high risk in
the initial screening (defined as COPD-SQ >16). The third is
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the proportion of patients with confirmed COPD who receive
standardized inhaled treatment, relative to the total number of
patients with confirmed COPD in the township. The fourth is
the proportion of patients with confirmed COPD who have not
experienced an acute exacerbation in the past 6 months, relative
to all patients with confirmed COPD in the township. The county
hospital’s respiratory department and the county Center for
Disease Control and Prevention are assessed using the same 4
performance indicators, with data aggregated across all 13
intervention townships. In addition to providing an extrinsic,
results-based financial incentive, providers are offered
specialized training and capacity-building opportunities to
appeal to intrinsic motivation, support proactive service delivery,
and foster a strong sense of responsibility for population health.
Details on implementing the pay-for-population mechanism are
provided in Multimedia Appendix 2.
Outcomes
We will measure both primary and secondary end points at the
end of follow-up for both trial cohorts. End points will be
assessed among individuals assigned to the intervention group
and compared with those among individuals assigned to the
control group (ie, intent-to-treat). This high-COPD-risk
population cohort will be followed longitudinally through March
2026 for repeated measurement of end points and covariates,
according to the initial intervention versus control assignment.
Specifically, all primary and secondary end points will be
measured during health worker visits at 6 and 12 months after
assignment, except for knowledge of diseases, health service
utilization, and the number of chronic diseases controlled, which
will be assessed only at the end of the trial (ie, 12 months after
assignment). Table 3 defines the trial’s primary outcomes,
illustrating the functional forms of measured variables and the
approach to assessing each end point, while secondary outcomes
are detailed in Multimedia Appendix 3.
Table 3. Primary outcomes.
Description
Outcome
Number of chronic conditions controlled
•
Definition: Number of controlled conditions among 7 objectively measured chronic
health conditions: chronic obstructive pulmonary disease, asthma, depressive
symptoms, anxiety symptoms, BMI, hypertension, and type 2 diabetes.
•
Functional form: Counting data
•
Measurement: Through objective physical examination or a validated scale
COPDa screening
•
Definition: Response to the question “Have you ever had a pulmonary function
test?”
•
Functional form: Binary
•
Measurement: Self-reported response
FEV1
b measurement
•
Definition: Volume of air forcibly exhaled during the first second of a forced expi-
ratory maneuver
•
Functional form: Continuous
•
Measurement: Pulmonary function test and portable spirometry
aCOPD: chronic obstructive pulmonary disease.
bFEV1: forced expiratory volume in 1 second.
Timeline
Our trial began on June 17, 2024, with recruitment conducted
across all townships in Xishui County. Participants in the
intervention arm undergo structured assessments at 4 time
points: baseline (in-person), 3-month follow-up (telephone),
6-month follow-up (in-person), and 12-month follow-up
(in-person). At baseline, following completion of informed
consent, participants were invited to undertake a comprehensive
in-person assessment. Physical examinations and pulmonary
function tests are conducted at multiple points, with protocols
tailored by subgroup: prebronchodilator spirometry at baseline
and 12 months for the high-COPD-risk population;
postbronchodilator spirometry at baseline and 12 months for
patients with COPD; and prebronchodilator spirometry at 6
months for high-COPD-risk participants with prebronchodilator
FEV1/FVC<70% at baseline. All baseline assessments were
administered under the supervision of trained site staff to ensure
accuracy and adherence to protocol standards. Subsequent
follow-ups include a 3-month telephone assessment to monitor
intervention adherence and health status. In-person follow-ups
at 6 and 12 months repeat elements of the baseline assessment,
including the questionnaire, physical examination, and
pulmonary function testing. For the control group, the schedule
includes a baseline questionnaire and a 12-month on-site survey.
Table S2 in Multimedia Appendix 3 presents the timeline of
prospective cohort visits and data collection.
Sample Size
We included all 26 townships in Xishui County and allocated
them to the intervention or control group using 1:1 stratified
randomization. Based on the population size of each township,
we recruited a representative sample of 44,000 residents aged
35 years or older. All 44,000 participants underwent COPD-SQ
screening via QR code. Considering a 10% attrition rate and an
estimated high-risk prevalence of approximately 25% among
individuals aged 35 years and above [27], the projected final
enrollment is 10,000 individuals in the high-COPD-risk
population.
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Because of the relatively large number of important secondary
end points and the high degree of uncertainty regarding the
prevalence and risk factors of COPD in rural and urban
communities, we chose FEV1 as one of the primary end points
of interest and conducted the power calculation conservatively.
