Skip to main content
NCBI home page
NPJ Primary Care Respiratory Medicine logoLink to NPJ Primary Care Respiratory Medicine
. 2026 May 12;36:66. doi: 10.1038/s41533-026-00508-3

Prevalence, awareness, treatment and control of chronic obstructive pulmonary disease (COPD) among community-dwelling adults aged 40 years and older in regional China

Jian Kang 1,#, Huiqing Xu 2,3,#, Qing Ye 2,3, Zhiyong Wang 2,3, Caihong Hu 2, Yunting Xu 2,3, Guofeng Ao 2,3, Xiaojing Deng 4,✉, Yeping Bian 4, Jian Xu 4, Fei Xu 2,3,✉
PMCID: PMC13627666  PMID: 42120402

Abstract

This study aimed to investigate the prevalence, awareness, treatment and control rates of chronic obstructive pulmonary disease (COPD) and their influencing factors among adults in regional China. In 2023, 6403 community-dwelling residents aged 40 years and older were randomly chosen from Nanjing municipality of China. COPD was determined as self-reported physician-diagnosed patients or post-bronchodilator FEV1/FVC < 0.70, and without other lung function impaired diseases. Multivariate logistic regression models were introduced to identify influencing factors for each rate. Totally, 5605 participants were analyzed. The spirometry-based COPD prevalence, awareness, treatment and control rates were 15.4%, 4.4%, 2.4% and 56.8%, separately, while the corresponding age- and sex-standardized rates were 15.5%, 4.6%, 2.4% and 56.5%, respectively. Age, marital status, education, physical activity and body weight status significantly correlated with COPD prevalence. Among COPD patients, these with higher educational level, chronic respiratory symptoms, or family history, and former smokers were more likely to be aware of COPD. Those smoking formerly, aged 60–79 years, with chronic respiratory symptoms, family history of COPD, higher educational level, or physical inactivity were more likely to receive treatment. Additionally, patients living in urban areas tended to have the disease under control. The prevalence of COPD among adults aged 40 years and older was high, but awareness, treatment and control rates remained limited in regional China. For the purpose to reduce COPD burden, it shall be a priority for policy-makers to initiate/provide effective and accessible education and lung function screening programs of COPD for adults in China.

Subject terms: Diseases, Health care, Medical research, Risk factors

Introduction

Chronic obstructive pulmonary disease (COPD) is a common lung disease characterized typically by irreversible airflow limitation and persistent respiratory symptoms, primarily caused by long-term exposure to smoking or/and air pollution1. Its symptoms include chronic cough, sometimes with phlegm, shortness of breath, and tiredness1. COPD is regarded as an age-related disease and leads to disability and premature death2,3. In China, COPD has ranked among the top three leading causes of death4. Moreover, the numbers of COPD deaths would continue to increase by approximately 1.5 times from 2019 to 2045 in China5. Therefore, COPD has become a major public health concern for China.

Improving awareness, treatment and control rates is the key for comprehensively secondary and tertiary prevention of COPD6. Clinically, the insidious onset makes the early diagnosis and treatment of COPD a big challenge worldwide7,8. Assessing awareness rate can help identify gaps in diagnosis of COPD, enabling targeted interventions and subsequent treatment. Additionally, investigating treatment and control rates can provide constructive insights into healthcare accessibility and adherence, guiding improvements in medical resource allocation and patient support systems.

With the rapid economic growth and population ageing, China has been witnessing an increasing prevalence of COPD from 8.2% in 2004 to 13.6% in 2015 among adults aged 40 years or above9–12. However, there are limited studies documenting the awareness, treatment and control of COPD and their corresponding influencing factors among adults in China. To address this gap, this study was developed to investigate the prevalence, awareness, treatment and control rates of COPD and their influencing factors among community-dwelling adults aged 40 years and older in regional China.

Methods

Study design and participants

This study was a population-based cross-sectional survey, conducted in Nanjing municipality of China in 2023. Nanjing, the capital of Jiangsu province in eastern China, had a registered population of 9.3 million in 12 urban/suburban districts in 202013. If a participant was eligible to take part in the survey, he/she must: 1) live in Nanjing for at least six months in the past year (a regular inhabitant), 2) be 40 years old or above, 3) have no literal or cognitive/mental problems, 4) not be experiencing any active infectious diseases, and 5) be able to do lung function test.

The sample size was estimated based on: 1) the previously-documented spirometry-based COPD prevalence (11.3%) among adults aged 40 years or above in Jiangsu province in 201914, 2) design efficiency of a cross-sectional survey, 3) multi-stage sampling approach, 4) statistical power (90%) expected, and 5) response rate (85%) assumed. Therefore, it was calculated that approximately 5077 participants would be sufficient for this study.

Participants were randomly determined with a multi-stage selection strategy. First, eight districts (urban vs. suburban: 1 vs. 1) were randomly chosen from all the 12 districts of Nanjing municipality. Second, four streets/towns were selected from each chosen district. Third, one or two neighborhoods/villages were determined from each selected street/town based on the street-/town-to-district proportion of residents aged 40 years or above. Fourth, 60 households were randomly recruited from each determined neighborhood/village. Finally, all individuals aged 40 years or above in the recruited households were invited to participate in the survey and lung function test. Consequently, 6403 participants were determined in this study. Figure 1 displays the flow chart of participant’s selection.

Fig. 1. The flow chart of participant’s selection.

Fig. 1

Open in a new tab

Initially, 6403 participants were randomly selected for this survey. Of them, 5840 were successfully recruited. Finally, 5605 subjects completing lung function tests were included in analysis.

All participants provided informed consent before the survey. Data were de-identified prior to analysis. The study was approved by the Ethics Committee of the Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention. Regarding experimental methods, this study followed the principles documented in the Declaration of Helsinki.

Data collection and measures

The questionnaire used in this study was adopted from the “Chinese Adult Chronic Disease and Nutrition Surveillance Questionnaire”, which was developed by the National Center for Chronic Non-communicable Disease Prevention and Control of Chinese Center for Disease Control and Prevention (CCDC)15. Investigation information included participants’ socio-demographic characteristics (including age, gender, living area, educational attainment, marital status and income), potential influencing factors of COPD (including physical activity [PA], smoking history, cooking environment and occupational exposure), self-reported history of exacerbations and weekly use of rescue medications in the past three months, degree of dyspnea, personal history of relevant diseases and family history of COPD. All information was gathered by trained investigators via face-to-face interview in local community health service centers.

Lung function tests

Lung function tests were performed by registered respiratory physicians using portable spirometers (Flow Screen, Jaeger, Germany). All participants firstly underwent baseline lung function test. Next, for those without contraindication, they inhaled 400 μg of salbutamol aerosol. Then, 15 minutes later, they received post-bronchodilator lung function test. The main measures of lung function included forced expiratory volume in one second (FEV1) and forced vital capacity (FVC).

Outcome variables

The outcome event was COPD, which was defined based on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria16. A participant was defined as a COPD patient, if he/she: 1) had chronic respiratory symptoms or/and a history of exposure to risk factors for COPD, together with a post-bronchodilator FEV1/FVC < 70%, and no other lung-function-impaired diseases (such as self-reported asthma, tuberculosis, lung cancer or pulmonary heart disease), or 2) had been diagnosed with COPD by a registered respiratory physician (already-known COPD), regardless of their lung function tested at the survey time10. Notably, GOLD standards were employed for physicians to make a clinical diagnosis of COPD in China17, implying already-known COPD patients were ever diagnosed with spirometry-based lung function tests. Those with FEV1/FVC < 70% and not previously-diagnosed with COPD were considered as newly-identified COPD cases. Thus, all newly-identified and already-known COPD cases were determined in line with GOLD criteria in this study16,17. Totally, 34 participants with these other lung-function-impaired diseases were excluded based on the definition of COPD. Additionally, COPD was categorized into four GOLD-based types based on predicted FEV1%: stage 1 (FEV1% ≥ 80%), stage 2 (50% ≤FEV1% < 80%), stage 3 (30% ≤FEV1% < 50%), or stage 4 (FEV1% < 30%)15.

