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Abstract
Background Despite substantial progress in reducing the global burden of chronic obstructive pulmonary disease (COPD), traditional methods to promote understanding and management of COPD are insufficient. We developed an innovative model based on the internet of things (IoT) for screening and management of COPD in primary healthcare (PHC).
Methods Electronic questionnaire and IoT-based spirometer were used to screen residents. We defined individuals with a questionnaire score of 16 or higher as high-risk population, COPD was diagnosed according to 2021 Global Initiative for COPD (Global Initiative for Chronic Obstructive Lung Disease) criteria. High-risk individuals and COPD identified through the screening were included in the COPD PHC cohort study, which is a prospective, longitudinal observational study. We provide an overall description of the study’s design framework and baseline data of participants.
Results Between November 2021 and March 2023, 162 263 individuals aged over 18 from 18 cities in China were screened, of those 43 279 high-risk individuals and 6902 patients with COPD were enrolled in the cohort study. In the high-risk population, the proportion of smokers was higher than that in the screened population (57.6% vs 31.4%), the proportion of males was higher than females (71.1% vs 28.9%) and in people underweight than normal weight (57.1% vs 32.0%). The number of high-risk individuals increased with age, particularly after 50 years old (χ2=37 239.9, p<0.001). Female patients are more common exposed to household biofuels (χ2=72.684, p<0.05). The majority of patients have severe respiratory symptoms, indicated by a CAT score of ≥10 (85.8%) or an Modified Medical Research Council Dyspnoea Scale score of ≥2 (65.5%).
Conclusion Strategy based on IoT model help improve the detection rate of COPD in PHC. This cohort study has established a large clinical database that encompasses a wide range of demographic and relevant data of COPD and will provide invaluable resources for future research.
- Pulmonary Disease, Chronic Obstructive
Data availability statement
No data are available. Researchers interested in collaboration and further information are invited to contact the corresponding author XZhang.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Delayed diagnose, discontinuous management of chronic obstructive pulmonary disease (COPD) due to the shortage of medical resources and slow network construction seriously hinder the efficiency of COPD control in primary healthcare (PHC). There is a paucity of research on the application of the internet of things (IoT) systems to integrate large-scale PHC medical data.
WHAT THIS STUDY ADDS
The COPD PHC cohort study is the largest study to integrating IoT system and PHC medical data for early diagnosis and whole-course management of COPD in China, which includes high-risk individuals and COPD identified through screening process, aiming to meet the urgent demand for potential risk factors and related data of COPD development.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
This study offers a technique model that can be replicated and easily scaled up in low-resource settings. It captures varieties of high-risk population and diverse COPD phenotypes, which contribute to the exploration of COPD heterogeneity and biomarkers.
Introduction
Chronic obstructive pulmonary disease (COPD) is an increasingly important cause of morbidity, disability and death worldwide. A globally estimated 390 million people aged 30–79 had COPD in 2019, more than three-quarters occurring in low-income and middle-income countries, particularly in the Western Pacific region with an overall prevalence of 11.7%.1 COPD is the sixth-leading cause of of DALYs or health loss globally for both sexes combined, all ages,2 accounting for an astonishing 81.7% of the total number of deaths from chronic respiratory diseases and bringing about significant social and economic consequences.3 In the European Union, COPD accounts for an estimated annual economic burden of €25.1 billion, and wider societal costs are likely to be much greater.4 In the USA, the costs attributable to COPD are expected to increase over the next 20 years, with an estimated cost of US$800.9 billion.5 It has been shown that counties with higher Social Vulnerability Indexes, a comprehensive tool to assess disaster-related sociodemographic risks at the county level, had considerably higher COPD death rates.6 In low-income and middle-income countries, both direct and indirect medical expenses attributable to COPD pose a serious threat to their economy.7–10 Hence, it is essential to motivate policy-makers and funders to provide political and financial support and encourage researchers to carry out relevant research for the prevention and control of COPD, especially in low-resource settings.11 12
COPD is initially diagnosed and mainly managed in primary healthcare (PHC). PHC-based programmes were reported to be an effective strategy to promote early diagnosis and management of COPD both in Europe and Latin America.13 14 In UK, with a relatively well-established PHC system, carrying on screening questionnaires before spirometry is reported as a cost-effective way to identify high-risk groups and undiagnosed patients and has the potential to improve their health.15 While there is currently a lack of medical record integration between general hospitals and primary care, numerous online assessment and management tools for PHC have been developed in Australia by some groups to boost the knowledge and abilities of associated professionals.16 In Asian countries such as Nepal, community-based screening is proved to be helpful for the management and referral for patients with COPD.17 18 China accounts for one fifth of the world’s population, among which there are nearly 100 million patients with COPD and one-third of the global deaths owing to COPD.19 In recent years, China has introduced a series of prevention and control strategies at different level of national policy, association and scientific research,20 nearly 50% of PHC institutions within the country have been equipped with pulmonary function apparatus, homogeneous training for PHC professionals and some of the medications for COPD treatment added to national lists of essential drugs, which has greatly promoted the early diagnosis and control of COPD.21 22 However, there are still challenges existing, such as the shortage of human resources, slow network construction, and information connectivity, and all of these hinder the efficiency of COPD prevention and management.23 Screening and whole-course management programme require a great deal of manpower and material resources, and therefore, lead to the question of how to persuade a busy PHC professional to perform such a heavy workload for a very long period of time. Hence, more efficient and appropriate technologies should be adopted to better the prevention and management of COPD in PHC accordingly.