This choice was made because FEV1 is an objective and
clinically relevant measure that helps address uncertainties in
previous studies while allowing for a more robust power
calculation. Given that 3 outcomes are designated as primary
end points, we applied a Bonferroni adjustment for both sample
size and power calculations. According to the China Pulmonary
Health Study—a cross-sectional study in a nationally
representative sample of adults aged 20 years or older—the
overall prevalence of spirometry-defined COPD is expected to
be 8.6% (95% CI 7.5%-9.9%) [10]. Based on a study conducted
by Chen et al [28], the mean and SD of FEV1 levels among
patients with mild COPD and the high-COPD-risk population
in China were reported to be 2.62 L and 0.55 L, respectively.
Following standard procedure, the intraclass correlation
coefficient (ICC) was identified in advance, as it is a key
parameter required for accurate sample size estimation in cluster
randomized designs. Previous literature reports an ICC of 0.055
for patients with COPD in the PACK Brazil Study and 0.05 in
the Danish TeleCare North Study [29]. Thus, we set the ICC at
0.05 for sample size calculation. We followed the formula for
population-based stratified cRCTs proposed by Crespi [30] and
assumed a 2-sided significance level of 1.67% under the
Bonferroni adjustment and a power of 80%. Based on these
assumptions, we calculated a conservative sample size estimate
of 2850 individuals in the high-COPD-risk population, which
is required to detect a minimal difference of 0.1 L in FEV1 in
our study.
Power Calculations
In our power calculations, we estimated the minimum detectable
differences (MDDs) in FEV1 (L) levels under varying sample
sizes and ICC values, assuming a 2-sided significance level of
5% and a power of 80%. We further assumed that 10% of
participants would refuse to participate in the trial and that 10%
of individuals could not be followed up for end point assessment.
To our knowledge, this is the first real-world, large-scale
multimorbidity implementation trial, and no similar trials are
available for reference. Therefore, we conducted a sensitivity
analysis to examine how the MDDs in FEV1 levels change with
different proposed population sizes. The results of this sensitivity
analysis are presented in Table 4. Our analyses indicate that the
population-based cluster randomized trial design can detect
differences of 0.052-0.218 L in FEV1 levels between the
treatment and control groups. In the implementation, we applied
stratification before randomization to reduce between-cluster
variance.
Table 4. Minimum detectable differences in forced expiratory volume in 1 second (liters) across ICCa values and proposed sample sizes.
ICC=0.05
ICC=0.025
ICC=0.01
ICC=0.005
Proposed sample size, n
0.226
0.165
0.113
0.089
5000
0.216
0.157
0.108
0.085
5500
0.207
0.151
0.103
0.081
6000
0.191
0.139
0.095
0.075
7000
aICC: intraclass correlation coefficient.
As shown in Table S2 in Multimedia Appendix 3, we listed 3
primary outcomes and a large set of secondary outcomes. For
primary outcomes, we will apply the Bonferroni adjustment,
setting the significance threshold at 0.0167 (0.05/3). For
secondary outcomes, to mitigate concerns of type I error
inflation, we plan to use the Benjamini-Hochberg procedure to
control the false discovery rate. We will set the false discovery
rate at 0.05 and rank all unadjusted P values of the secondary
outcomes from smallest to largest. Adjusted P values will then
be reported following the Benjamini-Hochberg procedure, and
the significance of each secondary outcome will be determined
based on these adjusted P values.
In Table 5, we present a sensitivity analysis reporting Cohen d
for the trial. We set the SD to 1 to explore Cohen d (standardized
MDDs) under different ICC values and proposed sample sizes.
Table 5. Minimum detectable differences in standardized mean difference (Cohen d) across ICCa values and proposed sample sizes.b
ICC=0.05
ICC=0.025
ICC=0.01
ICC=0.005
Proposed sample size, n
0.412
0.300
0.205
0.162
5000
0.393
0.286
0.196
0.155
5500
0.376
0.274
0.188
0.148
6000
0.348
0.253
0.174
0.137
7000
aICC: intraclass correlation coefficient.
bMinimum detectable difference values represent standardized mean differences (Cohen d), calculated by assuming a pooled SD (σ) of 1 for the outcome.