The outcome variables included prevalence, awareness, treatment and control rates of COPD. Prevalence of COPD was defined as the proportion of all determined (both already-known and newly-identified) COPD patients among overall participants. Awareness, and treatment rates of COPD referred to the proportion of already-known COPD patients, those patients receiving clinical treatment (including medication or oxygen therapy) in the past year, respectively, among all determined COPD patients10,11.

With respect to control of COPD, in the recently-released Global Strategy for Prevention, Diagnosis and Management of COPD: 2026 Report, it was described as a state of low disease activity, referring to no exacerbation, no worsening of symptoms and no substantial impact on the patient, which was comprehensively assessed using the RADAR (Rescue medication, Acute exacerbations, Dyspnea, PA, and Risk) score18,19. The RADAR score was estimated with consideration of weekly rescue medication use and acute exacerbations in last three months, dyspnea severity (indicated with Modified Medical Research Council scale adjusted for post-FEV1), and daily average time of PA18,19. As for scoring, 3 points were assigned to weekly frequency of rescue medication use >3 times/week and 2 points assigned to ≥1 moderate or severe exacerbation in the last 3 months, 2 points assigned when post-FEV1 was ≥50% and dyspnea was ≥2, or post-FEV1 was <50% and dyspnea was ≥3, and 1 point assigned to daily average walk <30 minutes, resulting in a total score ranging from 0 to 8 points18,19. The lower the RADAR score predicts the better state COPD control, with a score of 0–1 indicating well-controlled state of COPD18,19. In the present study, a RADAR score was calculated for each individual with COPD.

A sub-questionnaire (Appendix 1), also recommended by CCDC for community-based COPD survey, was employed to gather information on COPD 15. The responses to questions of “used inhaled medications to help you breathe (item 6)”, and “received treatment due to worsening respiratory symptoms” were considered together to create a proxy of “use of rescue medication≥3 times/week (item 9)”. Only for participants who received ≥3 times/week treatment due to worsening respiratory symptoms and simultaneously took inhaled rescue medications to improve symptoms, they were classified as “use of rescue medication ≥3 times/week”, and then 3 points were assigned for estimating RADAR score. A patient was categorized as having the disease under control if he/she had a RADAR score of 0–1. Then, the rate of COPD control was defined as the proportion of patients having the disease under control among all determined COPD patients.

Covariates

Socio-demographic characteristics included age (40–59, 60–79 or 80+ years), sex (men or women), living areas (urban or sub-urban), educational attainment (≤6, 7–9 or ≥10 years of schooling), marital status (married/having a partner or no spouse/partner), annual household income per capita (lower [<¥15,600], middle [¥15,600–33,332] or upper tertile [≥¥33,333]). Self-reported chronic respiratory symptoms were defined as chronic cough, chronic expectoration, wheezing, or dyspnea20. PA level and walking time in the past 7 days was assessed using the Chinese version of International Physical Activity Questionnaire (IPAQ-CHN) for each participant, separately21. The moderate plus doubled vigorous PA (MVPA) time was calculated for categorizing participants into: “sufficient (MVPA ≥ 150 minutes/7 days) PA” or “insufficient (MVPA < 150 minutes/7 days) PA”21. Positive family history of COPD referred to that at least one parent had been diagnosed as COPD patient. As for smoking status, participants were classified as current-, former- or never-smokers22. Passive smoking was defined as an exposure to second-hand tobacco smoke for at least 15 min/day23. Additionally, participants were classified as “having a history of exposure to polluted fuels during cooking or/and heating” or “no such an exposure”24, “having a history of occupational exposure to dust/harmful gases during work or agricultural production” or “no such an exposure”25.

The height and weight were assessed, with light clothing and no shoes, twice for each participant by trained investigators using calibrated automatic height and weight measuring instruments. Based on the mean values of height and weight readings, body mass index (BMI) was calculated as weight (kg) divided by height squared (m²) for participants. Then, body weight status was categorized into: underweight (BMI < 18.5 kg/m²), normal (18.5 ≤ BMI < 24 kg/m²) or overweight/obese (BMI ≥ 24 kg/m²), according to recommendations for Chinese adults26.

Statistical analysis

First, categorical variables were expressed as numbers (percentages), and chi-square test was used to compare the differences in COPD prevalence, awareness rate, treatment rate and control rate between participants with different socio-demographic characteristics. The COPD prevalence, rates of awareness, treatment and control were also age- and sex-standardized based on the 2020 Jiangsu province census data27. Then, multivariable logistic regression models were introduced to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for identifying the influencing factors of COPD prevalence, awareness rate, treatment rate and control rate, separately. All statistical analyses were two-tailed with the significance level at p < 0.05. Data were analyzed using R 4.1.3 software.

Results

Characteristics of participants

Table 1 presents the selected characteristics of participants by residence area. Of the 6403 participants determined for this survey, 5840 were successfully recruited (response rate=91.2%). Due to contraindications for lung function test, 235 participants were excluded from spirometry, resulting in 5605 subjects were included in analysis. Between participants included and not included in analysis, there was no difference in age, sex, or area. For the 5605 subjects analyzed, the mean age was 59.3 years (SD = 9.6), while 47.1% and 38.9% were men and urban residents, respectively. The prevalence of smoking was 22.6% (1,265/5,605) among overall participants, while a substantial disparity of smoking prevalence was observed in men (46.9% = 1,240/2,642) and women (0.8% =25/2,963). However, notably, 72.0% of participants were never-smokers, while 5.4% were ex-smokers (Table S1).

Table 1.

Selected characteristics of participants by area in this study.

Characteristics % (n) of participants
Overall Urban* Suburban*
Overall 100.0% (5,605) 38.9% (2,179) 61.1% (3426)
Age group (years)
 40–59 52.2% (2,923) 36.5% (796) 62.1% (2,127)
 60–79 46.3% (2,596) 60.9% (1,328) 37.0% (1,268)
 ≥80 1.5% (86) 2.5% (55) 0.9% (31)
Gender
 Man 47.1% (2,642) 47.1% (1,027) 47.1% (1,615)
 Woman 52.9% (2,963) 52.9% (1,152) 52.9% (1,811)
Education (schooling years)
 ≤6 39.6% (2,220) 21.2% (462) 51.3% (1,758)
 7–9 33.5% (1,880) 33.3% (725) 33.7% (1,155)
 ≥10 26.9% (1,505) 45.5% (992) 15.0% (513)
Marital status
 Married 92.8% (5,201) 92.1% (2,007) 93.2% (3,194)
 No spouse 7.2% (404) 7.9% (172) 6.8% (232)
Annual income level (tertiled) a
 Lower 32.2% (1,805) 27.7% (603) 35.1% (1,202)
 Middle 33.6% (1,886) 29.9% (652) 36.0% (1,234)
 Upper 34.1% (1,914) 42.4% (924) 28.9% (990)
Chronic respiratory symptoms b
 Yes 18.8% (1,052) 17.5% (382) 19.6% (670)
 No 81.2% (4,553) 82.5% (1,797) 80.4% (2,756)
Physical activity c
 Sufficient 76.8% (4,302) 79.7% (1,737) 74.9% (2,565)
 In-sufficient 23.2% (1,303) 20.3% (442) 25.1% (861)
Family history of COPD d
 Yes 3.0% (166) 4.1% (90) 2.2% (76)
 No 97.0% (5,439) 95.9% (2,089) 97.8% (3,350)
Smoking history
 Never-smokers 72.0% (4,038) 73.7% (1,605) 71.0% (2,433)
 Current smokers 22.6% (1,265) 19.8% (431) 24.3% (834)
 Former smokers 5.4% (302) 6.6% (143) 4.6% (159)
Passive smoking e
 Yes 35.3% (1,977) 74.6% (1,626) 58.4% (2,002)
 No 64.7% (3,628) 25.4% (553) 41.6% (1,424)
BMI group (kg/m²) f
 <18.5 1.2% (70) 1.5% (33) 1.1% (37)
 18.5–23.9 36.3% (2,033) 37.3% (812) 35.6% (1,221)
 ≥24 62.5% (3,502) 61.2% (1,334) 63.3% (2,168)
History of exposure to polluted fuels g
 Yes 3.3% (183) 0.3% (6) 5.2% (177)
 No 96.7% (5,422) 99.7% (2,173) 94.8% (3,249)
History of occupational exposure h
 Yes 16.8% (944) 14.6% (319) 18.2% (625)
 No 83.2% (4,661) 85.4% (1,860) 81.8% (2,801)
Open in a new tab