Henan province is located in the central part of China and has approximately 75 174 PHC institutions serving 99.37 million populations. It is estimated that there are about 8.49 million patients with COPD in Henan province according to the China Pulmonary Health study,19 with the per capita disposable income level ranking low nationwide. In order to meet the urgent demand for potential risk factors and related data of COPD in the resource-limited environment, we developed an online integrated management system through internet of things (IoT) to integrate diverse digital data from 289 PHC institutions, and design the COPD PHC cohort study comprising high-risk population and patients with COPD, a total of 162 263 residents aged over 18 years were screened in the baseline survey.
Study objective
The key objectives of the COPD PHC cohort study are to (1) characterise the local aetiology of COPD in China in terms of risk factors and lifestyles in different geographical areas; (2) explore the natural course and diverse phenotypes of COPD through dynamic monitoring of clinical characteristics and pulmonary function, and further explore new blood and urine biomarkers; (3) evaluate inhalation medications on the efficacy of diverse phenotypes of COPD with long-term follow-up and the enhanced patient selection for therapeutic agents and (4) explore the long-term effectiveness of the IoT system in the management of high-risk population and patients with COPD.
Methods
Study design
This study is a prospective cohort study which is implemented across PHC institutions in China. It consists of two stages: the baseline and long-time follow-up survey. The baseline survey has started from November 2021, a structured process including face-to-face electronic questionnaire interview and spirometry before and after bronchodilators is applied (figure 1). All confirmed high-risk participants and patients with COPD will be notified at a specific time through short messaging service (SMS), WeChat platform, telephone or other forms for taking follow-up interviews and completing pulmonary function tests. The follow-up intervals are determined according to clinical guidelines, high-risk participants are recommended to be examined every 12 months, patients with severe or higher COPD (forced expiratory volume in 1 s (FEV1) %<50% of the estimated value) to be examined every 6 months, and patients with mild or moderate COPD (FEV1 %>50% of the estimated value) to be examined annually. Figure 2 shows the overall study plan.
Structured work flow diagram of the baseline survey. COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1s; FVC, forced vital capacity.
Study plan of the COPD primary healthcare cohort study. CAT, COPD Assessment Test; COPD, chronic obstructive pulmonary disease; mMRC, the modified Medical Research Council Dyspnoea Scale.
Setting and participants
In stage 1, we selected at least one representative general hospital as regional centre in every city of Henan province in consideration of the population stability and medical conditions. Locations of 33 regional centres are shown in figure 3. In stage 2, we selected more than five PHC institutions in consideration of the basic medical facilities, trained general practitioners and supportable network construction under every regional centre. A total of 289 PHC institutions have been identified from November 2021 to March 2023.
Locations of the 33 regional centres (red dots) in the model. More than five primary healthcare institutions were selected under every regional centre. The map of China was downloaded from the National Administration of Surveying, Mapping and Geoinformation (drawing review number was GS(2019)1676). The map of Henan province was generated by the Map Institution of Henan Province.
Community permanent residents aged over 18 years are enrolled and screened from November 2021 to March 2023, with their unique identification code. Persons with any of the following conditions were excluded from this programme: (1) under 18 years of age; (2) suffering from severe cognitive impairment such as dementia; (3) suffering from severe mental illness (auditory hallucinations, visual hallucinations, seizures requiring medication, etc); (4) refusing to participate and follow-up. High-risk participants are defined as those whose total score of the COPD screening questionnaire is no less than 16 points.24 The postbronchodilator FEV1:forced vital capacity (FVC) ratio less than 0.70 was defined as COPD, according to the 2021 Global Initiative for Chronic Obstructive Lung Disease guidelines.