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Assignment of the Intervention
This cRCT assigned participants to either the population
medicine multicomponent intervention or the control arm at the
township level, with randomization stratified by geographic
region to ensure balance across community characteristics. A
computer-generated algorithm was used to randomly allocate
13 townships to each arm, with the sequence generated by an
independent statistician who was not involved in recruitment
or intervention delivery. Given the nature of the intervention,
this is an open-label trial, meaning health care providers and
research staff are aware of group assignments. However,
participants themselves were not explicitly informed of their
allocation or that they were part of an “intervention group,” to
mitigate expectation bias. No additional interventions were
provided to the control group, although usual care remained
accessible throughout the study period.
To enhance adherence to the intervention protocol, a structured
approach was integrated into all follow-up phases. Adherence
was reinforced through telephone consultations at the 3-month
follow-up and through an in-person visit at the 6-month
follow-up. During these sessions, participants received
personalized feedback on their health outcomes (eg, spirometry
results, smoking status), including comparisons with baseline
data to highlight improvements or areas requiring attention. For
example, if a participant had not sought professional medical
treatment for COPD, a root-cause analysis was conducted by
reviewing baseline results and exploring potential barriers, such
as lack of motivation or difficulty accessing health care.
Adherence was systematically monitored at each follow-up
through validated questionnaires assessing compliance with the
intervention (eg, whether the participant had sought care, app
usage frequency, smoking cessation attempts). In addition,
automated reminders and tailored counseling were provided to
address gaps in adherence. This iterative process ensured that
each follow-up served as an opportunity to motivate participants,
troubleshoot challenges, and adapt strategies to sustain
engagement throughout the trial.
Blindness
This is an open-label cRCT, in which field workers and
participants are not blinded. We consider this scientifically
reasonable for large-scale population health intervention trials
conducted in real-world community settings, such as the
POPMIX trial. However, we made efforts to blind most
members of the Data and Safety Monitoring Board (DSMB).
Statisticians will remain blinded when conducting data analyses,
and DSMB members will not be informed of the exact
intervention assignment of each township. Thus, while full
blinding was not feasible, we maintained investigator blinding
wherever operationally possible, consistent with best practices
in open-label population health and behavioral intervention
trials.
Data Collection Plan
We are collecting trial data across multiple dimensions to
comprehensively assess the impact of the multicomponent
intervention (Table 2). At baseline, all participants completed
a structured questionnaire capturing demographics and
socioeconomic status, quality of life, lifestyle and risk behaviors,
disease history, mental health status (9-item Patient Health
Questionnaire [PHQ-9], 7-item Generalized Anxiety Disorder
[GAD-7], and WEMWBS), and productivity loss. In addition
to self-reported data, participants underwent physical
examinations, including measurements of height, weight, BMI,
waist circumference, heart rate, blood pressure, and blood
glucose. Pulmonary function was assessed using spirometry to
evaluate respiratory health and detect airflow limitation. For all
participants 
in 
the 
high-COPD-risk 
population,
prebronchodilator spirometry was conducted at baseline and
again at 12 months to monitor changes in lung function over
time. For participants diagnosed with COPD, both pre- and
postbronchodilator spirometry were performed at baseline, with
prebronchodilator tests repeated at 6 months and both pre- and
postbronchodilator tests repeated at 12 months.
Follow-up data are collected at 3 time points: 3 months (via
telephone), 6 months (in-person), and 12 months (in-person)
for the intervention group and only 12 months for the control
group (in-person). At each in-person follow-up, participants
complete key sections of the baseline assessments, including
self-reported data on COPD symptoms and management (lung
function via spirometry, exacerbations, treatment adherence,
COPD Assessment Test, and Modified Medical Research
Council), asthma symptoms and management, high-risk
behaviors (eg, daily cigarette consumption and smoking
dependence, alcohol use), and chronic disease status (blood
pressure, blood glucose, and BMI). Mental health status is
reassessed using the same scales (PHQ-9, GAD-7, and
WEMWBS), and health-related quality of life is measured using
the EQ-5D-5L and the Saint George Respiratory Questionnaire
for patients with COPD. Additionally, lifestyle behaviors, such
as physical activity, alcohol consumption, and diet (including
sugar, salted vegetable, and general vegetable intake), are
monitored through self-report. Health resource utilization,
including outpatient and inpatient visits, is also recorded. Data
are collected by trained field staff using standardized protocols
to ensure accuracy and reliability. All data are entered
electronically via an electronic data capture (EDC) system,
which includes built-in validation checks to minimize data entry
errors.