*The study employed a sampling design stratified by urban and suburban residential areas to enable comparative analysis of COPD characteristics across these distinct setting.

aAnnual income level was tertiled as lower (<¥15,600), middle (¥15,600-33,332) and upper (≥¥33,333) based on annual household income per capita.

bChronic respiratory symptoms were defined as self-reported presence of at least one of the followings: chronic cough, chronic expectoration, wheezing or dyspnea.

cPhysical activity was measured by moderate plus doubled vigorous physical activity (MVPA) time, and classified into having “sufficient (MVPA ≥ 150 minutes/7 days)” or “insufficient (MVPA < 150 minutes/7 days)”.

dFamily history of COPD (chronic obstructive pulmonary disease) referred to that at least one parent had been diagnosed as COPD patient.

ePassive smoking was defined as an exposure to second-hand tobacco smoke for at least 15 min/day at home or in the workplace.

fBMI (body mass index) group was categorized using recommendations for Chinese adults.

gHistory of exposure to polluted fuels was defined as having a history of exposure to biomass fuels (e.g., coal, straw, wood, and animal dung) during cooking or/and heating.

hHistory of occupational exposure was defined as having a history of occupational exposure to dust/harmful gases during work or agricultural production.

Prevalence of COPD among participants

Table 2 shows the prevalence of COPD among participants. There were 863 participants determined with COPD, and the crude prevalence of COPD was 15.4% (95% CI = 14.5%, 16.3%) among overall participants. After age- and sex-standardization, the overall prevalence of COPD remained relatively stable (15.5%; 95% CI = 14.6%, 16.4%). The crude prevalence of COPD increased with age in either men or women (Fig. 2). Moreover, the prevalence of COPD significantly differed in participants by age, educational attainment, marital status, income, PA, body weight status, or history of exposure to polluted fuels (p < 0.05, respectively). However, there was no difference in COPD prevalence between men and women (p = 0.75). Among never-smokers, similar patterns of differences in COPD prevalence were observed among participants stratified by age, educational attainment, marital status, income, PA, or history of exposure to polluted fuels (p < 0.05, respectively) (Table S1). Additionally, among the 863 COPD patients, the majority fell into stage 2 (43.9%), followed by stage 3 (24.4%).

Table 2.

Prevalence of COPD among residents aged 40 or above in Nanjing municipality, China.

Characteristics Overall participants, N All COPD a Stage 1 COPD b Stage 2 COPD c Stage 3 COPD d Stage 4 COPD e
COPD patients, n Prevalence, % (95% CI) f Stage 1 COPD patients, n Prevalence % (95% CI) f Stage 2 COPD patients, n Prevalence, % (95% CI) f Stage 3 COPD patients, n Prevalence, % (95% CI) f Stage 4 COPD patients, n Prevalence, % (95% CI) f
Overall 5605 863 15.4% (14.5%–16.3%) 154 2.7% (2.3%–3.2%) 379 6.8% (6.1%–7.4%) 211 3.8% (3.3%–4.3%) 119 2.1% (1.7%–2.5%)
Age group (years)
 40–59 2923 339 11.6% (10.4%–12.8%)* 48 1.6% (1.2%–2.1%)* 146 5.0% (4.2%–5.8%)* 86 2.9% (2.3%–3.6%)* 59 2.0% (1.5%–2.5%)
 60–79 2596 496 19.1% (17.6%–20.6%) 98 3.8% (3.0%–4.5%) 224 8.6% (7.5%–9.7%) 118 4.5% (3.7%–5.3%) 56 2.2% (1.6%–2.7%)
 ≥80 86 28 32.6% (22.7%–42.5%) 8 9.3% (3.2%–15.4%) 9 10.5% (4.0%–16.9%) 7 8.1% (2.4%–13.9%) 4 4.7% (0.2%–9.1%)
Gender
 Man 2642 402 15.2% (13.8%–16.6%) 62 2.3% (1.8%–2.9%) 194 7.3% (6.3%–8.3%) 90 3.4% (2.7%–4.1%) 56 2.1% (1.6%–2.7%)
 Woman 2963 461 15.6% (14.3%–16.9%) 92 3.1% (2.5%–3.7%) 185 6.2% (5.4%–7.1%) 121 4.1% (3.4%–4.8%) 63 2.1% (1.6%–2.6%)
Living area
 Urban 2179 351 16.1% (14.6%–17.7%) 89 4.1% (3.3%–4.9%)* 172 7.9% (6.8%–9.0%)* 56 2.6% (1.9%–3.2%)* 34 1.6% (1.0%–2.1%)*
 Suburban 3426 512 14.9% (13.8%–16.1%) 65 1.9% (1.4%–2.4%) 207 6.0% (5.2%–6.8%) 155 4.5% (3.8%–5.2%) 85 2.5% (2.0%–3.0%)
Education
 ≤6 schooling years 2220 407 18.3% (16.7%–19.9%)* 70 3.2% (2.4%–3.9%) 155 7.0% (5.9%–8.0%) 113 5.1% (4.2%–6.0%)* 69 3.1% (2.4%–3.8%)*
 7–9 schooling years 1880 273 14.5% (12.9%–16.1%) 46 2.4% (1.7%–3.1%) 136 7.2% (6.1%–8.4%) 59 3.1% (2.4%–3.9%) 32 1.7% (1.1%–2.3%)
 ≥10 schooling years 1505 183 12.2% (10.5%–13.8%) 38 2.5% (1.7%–3.3%) 88 5.8% (4.7%–7.0%) 39 2.6% (1.8%–3.4%) 18 1.2% (0.6%–1.7%)
Marital status
 Married 5201 769 14.8% (13.8%–15.8%)* 135 2.6% (2.2%–3.0%)* 340 6.5% (5.9%–7.2%)* 187 3.6% (3.1%–4.1%)* 107 2.1% (1.7%–2.4%)
 No spouse 404 94 23.3% (19.1%–27.4%) 19 4.7% (2.6%–6.8%) 39 9.7% (6.8%–12.5%) 24 5.9% (3.6%–8.2%) 12 3.0% (1.3%–4.6%)
Annual income level (tertiled)
 Lower 1805 333 18.4% (16.7%–20.2%)* 57 3.2% (2.4%–4.0%)* 147 8.1% (6.9%–9.4%)* 84 4.7% (3.7%–5.6%)* 45 2.5% (1.8%–3.2%)*
 Middle 1886 293 15.5% (13.9%–17.2%) 58 3.1% (2.3%–3.9%) 118 6.3% (5.2%–7.3%) 71 3.8% (2.9%–4.6%) 46 2.4% (1.7%–3.1%)
 Upper 1914 237 12.4% (10.9%–13.9%) 39 2.0% (1.4%–2.7%) 114 6.0% (4.9%–7.0%) 56 2.9% (2.2%–3.7%) 28 1.5% (0.9%–2.0%)
Chronic respiratory symptoms
 Yes 1052 181 17.2% (14.9%–19.5%) 30 2.9% (1.8%–3.9%) 84 8.0% (6.3%–9.6%) 43 4.1% (2.9%–5.3%) 24 2.3% (1.4%–3.2%)
 No 4553 682 15.0% (13.9%–16.0%) 124 2.7% (2.3%–3.2%) 295 6.5% (5.8%–7.2%) 168 3.7% (3.1%–4.2%) 95 2.1% (1.7%–2.5%)
Physical activity
 Sufficient 4302 628 14.6% (13.5%–15.7%)* 113 2.6% (2.1%–3.1%) 282 6.6% (5.8%–7.3%) 153 3.6% (3.0%–4.1%) 80 1.9% (1.5%–2.3%)*
 Insufficient 1303 235 18.0% (15.9%–20.1%) 41 3.1% (2.2%–4.1%) 97 7.4% (6.0%–8.9%) 58 4.5% (3.3%–5.6%) 39 3.0% (2.1%–3.9%)
Family history of COPD
 Yes 166 29 17.5% (11.7%–23.2%) 3 1.8% (0.6%–5.2%) 15 9.0% (4.7%–13.4%) 7 4.2% (1.2%–7.3%) 4 2.4% (0.1%–4.7%)
 No 5439 834 15.3% (14.4%–16.3%) 151 2.8% (2.3%–3.2%) 364 6.7% (6.0%–7.4%) 204 3.8% (3.2%–4.3%) 115 2.1% (1.7%–2.5%)
Smoking history
 Never-smokers 4038 626 15.5% (14.4%–16.6%) 121 3.0% (2.5%–3.5%) 264 6.5% (5.8%–7.3%) 157 3.9% (3.3%–4.5%) 84 2.1% (1.6%–2.5%)
 Current smokers 1265 181 14.3% (12.4%–16.2%) 26 2.1% (1.3%–2.8%) 86 6.8% (5.4%–8.2%) 41 3.2% (2.3%–4.2%) 28 2.2% (1.4%–3.0%)
 Former smokers 302 56 18.5% (14.2%–22.9%) 7 2.3% (0.6%–4.0%) 29 9.6% (6.3%–12.9%) 13 4.3% (2.0%–6.6%) 7 2.3% (0.6%–4.0%)
Passive smoking
 Yes 1977 292 14.8% (13.2%–16.3%) 43 2.2% (1.5%–2.8%) 127 6.4% (5.3%–7.5%) 79 4.0% (3.1%–4.9%) 43 2.2% (1.5%–2.8%)
 No 3628 571 15.7% (14.6%–16.9%) 111 3.1% (2.5%–3.6%) 252 6.9% (6.1%–7.8%) 132 3.6% (3.0%–4.2%) 76 2.1% (1.6%–2.6%)
BMI group (kg/m²)
 <18.5 70 14 20.0% (10.6%–29.4%)* 3 4.3% (1.5%–11.9%) 3 4.3% (1.5%–11.9%)* 4 5.7% (0.3%–11.2%) 4 5.7% (0.3%–11.2%)
 18.5–23.9 2033 345 17.0% (15.3%–18.6%) 61 3.0% (2.3%–3.7%) 159 7.8% (6.7%–9.0%) 80 3.9% (3.1%–4.8%) 45 2.2% (1.6%–2.9%)
 ≥24 3502 504 14.4% (13.2%–15.6%) 90 2.6% (2.0%–3.1%) 217 6.2% (5.4%–7.0%) 127 3.6% (3.0%–4.2%) 70 2.0% (1.5%–2.5%)
History of exposure to polluted fuels
 Yes 183 40 21.9% (15.9%–27.8%)* 3 1.6% (0.6%–4.7%) 14 7.7% (3.8%–11.5%) 16 8.7% (4.7%–12.8%)* 7 3.8% (1.0%–6.6%)
 No 5422 823 15.2% (14.2%–16.1%) 151 2.8% (2.3%–3.2%) 365 6.7% (6.1%–7.4%) 195 3.6% (3.1%–4.1%) 112 2.1% (1.7%–2.4%)
History of occupational exposure
 Yes 944 142 15.0% (12.8%–17.3%) 28 3.0% (1.9%–4.0%) 65 6.9% (5.3%–8.5%) 35 3.7% (2.5%–4.9%) 14 1.5% (0.7%–2.3%)
 No 4661 721 15.5% (14.4%–16.5%) 126 2.7% (2.2%–3.2%) 314 6.7% (6.0%–7.5%) 176 3.8% (3.2%–4.3%) 105 2.3% (1.8%–2.7%)
Open in a new tab