Data collection
At the baseline survey, a structured questionnaire including demographic data and questions regarding COPD is used for the evaluation of the participants by trained staff. The items include (1) demographic characteristics (name, sex, age, date of birth, telephone number and home address); (2) lifestyles (smoking status and household biofuel exposure); (3) anthropometric measures (height, weight and body mass index); (4) personal respiratory symptoms (chronic cough and dyspnoea); (5) personal medical history (including cardiovascular diseases, lung cancer, obstructive sleep apnoea, gastro-oesophageal reflux disease, metabolic syndrome and diabetes, anaemia, osteoporosis and depression/anxiety) and (6) family history of respiratory diseases (including chronic bronchitis, asthma, emphysema and COPD). Pollutant exposure data were obtained from the website of ‘Department of Ecology and Environment of Henan Province’ according to their home address of participants.
Spirometry is conducted on high-risk participants by trained technicians with IoT-enabled portable spirometer (X1, XEEK, Xiamen, China and SMPF-1, Sonmol, Shanghai, China), which can automatically do quality control according to the American Thoracic Society and European Respiratory Society criteria. We did all spirometric manoeuvres with the participant in a seated position, wearing a nose clip and using a disposable mouthpiece, at least three forced expiratory manoeuvers are required until the results could be reproducible well. For bronchodilator test, we administer a bronchodilator (salbutamol 400 µg) by inhalation and repeated spirometry 30 min later, using the same criteria. The main test results include FVC, FEV1, peak expiratory flow, forced expiratory flow at 25 of FVC (FEF25), FEF at 50 of FVC (FEF50), FEF at 75 of FVC (FEF75) and maximum mid expiratory flow. Once diagnosed, health status scores (Modified Medical Research Council Dyspnoea Scale (mMRC) and COPD Assessment Test (CAT)), comorbidities and current therapeutic medications (including short-acting β-agonists (SABA), short-acting muscarinic antagonist, long-acting β-agonists (LABA), long-acting muscarinic antagonist (LAMA), inhaled corticosteroids (ICS), LABA+ICS, LAMA+LABA and LAMA+LABA+ICS) of these participants are to be recorded.
Follow-up survey contains smoking status alteration (including smoking cessation and smoking index), COPD-associated lifestyles changing, pulmonary function tests, health status scores changing (mMRC and CAT), frequency and severity of exacerbations, adherence to the current dosage regimen. Fasting blood sample (at least 8 hours) and urine sample are drawn from specific high-risk participants and patients with COPD to establish the biobank in the subgroup. An overview of data collection is shown in table 1.
Summary of data collected in the COPD primary healthcare cohort study
Data sources of the IoT-enabled COPD management system
To conduct this complex multilevel study, an online IoT-enabled integrated management system has been developed to integrate diverse digital data from 33 regional centres and 289 PHC institutions (figure 4). This system incorporates a web-based interactive database for the collection and management of structured data generated during baseline and follow-up surveys. It adopts a split architecture that separates data storage from business operations. In this system, data from PHC institutions are synchronously connected with their respective superior regional centres. Clinicians of region centres log in this management system through exclusive account and can access the historical medical records of each participant in the system according to their unique identification code, then they provide corresponding advice to PHC institutions. Meanwhile, a mobile application has been developed for more than 7500 trained PHC professionals to capture and collect medical data related to COPD, which will be stored in the cloud-based database that can also be integrated with various big data analytics tools. IoT technologies were used to integrate the pulmonary function test results between different levels of hospitals. Specifically, structured data processed by MySQL+Java is stored in MySQL for clinicians to consult and supports third-party data interconnection.
The internet of things (IoT)-enabled COPD integrated management system in Henan province, China (created with BioRender.com). COPD, chronic obstructive pulmonary disease.
For the purpose of data security, each professional in this system is assigned an exclusive account and all the operation logs are correspondingly preserved. With regard to data permission, records of certain participates can only be accessed by relevant PHC professional and clinicians from their superior regional centres. Technically speaking, data transmission is encrypted twice based on hypertext transfer protocol secure (HTTPS) encryption to protect participant’s privacy.