Data Management
This study uses an EDC system to ensure real-time data entry,
automated validation, and secure storage. Data are collected by
trained field staff using tablet-based digital surveys that sync
directly with a central database. The EDC system incorporates
range checks, logical consistency validations, and missing data
alerts to minimize errors, with flagged inconsistencies promptly
reviewed by the data management team. Data collection forms,
covering demographic, clinical, and behavioral assessments,
are standardized and precoded, while biometric data (eg,
pulmonary function, blood pressure, BMI, and blood glucose)
are recorded directly into the system. Spirometry data are
transmitted from portable devices and subjected to automated
quality control checks to ensure validity.
We store data using numeric identifier codes to maintain
participant anonymity in a password-protected database
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accessible only to the research team for analysis. Access is
restricted to a minimal number of individuals, including the
principal investigator, co-investigators, biostatisticians, and data
scientists.
Statistical Analysis
Primary analyses will compare outcomes between baseline and
the 12-month follow-up using generalized linear mixed models
(GLMMs) with township as a random effect under the
intention-to-treat principle. To evaluate the robustness of the
findings, intervention effects will be estimated using a primary
design-adjusted model, which will adjust for stratification factors
and baseline values of the outcome measure. This will be
complemented by a sensitivity-adjusted model that will further
incorporate demographic covariates, such as age, sex, and
education level. Adjustment for multiple testing will be applied
using the Bonferroni correction and Benjamini-Hochberg
procedure.
To evaluate the effects of individual intervention components
and explore which components drive specific outcomes, we
designed a series of regression discontinuity design analyses,
planned for publication after the main trial results. We
established 7 clear diagnostic thresholds for the conditions
included in our trial. For COPD, the threshold is an
FEV1-to-FVC ratio of 70%. For PHQ-9 and GAD-7, a score of
10 is used to indicate the presence of depressive and anxiety
symptoms, respectively. For the WEMWBS questionnaire, a
threshold of 45 is used to indicate whether general mental health
is adequate. For hypertension, clear thresholds are defined as
140 mmHg for systolic blood pressure or 90 mmHg for diastolic
blood pressure. Individuals with a BMI below 18.5 kg/m2 are
considered underweight, while those with a BMI above 24 kg/m2
are considered overweight. For high blood glucose, thresholds
of 7.0 mmol/L for fasting glucose or 11.1 mmol/L for random
glucose indicate potential type 2 diabetes mellitus. These
well-defined thresholds allow us to conduct regression
discontinuity design analyses to evaluate the component-specific
effects of the integrated intervention package within the
intervention arm.
To better elucidate how the intervention package will reshape
the behaviors of both high-COPD-risk populations and health
care providers, we also designed a series of qualitative studies
involving interviews with high-COPD-risk populations,
physicians and community health workers, and policy makers
in the intervention arm. These studies will serve as important
supplements to the main trial by illustrating the mechanisms of
the intervention and the subjective experiences of being
intervened under the population medicine framework.
Monitoring
We appointed an independent DSMB to provide scientific rigor
and ensure an independent evaluation of safety. The DSMB
comprised leading experts in public health, epidemiology,
clinical research, and respiratory disease. The DSMB convened
3 meetings in which baseline and follow-up descriptive analyses
for the intervention arm were evaluated for process monitoring.
The DSMB convened for a formal interim analysis in February
2026. At this meeting, the research team reviewed the conduct
of the trial, including adherence to intervention components,
logistical issues, and the completeness and quality of the
collected data. Our biostatisticians also presented emerging
effect estimates for the intervention and control arms to the
steering committee. Stopping rules were established regarding
safety and study completion:
•
If deaths or severe adverse effects were reported, these
would be immediately conveyed to the DSMB. The DSMB
would determine if the deaths or adverse effects were related
to the intervention and advise if the study should be stopped
immediately.
•
In February 2026, during the planned interim analysis, the
trial was approaching its completion as field workers
confirmed that the majority of eligible participants had been
contacted. At this stage, the results were assessed to
determine the study’s conclusion based on the following:
•
Null findings, suggesting that the intervention had no
detectable effect and indicating that the study should
be stopped.
•
Statistically significant findings, indicating that the
study had achieved its primary objectives and should
be stopped.
•
Emerging but statistically insignificant effects,
suggesting that the study might require further
observation if applicable. However, the study would
be terminated if field workers confirmed that all eligible
participants had been contacted and no additional
individuals remained to undergo follow-up assessments.