COPD, chronic obstructive pulmonary disease; CI, confidence interval; BMI: body mass index.

*P < 0.05, chi-square test.

aCOPD included both already-diagnosed and newly-identified cases.

bStage 1 COPD: predicted FEV1% ≥80%.

cStage 2 COPD: 50% ≤ predicted FEV1% <80%.

dStage 3 COPD: 30% ≤ predicted FEV1% <50%.

eStage 4 COPD: predicted FEV1% <30%.

fPrevalence of COPD was defined as the proportion of determined COPD patients among overall participants.

Fig. 2. Prevalence, awareness, treatment and control rates of COPD by sex across age groups among participants in the study.

Fig. 2

Open in a new tab

Male: The rates of prevalence, awareness, treatment and control of COPD among men across age groups; Female: The rates of prevalence, awareness, treatment and control of COPD among women across age groups.

Awareness, treatment, and control rates among COPD patients

Table 3 displays the rates of awareness, treatment, and control among COPD patients. Of the total 863 COPD patients, 38 and 825 were already-known and newly-identified cases, respectively. Only 21 of 38 already-known COPD patients received clinical treatment. Among all COPD patients, the crude, and age- and sex-standardized rates of awareness, treatment, and control of COPD were 4.4% (95% CI = 3.0%, 5.8%), 2.4% (95% CI = 1.4%, 3.4%), and 56.8% (95% CI = 53.5%, 60.1%), separately, and 4.6% (95% CI = 3.2%, 6.0%), 2.4% (95% CI = 1.4%, 3.4%), and 56.5% (95% CI = 53.3%, 59.8%), respectively. Moreover, among patients with COPD, 1.6% (95% CI = 0.8%, 2.5%) of them used inhaled medications. Additionally, 30.6% (95% CI = 27.5%, 33.7%) of COPD cases reported ≥1 acute exacerbations, while 21.0% (18.3%, 23.7%) reported experiencing chronic respiratory symptoms.

Table 3.

Awareness, treatment and control of COPD among community-dwelling COPD patients aged 40+ years in Nanjing municipality, China.