Quality control
Prior to the implementation of the study, we conducted standardised training for over 7500 PHC professionals in this project, including measures such as designated on-site operation manuals and the details of the entry of each question. All professionals adopted a unified technical solution and operational process. The electronic structured questionnaire and pulmonary function results are transmitted automatically to the COPD integrated management platform through the cloud database, so as to avoid errors caused by manual input. Project administrative meetings are held regularly to summarise, discuss, and develop potential coping strategies for emerging problems and concerns.
Patient and public involvement
Residents were enrolled in the baseline survey according to the inclusion and exclusion criteria above. Confirmed high-risk participants and patients will be notified at different times through SMS, WeChat platform, telephone or other means according to the severity of their condition, to arrange follow-up interviews and complete pulmonary function tests. All examination results will be communicated to the patients on the same day after the examinations are completed.
Statistical analysis
Outcome measures were summarised as means with SDs for continuous variables or numbers with percentages for categorical variables. Differences of characteristics between groups were examined by χ2 test for categorical variables. Statistical significance was defined as p<0.05.
Results
162 263 participants—88 814 men and 73 449 women—were screened between November 2021 and March 2023, all individuals had complete basic information and eligible questionnaire results. The overall proportion of high-risk population defined by the questionnaire was 26.7%, with a total of 43 279 participants. 6902 were diagnosed with COPD out of the 22 907 participants who had completed pulmonary function tests.
Baseline characteristics of participants
The detection rate of COPD in high-risk groups (6902/22 907) is much higher than that in the screening population (6902/162 263) (30.1% vs 4.3%). In the high-risk population, the mean age and the proportion of smoking are higher than that of screening population (70.1 vs 58.0 years and 57.6% vs 31.4%, respectively). The proportion of men is much higher than that of women (71.1% vs 28.9%), and the absolute number of high-risk population increased with age in both male and female, especially after 50 years old. The high-risk proportion of underweight individuals is much higher than that of individuals with normal weight. (57.1% vs 32.0%). The proportion of high-risk population was higher in rural areas than in urban areas (30.8% vs 25.2%, t=493.9, p<0.001) and higher in Southern Henan areas (35.4%) than in others (19.5%–32.6%) (table 2). A dose–response association was noted between smoking index and the proportion of high-risk population. Symptoms of airway obstruction (chronic cough and dyspnoea) and household biofuel exposure were more common in high-risk population, especially in female (tables 2 and 3).
The baseline characteristics of the overall screened and high-risk participants in the COPD primary healthcare cohort study
The comparison baseline of high-risk and non-high-risk participants in the COPD primary healthcare cohort study
General characteristics of patients with COPD are presented in table 4. Of these, there were more male patients than females (80.2% vs 19.8%). 96.4% of patients were over 40 years old, and the proportion of both male and female with COPD increased with age. In the male patients, smokers accounted for 82.6% of the total number of patients, much higher than the percentage in female (11.5%). The percentage of household biofuel exposure was higher in the female patients than that in males, which may contribute to their course (χ2=72.684, p<0.05). Additionally, higher scores of mMRC (65.5%) and CAT (85.8%) were found in patients in both two groups, indicating more clinical symptoms of our patients, but no significant differences were found between males and females (χ2=0.025, p=0.875 and χ2=1.859, p=0.173, respectively). The classifications of inhalation medications used are also shown in table 4. Therapeutic regimens were different between patients, and the absolute number of dual therapy with ICS+LABA or LABA+LAMA (30.2%) was higher than monotherapy (29.4%) and triple combinations (10.0%).
General characteristics of 6902 patients with COPD in the COPD primary healthcare cohort study
Comorbidities
1534 patients with COPD coexisted with other diseases (comorbidities), the incidence rates of concurrent respiratory system diseases and cardiovascular diseases in patients with COPD are 76.3% and 73.5%, respectively. Among the concurrent respiratory system diseases, pneumonia accounts for 39.2%, followed by respiratory failure (20.3%), bronchiectasis (11.5%) and lung cancer (5.5%).
Discussion
Strengths and weaknesses
The biggest strength of this model is in person-centred continuity of care. First, it is unique to establish a multistage and interconnected COPD prevention and treatment network featuring 289 PHC institutions based on the IoT with the substantial support from the government and financial funds, and conduct standardised training for over 7500 PHC professionals, covering clinical medicine, public health and preventive medicine, nursing, management science and other fields. It provides an efficient and rapid organisational framework, allowing for screening and managing tens of thousands of high-risk groups and patients with just a few hundred doctors, greatly improving the utilisation of medical resources, and this model has been proven to improve the diagnosis rate of COPD in our study. Second, integrating an IoT system enhances data collection, management and communication between healthcare providers across different levels, which contributes to achieve the hierarchical healthcare, greatly improves the efficiency of service providers and reduces repeated examination. Finally, population follow-up is linked to online integrated management system through the mobile application and IoT-based portable spirometer, providing a basis for ongoing interaction between practice and population, ultimately improving medication compliance in patients.