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 of any protocol
amendments requiring its approval. Following approval by the
Ethics Committee and before implementation, all research team
members were informed of the protocol amendments.
In this trial, an unavoidable amendment was made to the
secondary outcome measures. Although outcome modifications
are generally discouraged, this change was necessitated by a
nonnegotiable regulatory restriction. Specifically, we initially
planned to measure health care utilization (ie, outpatient visits,
inpatient visits, and medical expenditure) using electronic health
records from medical institutions in Xishui County. However,
due to unexpected government regulations, we were unable to
reach an agreement to obtain these data. 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 field workers explained the
study’s aims and detailed procedures, in the presence of a
witness if required. Participants were given sufficient time to
decide whether to participate in the study and the opportunity
to ask questions, which were answered accordingly. For illiterate
participants, consent was documented before enrollment using
a thumbprint signature and a witness’s signature.
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We informed eligible participants about the risks and benefits
of participation in the trial. A decision not to participate in the
study did not carry any consequences for the individual. All
participants were free to refuse or discontinue data collection
at any stage, and participation did not require relinquishing any
concomitant care. Although there were no predefined criteria
for modifying or discontinuing allocated interventions, reasons
for attrition will be documented and reported in future
publications.
Confidentiality
Throughout the study, all data will be handled with strict
confidentiality, and data collection will conform to the
requirements of national data protection legislation. Digital data
will be stored in password-protected files at the Center of the
Chinese Academy of Medical Sciences and will be accessible
exclusively to research team members. Third parties may receive
anonymized data solely for research purposes. Informed consent
forms, laboratory books, and other participant-related documents
will be securely stored during the conduct of the study and will
subsequently be stored at the co-principal investigators’ (CW,
SC, and TY) office premises.
Dissemination
Upon completion of the trial, participant-related information
and results will be provided to the participants themselves or
to their families. The trial has gained public attention since it
was first introduced in the Lancet Commission on Investing in
Health and now serves as a critical component of the ongoing
Lancet Commission Report on Population Medicine and Public
Health. We will disseminate the trial results through
international peer-reviewed journals and at other renowned
international academic conferences.
Results
Data collection for the POPMIX-COPD trial began in June
2024. Baseline, 3-month, and 6-month assessments have been
completed. The trial is planned to finish data collection by
March 2026. Data analyses will be conducted after all data have
been collected and the dataset has been locked. We plan to
publish the POPMIX-COPD paper before mid-2027. At the
time of protocol submission, the trial status is active.
Recruitment began on June 17, 2024, and was completed on
December 31, 2024. Follow-up data collection will conclude
on March 31, 2026.
Discussion
To the best of our knowledge, this is the world’s first large-scale,
community-based 
population 
medicine 
multimorbidity
intervention study targeting tobacco-related NCDs among a
high-COPD-risk population. As The Lancet Commission on
Investing in Health has suggested, tobacco use–related NCDs
are among the leading modifiable risk factors contributing to
life expectancy gaps in China. However, awareness of
tobacco-related NCDs remains low; China is the world’s largest
producer and consumer of tobacco, and the health and economic
burdens of tobacco-related NCDs are substantial, especially in
rural China [31]. Against this context, our population medicine
trial is designed to improve population health and maximize
collective welfare. Through this study, we aim to evaluate
whether an integrated multimorbidity intervention based on
population medicine theory can enhance health outcomes and
awareness of tobacco-related NCDs in a relatively low-resource
setting such as Xishui County.
Our population medicine trial emphasizes population health
rather than individual outcomes. Unlike most clinical trials that
predominantly focus on patients actively seeking care within
the health system, our study targets high-COPD-risk individuals
in the general population and applies community-level
interventions through the use of the COPD-SQ. Early detection
and intervention to prevent disease progression among the
high-COPD-risk population, including individuals with
undiagnosed conditions or unmet care needs, are essential for
improving population health.
We also extended the scope of our study to population health
outcomes, such as multimorbidity. We specified the “number
of chronic diseases controlled” as a primary outcome to capture
the intervention’s effects not only on COPD but also on common
co-occurring conditions, such as hypertension, diabetes, smoking
dependence, abnormal BMI, asthma, and symptoms of
depression and anxiety. Given that our target population is a
high-COPD-risk population, we considered measurement of
FEV1 to be another essential primary outcome. Finally, we
included whether participants had ever received lung function
testing as a primary outcome to highlight the importance of
community-level screening. We additionally listed a series of
secondary outcomes, such as care cascade performance for
COPD, asthma, hypertension, and diabetes; health care resource
utilization; mental health status; knowledge of various
conditions; 
high-risk 
behaviors 
and 
lifestyles; 
and
socioeconomic profile, to facilitate evaluation of the
intervention’s effects across multiple dimensions of population
health, including physical, mental, social, and environmental
well-being.