Characteristics All COPD patients, N Awareness a Treatment b Control c
Patients with already-known COPD, n Rate, % (95% CI) d Patients receiving COPD treatment, n Rate, % (95% CI) d Patients with COPD under control, n Rate, % (95% CI) d
Overall 863 38 4.4% (3.0%–5.8%) 21 2.4% (1.4%–3.4%) 490 56.8% (53.5%–60.1%)
Age group (years)
 40–59 339 10 2.9% (1.1%–4.7%) 3 0.9% (0.2%–3.8%) 197 58.1% (52.9%–63.4%)
 60–79 496 26 5.2% (3.2%–7.2%) 17 3.4% (1.8%–5.0%) 279 56.3% (51.9%–60.6%)
 ≥80 28 2 7.1% (1.3%–31.4%) 1 3.6% (0.4%–26.8%) 14 50.0% (31.5%–68.5%)
Gender
 Man 402 26 6.5% (4.1%–8.9%)* 15 3.7% (1.9%–5.5%)* 234 58.2% (53.4%–63.0%)
 Woman 461 12 2.6% (1.1%–4.1%) 6 1.3% (0.3%–2.3%) 256 55.5% (51.0%–60.1%)
Living area
 Urban 351 21 6.0% (3.5%–8.5%) 10 2.8% (1.1%–4.5%) 245 69.8% (65.0%–74.6%)*
 Suburban 512 17 3.3% (1.8%–4.8%) 11 2.1% (0.9%–3.4%) 245 47.9% (43.5%–52.2%)
Education (schooling years)
 ≤6 407 11 2.7% (1.1%–4.3%)* 7 1.7% (0.4%–3.0%)* 196 48.2% (43.3%–53.0%)*
 7–9 273 14 5.1% (2.5%–7.7%) 5 1.8% (0.6%–5.7%) 172 63.0% (57.3%–68.7%)
 ≥10 183 13 7.1% (3.4%–10.8%) 9 4.9% (1.8%–8.0%) 122 66.7% (59.8%–73.5%)
Marital status
 Married 769 34 4.4% (3.0%–5.8%) 18 2.3% (1.2%–3.4%) 440 57.2% (53.7%–60.7%)
 No spouse 94 4 4.3% (1.2%–14.3%) 3 3.2% (0.7%–12.7%) 50 53.2% (43.1%–63.3%)
Annual income level (tertiled)
 Lower 333 12 3.6% (1.6%–5.6%) 7 2.1% (0.6%–3.6%) 181 54.4% (49.0%–59.7%)*
 Middle 293 11 3.8% (1.6%–6.0%) 7 2.4% (0.6%–4.2%) 158 53.9% (48.2%–59.6%)
 Upper 237 15 6.3% (3.2%–9.4%) 7 3.0% (0.8%–5.2%) 151 63.7% (57.6%–69.8%)
Chronic respiratory symptoms e
 Yes 181 26 14.4% (9.3%–19.5%)* 17 9.4% (5.1%–13.7%)* / /
 No 682 12 1.8% (0.8%–2.8%) 4 0.6% (0.2%–2.1%) / /
Physical activity
 Sufficient 628 28 4.5% (2.9%–6.1%) 12 1.9% (0.8%–3.0%) / /
 In-sufficient 235 10 4.3% (1.7%–6.9%) 9 3.8% (1.4%–6.2%) / /
Family history of COPD
 Yes 29 9 31.0% (14.2%–47.9%)* 7 24.1% (8.6%–39.7%)* 14 48.3% (30.1%–66.5%)
 No 834 29 3.5% (2.2%–4.7%) 14 1.7% (0.8%–2.6%) 476 57.1% (53.7%–60.4%)
Smoking history
 Never-smokers 626 22 3.5% (2.1%–4.9%)* 12 1.9% (0.8%–3.0%)* 351 56.1% (52.2%–60.0%)
 Current smokers 181 6 3.3% (0.7%–5.9%) 3 1.7% (0.4%–6.9%) 109 60.2% (53.1%–67.4%)
 Former smokers 56 10 17.9% (7.9%–27.9%) 6 10.7% (2.6%–18.8%) 30 53.6% (40.5%–66.6%)
Passive smoking
 Yes 292 7 2.4% (0.6%–4.2%) 4 1.4% (0.4%–4.9%) 162 55.5% (49.8%–61.2%)
 No 571 31 5.4% (3.5%–7.3%) 17 3.0% (1.6%–4.4%) 328 57.4% (53.4%–61.5%)
BMI group (kg/m²)
 < 18.5 14 0 0.0% (0.0%–0.0%) 0 0.0% (0.0%–0.0%) 8 57.1% (31.2%–83.1%)
 18.5–23.9 345 17 4.9% (2.6%–7.2%) 8 2.3% (0.7%–3.9%) 194 56.2% (51.0%–61.5%)
 ≥24 504 21 4.2% (2.4%–6.0%) 13 2.6% (1.2%–4.0%) 288 57.1% (52.8%–61.5%)
History of exposure to polluted fuels
 Yes 40 2 5.0% (0.9%–23.6%) 2 5.0% (0.9%–23.6%) 20 50.0% (34.5%–65.5%)
 No 823 36 4.4% (3.0%–5.8%) 19 2.3% (1.3%–3.3%) 470 57.1% (53.7%–60.5%)
History of occupational exposure
 Yes 142 6 4.2% (0.9%–7.5%) 4 2.8% (0.8%–9.7%) 86 60.6% (52.5%–68.6%)
 No 721 32 4.4% (2.9%–5.9%) 17 2.4% (1.3%–3.5%) 404 56.0% (52.4%–59.7%)
Open in a new tab

COPD, chronic obstructive pulmonary disease; CI, confidence interval; BMI, body mass index.

*P < 0.05, chi-square.

aAwareness of COPD: a patient self-reported he/she has already-diagnosed with COPD by physicians.

bTreatment of COPD: a COPD patient receiving clinical treatment (including medication or oxygen therapy) in the past year.

cControl of COPD: a COPD patient had a RADAR score of 0–1.

dRate was defined as the proportion of already-known COPD patients, the patients receiving clinical treatment (including medication or oxygen therapy) in the past year and those patients with a RADAR score of 0–1, respectively, among all determined COPD patients.

Awareness rate was higher among men than women (p < 0.05). However, there was no difference in awareness rate of COPD by age (p = 0.22) or residence area (p = 0.09). Besides, awareness rate was higher among COPD patients with higher educational level, chronic respiratory symptoms, family history of COPD, or smoking formerly relative to the corresponding counterparts, separately (p < 0.05).

Treatment rate of COPD was higher in male patients than in female patients (p < 0.05). Moreover, COPD treatment rate differed significantly in participants by educational attainment, chronic respiratory symptoms, family history of COPD, or smoking history (p < 0.05). However, there was no significant difference in COPD treatment rate between urban and suburban patients, or among different age groups.

Control rate was higher among patients in urban areas relative to those in suburban settings. Additionally, higher control rate was observed among patients with higher educational level, or upper level of income (p < 0.05). However, control rate did not differ between COPD patients by age or sex.

Associated factors of COPD prevalence, awareness, treatment and control

Table 4 demonstrates the potential influencing factors of COPD prevalence, awareness, treatment and control. Relative to the corresponding counterparts, participants who were older, without spouse/partner, with lower education level, or physically inactive were at higher risk to experience COPD, whereas the prevalence of COPD decreased significantly with higher BMI levels. Among never-smokers, the potential COPD-associated factors were also age, educational attainment, marital status, and PA (Table S2). Among COPD patients, those with higher educational level, chronic respiratory symptoms, a family history of COPD, or smoking formerly were at higher odds to be aware of, and to receive treatment for COPD. Besides, patients who were 60–79 years old, with physically inactive were more likely to receive COPD treatment. And, patients in urban areas were at higher odds to have the disease under control.

Table 4.

Influencing factors for COPD prevalence, awareness, treatment and control among community-dwelling residents/patients aged 40 or above in Nanjing municipality, China.