COPD is complex and heterogeneous, involving varying degrees of airway remodelling, inflammation and tissue damage. This heterogeneity manifests as wide variations in respiratory symptoms and clinical outcomes among patients. Therefore, it is necessary to explore multiple biomarkers and combinations to develop targeted and accurate management strategies for different phenotypes of COPD. In the COPD PHC cohort study, we will capture varieties of high-risk population and diverse clinical phenotypes of COPD, which will contribute to the exploration of COPD heterogeneity and related diagnostic biomarkers based on robust data and large samples in the future.
Pollutant exposure is also a crucial element of the COPD PHC cohort study for exploring the potential relationship between pollutant exposure and the outcomes of COPD. PM2.5, NO2 and O3 are increasingly being considering to associate with a higher incidence of COPD.25 This may partly explain the difference in the detection rate of COPD between urban and rural areas. Our study is ongoing with a long-term follow-up design, we will be able to provide a comprehensive analysis of the potential association between pollutant exposure and COPD development in subsequent analyses.
Due to the huge amount of system operation data, there may be homogeneity bias of data from various institutions. However, effective training of professionals and good field implementation will ensure the accuracy and reliability of the information, and all the questionnaire and pulmonary function tests conducted for individuals are free, we communicated fully with them before the examination to improve compliance of all examinees, and to reduce the bias. This can also be seen from our detection rate of high-risk population (26.7%), which is similar to that reported before in China.26
Conclusion
The population screening and whole-course management of COPD using the IoT-enabled model has been proven to significantly reduce the amount of labour and material resources needed in PHC institutions. It offers a technique that can be easily replicated and expanded in regions with limited access to healthcare. And, this database comprises a wide range of individual demographics and related data of COPD, which will provide invaluable resources for scientific research in the future.
Supplemental material
Data availability statement
No data are available. Researchers interested in collaboration and further information are invited to contact the corresponding author XZhang.
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and was approved by the ethics review committee of Henan Provincial People’s Hospital (No. 2022042). Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The COPD PHC Cohort Study is conducted by multiple research groups under the project management of the integrated System. We gratefully appreciate the contribution of staff in the study areas: Zhumadian Central Hospital, Zhengzhou People's Hospital, Zhoukou Central Hospital, Xinxiang First People's Hospital, Shangqiu First People's Hospital, Anyang People's Hospital, the First Affiliated Hospital of Nanyang Medical College, Luohe Central Hospital, the First Affiliated Hospital of Henan University of Science and Technology, the Second People's Hospital of Jiaozuo, Xinyang Central Hospital, Jiyuan People's Hospital, Puyang People's Hospital, Luohe Third People's Hospital, the First People's Hospital of Nanyang City, Yongcheng Central Hospital, Xuchang Central Hospital, Hebi City People's Hospital, Pingdingshan First People's Hospital, Yongcheng City People's Hospital, Luoyang Central Hospital, Hua County People's Hospital, Pingdingshan General Hospital of what Medical Group, Zhongmou County People's Hospital, Nanyang City Central Hospital, Gongyi City People's Hospital, Henan University Huaihe Hospital, Sanmenxia Central Hospital, Xincai County People's Hospital, Dengzhou Central Hospital, Sanmenxia Yellow River Hospital, the First Affiliated Hospital of Zhengzhou University and 289 primary medical institutions in Henan province. As data collection is still ongoing, the number of collaborative groups will continue to increase in the future.
Supplementary materials
Supplementary Data
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Footnotes
XZ and HK contributed equally.
Contributors XZhao, HK and ZX contributed to data collection, analytical strategy design and data interpretation. YA, ZX and XZhang contributed to the study conception and design. LD, ZG directed the study's implementation. MW and QZ helped to data curation. All authors contributed to the material preparation. The original draft was written by XZhao, all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. XZhang is responsible for the overall content as the guarantor.
Funding This work was supported by the 'Health Central Plains' COPD Screening and Management Project (2021020), Zhengzhou Collaborative Innovation Major Project (20XTZX09019) and Science and Technology Major Project of Henan Province (201300310500).
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Competing interests None declared.
Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.
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Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.