Our trial leverages both intrinsic and extrinsic motivation to
encourage health workers to support population health. With
respect to intrinsic motivation, we provide professional training
focused on actively addressing unmet population health care
needs. With respect to extrinsic motivation, we incentivize
workers through pay-for-population mechanisms that emphasize
care cascade indicators. We acknowledge that implementing a
new integrated intervention package entails substantial
implementation costs, including financial resources as well as
the time and effort of primary care providers, and therefore
invested in a 3-month program of courses and training in Xishui
County covering population medicine theory, survey skills,
pulmonary function testing, and communication. Our experience
suggests that population medicine and clinical medicine are
complementary rather than contrasting. Population-level
activities conducted in the community enable the timely
detection of previously undiagnosed individuals at high risk of
COPD and facilitate their referral for professional clinical
management. By structuring these activities as community care
delivered by teams of primary care physicians and village health
workers, with many tasks (eg, blood pressure and BMI
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measurement, CBT-based interventions, and smoking cessation
interventions) not requiring physicians, the integrated package
creates a strong linkage between community and clinical care,
supporting earlier and more efficient disease management in
low-resource settings.
Our trial has strong policy implications. First, we aim to evaluate
whether a multicomponent population medicine intervention is
suitable for delivery in community and primary care settings in
China. The intervention package represents one potential
approach to reforming care at both levels. Second, we seek to
encourage policy makers to prioritize population-based
screening and long-term management at the community level,
rather than focusing primarily on hospital-based treatment.
Third, we aim to address unmet health care needs related to
tobacco-related NCDs among the high-COPD-risk population,
as many high-risk individuals lack awareness of both COPD
and common co-occurring conditions, such as asthma,
hypertension, and mental health symptoms. Through intensive
counseling, health education, and encouragement to seek care,
we emphasize the importance of early detection and long-term
management in low-resource settings. Fourth, this trial
encourages primary and community health care workers to shift
from a passive to a proactive mode of practice, in line with the
principles of population medicine. Population medicine calls
on health professionals to provide care beyond the hospital
setting, with community-level screening and referral to the
health system playing a central role. Accordingly, this trial asks
medical professionals to expand the scope of their
responsibilities across multiple dimensions. Specifically, it calls
for the provision of holistic care spanning health promotion,
screening, disease prevention, diagnosis, treatment, control, and
rehabilitation. It also encourages a focus on multimorbidity
rather than the separate treatment of single diseases, and an
expansion of care delivery beyond hospitals to include
communities and populations. This integrated approach may
provide valuable guidance for policy development and health
care planning. Building on these policy implications, our study
further envisions a paradigm shift in the role of primary and
community health workers within population medicine.
Specifically, we hope that 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, lies at the core of population
medicine. We believe that 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 and
sustainable health system reform.
This protocol describes the design and implementation of the
POPMIX-COPD trial, the first large-scale, community-based,
population medicine–oriented multimorbidity intervention
targeting a high-COPD-risk population in China. By integrating
population-level screening, proactive disease management,
patient education, and performance-linked provider incentives,
the study aims to address critical gaps in awareness, early
detection, and integrated care for tobacco-related NCDs.
Conducted in a resource-constrained setting, this trial has the
potential to generate robust evidence for scalable, cost-effective
models of multimorbidity management. The findings are
expected to inform policy development, improve health care
delivery, and promote population health not only in China but
also in other low- and middle-income countries striving to
achieve equitable and sustainable health system reforms.
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[DOCX File , 32 KB-Multimedia Appendix 1]
Multimedia Appendix 2
The pay-for-population incentive mechanism.
[DOCX File , 22 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Secondary outcomes and trial timeline.
[DOCX File , 44 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Full size versions of Figures 1–3.
[ZIP File (Zip Archive), 3486 KB-Multimedia Appendix 4]
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Abbreviations
CBT: cognitive behavioral therapy
COPD: chronic obstructive pulmonary disease
COPD-SQ: COPD Screening Questionnaire
cRCT: cluster randomized controlled trial
DSMB: Data and Safety Monitoring Board
EDC: electronic data capture
FEV1: forced expiratory volume in 1 second
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