Variables Prevalence Awareness Treatment Control
% (n/N) OR (95% CI) # % (n/N) OR (95% CI) # % (n/N) OR (95% CI) # % (n/N) OR (95% CI) †
Age group (years)
 40–59 11.6% (339/2,923) 1 2.9% (10/339) 1 0.9% (3/339) 1 58.1% (197/339) 1
 60–79 19.1% (496/2,596) 1.57 (1.31, 1.89) * 5.2% (26/496) 2.02 (0.80, 5.43) 3.4% (17/496) 8.87 (1.91, 59.69) * 56.3% (279/496) 0.75 (0.53, 1.06)
 ≥80 32.6% (28/86) 2.70 (1.63, 4.37) * 7.1% (2/28) 2.19 (0.22, 14.06) 3.6% (1/28) 4.90 (0.11, 108.47) 50.0% (14/28) 0.51 (0.22, 1.20)
Living area
 Urban 16.1% (351/2,179) 1 6.0% (21/351) 1 2.8% (10/351) 1 69.8% (245/351) 1
 Suburban 14.9% (512/3,426) 0.87 (0.73, 1.04) 3.3% (17/512) 1.27 (0.51, 3.19) 2.1% (11/512) 3.12 (0.82, 13.33) 47.9% (245/512) 0.39 (0.27, 0.55) *
Educational attainment (schooling years)
 ≤6 18.3% (407/2,220) 1 2.7% (11/407) 1 1.7% (7/407) 1 48.2% (196/407) 1
 7–9 14.5% (273/1,880) 0.87 (0.72, 1.04) 5.1% (14/273) 2.89 (1.01, 8.59) * 1.8% (5/273) 3.67 (0.72, 19.37) 63.0% (172/273) 1.42 (0.99, 2.04)
 ≥10 12.2% (183/1,505) 0.66 (0.53, 0.82) * 7.1% (13/183) 3.25 (1.09, 9.98) * 4.9% (9/183) 8.88 (1.94, 45.52) * 66.7% (122/183) 1.40 (0.91, 2.15)
Marital status
 Unmarried 23.3% (94/404) 1 4.3% (4/94) 1 3.2% (3/94) 1 53.2% (50/94) 1
 Married 14.8% (769/5,201) 0.69 (0.54, 0.90) * 4.4% (34/769) 0.87 (0.29,3.26) 2.3% (18/769) 0.49 (0.12, 2.73) 57.2% (440/769) 1.09 (0.68, 1.74)
Chronic respiratory symptoms
 No 15.0% (682/4,553) 1 1.8% (12/682) 1 0.6% (4/682) 1 / /
 Yes 17.2% (181/1,052) 1.13 (0.94, 1.36) 14.4% (26/181) 10.68 (4.91, 24.67) * 9.4% (17/181) 24.09 (6.98, 109.27) * / /
Physical activity
 Insufficient 18.0% (235/1,303) 1 4.3% (10/235) 1 3.8% (9/235) 1 / /
 Sufficient 14.6% (628/4,302) 0.77 (0.65, 0.91) * 4.5% (28/628) 0.76 (0.32, 1.89) 1.9% (12/628) 0.21 (0.06, 0.71) * / /
Family history of COPD
 No 15.3% (834/5,439) 1 3.5% (29/834) 1 1.7% (14/834) 1 57.1% (476/834) 1
 Yes 17.5% (29/166) 1.12 (0.73, 1.68) 31.0% (9/29) 6.50 (2.05, 20.05) * 24.1% (7/29) 13.40 (3.21, 59.46) * 48.3% (14/29) 0.49 (0.22, 1.07)
Smoking history
 Never-smokers 15.5% (626/4,038) 1 3.5% (22/626) 1 1.9% (12/626) 1 56.1% (351/626) 1
 Current smokers 14.3% (181/1,265) 0.93 (0.73, 1.17) 3.3% (6/181) 0.65 (0.19, 2.05) 1.7% (3/181) 0.31 (0.05, 1.66) 60.2% (109/181) 1.28 (0.81, 2.02)
 Former smokers 18.5% (56/302) 1.19 (0.85, 1.66) 17.9% (10/56) 4.39 (1.43, 13.54) * 10.7% (6/56) 5.58 (1.16, 27.99) * 53.6% (30/56) 0.91 (0.49, 1.73)
BMI group (kg/m²)
 18.5–23.9 17.0% (345/2,033) 1 4.9% (17/345) 1 2.3% (8/345) 1 56.2% (194/345) 1
 < 18.5 20.0% (14/70) 1.25 (0.66, 2.23) 0.0% (0/14) 0.00 (0.00, ∞) a 0.0% (0/14) 0.00 (0.00, ∞) a 57.1% (8/14) 0.91 (0.30, 2.94)
 ≥24 14.4% (504/3,502) 0.83 (0.71, 0.96) * 4.2% (21/504) 1.04 (0.49, 2.28) 2.6% (13/504) 1.74 (0.57, 5.75) 57.1% (288/504) 1.12 (0.84, 1.50)
Open in a new tab

COPD, chronic obstructive pulmonary disease; OR, odds ratio; CI, confidence interval; BMI, body mass index.

*P < 0.05

#ORs and CIs were estimated based on a multivariate logistic regression model with adjustment for age group, sex, living area, education, marital status, annual income level, chronic respiratory symptoms, physical activity, family history of COPD, smoking history, passive smoking, BMI group, history of exposure to polluted fuels and history of occupational exposure.

†ORs and CIs were estimated based on a multivariate logistic regression model with adjustment for age group, sex, living area, education, marital status, annual income level, family history of COPD, smoking history, passive smoking, BMI group, history of exposure to polluted fuels and history of occupational exposure.

aThe sample size is too small to estimate the effect.

Discussion

For this population-based study conducted in 2023, its aims were to investigate the prevalence, awareness, treatment and control of COPD, and to examine the influencing factors of them, separately, within community-dwelling adults aged 40 years or above in regional China. The prevalence of overall newly-identified and already-known COPD was 15.4% among adults in Nanjing municipality of China. Moreover, persistent gaps in awareness, treatment and control rates were observed among the determined COPD patients. Age, marital status, educational attainment, PA, and body weight status were in relation to COPD prevalence. Furthermore, chronic respiratory symptoms, educational level, family history of COPD, and smoking formerly were significantly associated with awareness of COPD. Age of 60–79 years, chronic respiratory symptoms, educational attainment, family history of COPD, physical inactivity, and smoking formerly were in a positive relation to treatment, whereas living in urban areas was in a positive link to the disease control.

Among adults aged 40 years or above, the recent spirometry-based COPD prevalence was 12.6% worldwide in 2016–202228, and 13.6% in China in 201511. In this study, the overall prevalence of COPD (15.4%) was higher than the global and national estimates in China. Meanwhile, it also significantly exceeded the prevalence among residents with the same-age in the local province of Jiangsu (11.3%)14, Beijing (8.5%)29, Heilongjiang (9.8%)30, Fujian (11.6%)31, or Tibet province (6.2%)32 in China. These differences may reflect variations in data collection time, regional economic development level, climate, and/or air quality.

As for the severity of COPD, the proportion of stage 2+ cases was 82.2%, which was much higher than those reported in prior comparable studies33,34. This finding suggested that the vast majority of patients had been already experiencing at least moderate airflow limitation at the time of detection. It highlights the importance of early-detection of patients for the purpose of efficiently population-level intervention of COPD in China.

Despite high COPD prevalence in Nanjing, the awareness, treatment, and control rates were sub-optimal. Notably, the awareness rate (4.4%) in this study was much higher than the national level (0.9%) in 2015 in China7. This might be likely due to that: 1) the “Healthy China Initiative (2019–2030)” action was launched to promote annual lung function tests for adults ≥40 years in China since 201935, and 2) Nanjing had more sufficient healthcare resources and its residents had a relatively higher health literacy level in China36,37. Nevertheless, the awareness rate of COPD still lagged behind some European countries (e.g., 12% in Sweden)38.

The treatment rate in this study (2.4%) was lower than the national level (11.7%) assessed in 2015 in China39. Such a difference was not easy to explain, as characteristics of COPD patients and awareness rate were different in our study and the national survey7,39. One possible explanation might be that there were more COPD patients with greater severity in the national survey than our study, as more patients experienced respiratory symptoms in the national survey compared to our study (33.5% vs. 21.0%, p < 0.01)39.

As for COPD control rate, to our knowledge, no population-based study has reported comparable estimates among community-dwelling adults in China, which limits direct comparisons. However, it is still of interest to take a look at a study on control rate among COPD out-patients in Xishui County of Guizhou province in central China40. Notably, participants were COPD outpatients in the Guizhou study, while community-dwelling adults within the same age-group were analyzed in our investigation. Moreover, FEV1 was used to assess control of COPD in Guizhou study40, while RADAR score, a comprehensive indicator recently-recommended in GOLD-2026 Report, was employed in our survey40. Among the COPD out-patients analyzed in Guizhou study, only 45.0% had the disease under control after treatment40. In contrast, 56.8% of COPD patients in our community-based sample met the criteria for control according to the RADAR-based definition. This apparent difference highlights the strong influence of definitions of control, and study settings on estimated control rates. Importantly, although more than half of patients were classified as “controlled” at the time of survey, a substantial proportion still reported clinically relevant disease activity, with approximately one-third experiencing at least one acute exacerbation in the past three months and 21.0% reporting chronic respiratory symptoms. Moreover, previous longitudinal studies have shown that persistent lack of disease control is associated with higher mortality and poorer quality of life in patients with COPD41. Therefore, adherence to use of appropriate treatment approaches and dynamic assessment of COPD control is encouraged.

Regarding potential factors associated with COPD prevalence, older, single, less-educated, or physical inactive participants were at higher risks of COPD, while excess body weight was negatively associated with the risk of COPD. This was in line with that older age, being single, lower level of education, and insufficient PA were independent risk factors for COPD42–44. Previous researches have shown that excess body weight was in a negative relation to COPD10,45,46, which was in line with findings in our study. However, the “obesity paradox” may exist for the relationship between excess body weight and COPD due to the potential bi-directional obesity-COPD association. It is also likely that individuals without COPD tended to have a higher BMI, which might reflect a body composition with proportionally greater muscle mass47. However, in this study, the COPD prevalence was similar in men and women, which might be due to the high proportion of older women, cumulative exposure of women to non-smoking risk factors, and improved diagnosis among women48–51. Notably, the majority of participants (72%) were never-smokers in this study, which was different from previous research centered on smokers52. After stratified analysis, participant’s age, educational attainment, marital status, and PA level were found to be associated with COPD among never-smokers in this study. This highlights that the non-smoking factors shall also be considered in COPD prevention in addition to smoking.

Chronic respiratory symptoms were risk factors of COPD awareness, and treatment in our study, which was consistent with reports in existing studies7,53. This might be likely because COPD has an insidious onset, and most patients did not seek medical attention until obvious respiratory symptoms appeared54. Smoking formerly was a positive factor for COPD awareness and treatment in this study, which was possibly due to smoking-related airway symptoms prompting earlier diagnosis and treatment54. Higher level of education could also improve awareness or treatment, which might be applauded by the positive link between education and health literacy53. Individuals with a family history of COPD are often more aware of the condition and more alert to the emergence of its characteristic symptoms55. Patients living in urban areas were more likely to achieve disease control, potentially due to better healthcare access, and higher socioeconomic status for urban residents than their suburban counterparts56. Interestingly, treated cases were more common in COPD patients aged 60–79 years or with family history, which might be owing to that these patients had greater health consciousness, clinician engagement, and compliance with prescribed regimens57. Surprisingly, COPD patients with sufficient PA were less likely to seek treatment in this study. This might be explained by that most of them were undiagnosed, and therefore had no idea to seek medical treatment for improving/maintaining respiratory function58–60. The factors associated with awareness, treatment and control were examined not in strictly consistent patterns. This might be due to the relatively small sample size of participants and social, cultural, or/and economic factors. It is likely that some significant factors may only appear in specific sub-populations or under certain contexts, highlighting the complexity of COPD management and the need for further research in large sample-size populations.

This study had several strengths. First of all, this is the first study designed specially to investigate the prevalence, awareness, treatment and control of COPD among community-based participants randomly selected from a typical mega-city in China. Second, COPD was defined based on objective lung function test or self-reported hospital-based physician diagnosis; both already-known and newly-identified cases were therefore captured. Third, the rates of treatment and control were investigated among not only overall COPD patients but also already-diagnosed COPD patients, showing a gap between disease identification and effective long-term management. Fourth, influencing factors of awareness, treatment and control were separately examined, which could inform tailored intervention programs.

Limitations should also be worthy of mention. First, as a cross-sectional study, it could not delineate causal links of the identified factors with awareness, treatment, and control. Second, the awareness and treatment status, information on socio-demographic characteristics, lifestyle and behaviors, and family history were self-reported by participants, which might introduce recall bias. Third, portable spirometers were used to assess participants’ lung function, which might imply potential bias on estimating the prevalence of COPD, although it was widely used to measure lung function for participants in large scale population studies. Fourth, only information on routes of drug administration (inhaled, oral, or intravenous) was gathered, whereas specific types of medications for COPD treatment (e.g., bronchodilators, inhaled corticosteroid) were not collected in the study. Therefore, no specific medications could be considered in analysis regarding COPD treatment, which might affect the interpretation of treatment patterns and clinical correlations. Fifth, two single questions, “used inhaled medications to improve symptoms” and “received treatment due to worsening respiratory symptoms” were integrated to create a proxy of “inhaled rescue medication use” for calculating RADAR score in this study. It should be noted that such a proxy might not fully correspond to the original item in RADAR score estimation, and might imply some misclassifications. In future community-based COPD study, data on the exact items shall be gathered for estimating RADAR score. Finally, although the overall sample size was as large as >5600, the number of patients who received treatment was very small, which might not warrant a sufficient statistical power for examining associations of treatment and control rates with potential factors. Therefore, it shall be prudent to interpret the findings on treatment-/control-associated factors in this study.

It is not easy to conduct a population-based study to assess COPD prevalence, awareness, treatment and control based on spirometry diagnosis among community-dwelling adults, as it is substantially costly in addition to difficulties in participant’s recruitment and organization of field survey. However, up-to-date information on COPD prevalence, awareness, treatment and control is vital for dynamically precision intervention of COPD, allocation of healthcare resources, and personalized heath education. Therefore, in future, it is of great value to implement periodical assessment of prevalence, awareness, treatment and control rates of COPD, using well-designed studies with sufficiently large sample size among representative population, for the purpose of population-level COPD prevention and control. This shall be strongly encouraged by administrations, financial supporters, individuals, academic research communities, and other stakeholders.

In conclusion, despite the high prevalence of COPD, awareness and treatment rates remain low, and a substantial gap in disease control persists among community-dwelling adults aged 40 years or above in regional China. These findings highlight both public health and clinical implications for community-based COPD prevention and control. First, it is of population-level significance to prioritize accessible and effective COPD education, and lung function screening programs, particularly through primary healthcare and community-based settings, with a focus on older adults and other high-risk populations. Next, it is also of public health importance to improve awareness of COPD symptoms among the general population and frontline healthcare providers, which may facilitate earlier detection of COPD, especially among never-smokers. In addition, it is of clinical meaningfulness to enhance the availability and appropriate use of guideline-based treatment approaches in primary care, together with lifestyle-oriented interventions such as physical activity promotion and reduction of harmful environmental exposures, which may help improve treatment and control rates of COPD. Only in this way, individuals with COPD can be identified early, then receive effective treatment and have the disease under control, which can consequently reduce the population-level burden of COPD in China.

Ethics approval

All participants provided informed consent before the survey. Data were de-identified prior to analysis. The study was approved by the Ethics Committee of the Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention (PJ2020-B001-01). Regarding experimental methods, this study followed the principles documented in the Declaration of Helsinki.

Supplementary information

Table S1-2 (40.2KB, docx)
Appendix 1 (13.9KB, docx)

Acknowledgements

Our special thanks go to all workers in community health service centers involved in this study for their kind support and assistance in data collection. This work was supported by Nanjing Medical Science and Technology Development Fund (ZKX24059; Recipient: Fei Xu).

Author contributions

JK, JX and FX conceived, designed and supervised the study. QY and FX were responsible for data acquisition. HX and FX analyzed the data. JK, HX, QY, ZW, CH, YX, GA, XD, YB, JX and FX wrote and critically reviewed the manuscript. Each author approved the final version for submission and was also responsible for all aspects of the work presented in this manuscript.

Data Availability

Data would be available upon request to Dr. Fei Xu.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Role of the funder/sponsor

The funder had no role in this work and the decision of submission.

Patient and Public Involvement

It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.

These authors contributed equally: Jian Kang, Huiqing Xu.

Contributor Information

Xiaojing Deng, Email: deng-xiaojing@163.com.

Fei Xu, Email: xufei@njmu.edu.cn.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41533-026-00508-3.

References

  • 1.Labaki, W. W. & Rosenberg, S. R. Chronic obstructive pulmonary disease. Ann Intern Med173, ITC17–ITC32 (2020). [DOI] [PubMed] [Google Scholar]
  • 2.van Durme, Y. et al. Prevalence, incidence, and lifetime risk for the development of COPD in the elderly: the Rotterdam study. Chest135, 368–377 (2009). [DOI] [PubMed] [Google Scholar]
  • 3.Alupo, P. et al. Incidence and predictors of COPD mortality in Uganda: A 2-year prospective cohort study. PLoS ONE16, e0246850 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Zhu, B., Wang, Y., Ming, J., Chen, W. & Zhang, L. Disease burden of COPD in China: a systematic review. Int J Chron Obstruct Pulmon Dis. 13, 1353–1364 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Hu, W., Fang, L., Zhang, H., Ni, R. & Pan, G. Global disease burden of COPD from 1990 to 2019 and prediction of future disease burden trend in China. Public Health208, 89–97 (2022). [DOI] [PubMed] [Google Scholar]
  • 6.Rossaki, F. M. et al. Strategies for the prevention, diagnosis and treatment of COPD in low- and middle- income countries: the importance of primary care. Expert Rev Respir Med15, 1563–1577 (2021). [DOI] [PubMed] [Google Scholar]
  • 7.Cong, S. et al. Analysis on awareness of chronic obstructive pulmonary disease (COPD) status and related knowledge in patients with COPD in China, 2014-2015. Chin J Epidemiol41, 1034–1040 (2020). [DOI] [PubMed] [Google Scholar]
  • 8.Lin, C. H. et al. Current progress of COPD early detection: key points and novel strategies. Int J Chron Obstruct Pulmon Dis18, 1511–1524 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zhong, N. et al. Prevalence of chronic obstructive pulmonary disease in China: a large, population-based survey. Am J Respir Crit Care Med176, 753–760 (2007). [DOI] [PubMed] [Google Scholar]
  • 10.Wang, C. et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet (London, England)391, 1706–1717 (2018). [DOI] [PubMed] [Google Scholar]
  • 11.Fang, L. et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med6, 421–430 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Quan, Z. et al. Current status and preventive strategies of chronic obstructive pulmonary disease in China: a literature review. J Thorac Dis13, 3865–3877 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Nanjing Municipal Bureau of Statistics. Nanjing Statistical Yearbook. https://tjj.nanjing.gov.cn/material/njnj_2021/renkou/3-1.htm. 2021. Accessed 17 November.
  • 14.Xiang, W. Q. et al. Disease burden and smoking-attributable burden of chronic obstructive pulmonary disease among adults aged 40 years and older in Jiangsu province in 2015 and 2019. Chin J Prev Control Chronic Dis. 30, 81-86+91 (2022). (in Chinese). [Google Scholar]
  • 15.Wang, L. M. et al. Scheme of the Chinese chronic non-communicable disease and risk factor surveillance. Chin J Prev Med52, 191–194 (2018). [DOI] [PubMed] [Google Scholar]
  • 16.Agusti, A. et al. Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary. Eur Respir J61, 2300239 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Chronic Obstructive Pulmonary Disease Group of Chinese Thoracic Society, Chronic Obstructive Pulmonary Disease Committee of Chinese Association of Chest Physician Guidelines for the diagnosis and management of chronic obstructive pulmonary disease (2021). Chin J Tubeculosis Respir Dis44, 170–205 (2021). [DOI] [PubMed] [Google Scholar]
  • 18.Global initiative for chronic obstructive lung disease. Global strategy for prevention, diagnosis and management of COPD: 2026 Report. https://goldcopd.org/2026-gold-report-and-pocket-guide/. 2025. Accessed 9 December.
  • 19.Soler-Cataluna J. J., et al Risk validation of a new quantitative score for clinical control of chronic obstructive pulmonary disease: The RADAR score. Arch Bronconeumol. 2025 (in press). 10.1016/j.arbres.2025.06.003 [DOI] [PubMed]
  • 20.Jiang, X. et al. Prevalence of chronic respiratory symptoms and dyspnea and related factors in residents in China. Chin J Epidemiol43, 315–323 (2022). [DOI] [PubMed] [Google Scholar]
  • 21.World Health Organization. Global recommendations on physical activity for health. World Health Organization. https://www.who.int/dietphysicalactivity/factsheet_recommendations/en/. 2010. Accessed 3 April.
  • 22.Wang, N. et al. Survey of smoking prevalence in adults aged 40 years and older in China, 2014. Chin J Epidemiol39, 551–556 (2018). [DOI] [PubMed] [Google Scholar]
  • 23.Hoe, C., Cohen, J. E., Yang, T., Peng, S. & Zhang, W. Association of cigarette production and tobacco retailer density on secondhand smoke exposure in urban China. Tob Control. 31, e118–e125 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gordon, S. B. et al. Respiratory risks from household air pollution in low and middle income countries. Lancet Respir Med2, 823–860 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Lam, K. B. et al. Past dust and GAS/FUME exposure and COPD in Chinese: the Guangzhou Biobank Cohort Study. Respir Med106, 1421–1428 (2012). [DOI] [PubMed] [Google Scholar]
  • 26.Division of Disease Control MoHotpsroC The guideline for prevention and control of overweight and obesity in Chinese adults. People’s Medial Publishing House: Beijing, 2006). (in Chinese). [Google Scholar]
  • 27.Jiangsu Provincial Bureau of Statistics. Jiangsu population census yearbook 2020. https://tj.jiangsu.gov.cn/2020pcnj/zk/indexch.htm. 2020. Accessed 3 April.
  • 28.Al Wachami, N. et al. Estimating the global prevalence of chronic obstructive pulmonary disease (COPD): a systematic review and meta-analysis. BMC Public Health24, 297 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zhou, W. et al. Prevalence and risk factors of chronic obstructive pulmonary disease among community residents in Beijing. Chin J Med55, 500–503 (2020). [Google Scholar]
  • 30.Jin, L., Yan, S. C., Xu, L. L. & Zhou, Y. Prevalence of chronic obstructive pulmonary disease of residents aged 40 years and above in Heilongjiang Province and analysis of the risk factors of COPD. Chin J Public Health Manag39, 905–908 (2023). [Google Scholar]
  • 31.Chen, J. et al. Chronic obstructive pulmonary disease prevalence and associated risk factors in adults aged 40 years and older in Southeast China: A cross-sectional study during 2019-2020. Int J Chron Obstruct Pulmon Dis17, 2317–2328 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Gamacangjue, Li, Y. J., Bai, G. X., Suolangquzhen & Pingcuozhuoma Prevalence of chronic obstructive pulmonary disease among residents aged 40 years and above in Tibet in 2015: a cross-sectional survey. Chin J Public Health. 35, 104–106 (2019). (in Chinese). [Google Scholar]
  • 33.Zaniku, H. R. et al. Prevalence and associated factors of chronic obstructive pulmonary disease among adults in neno district, malawi: a cross-sectional analytical study. Int J Chron Obstruct Pulmon Dis19, 389–401 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Tang, Y. et al. Associations between different body mass index and lung function impairment in Chinese people aged over 40 years: a multicenter cross-sectional study. BMC Pulm Med24, 30 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Fang, L. W., Wang, L. H. & Wu, J. Surveillance and evaluation of diagnosis, treatment and management of patients with chronic obstructive pulmonary disease. Chin J Epidemiol41, 1009–1013 (2020). [DOI] [PubMed] [Google Scholar]
  • 36.Hu, H. B. et al. Results of health literacy investigation among residents in Nanjing City in 2016. Occup Health33, 3070-3073+3077 (2017). [Google Scholar]
  • 37.Zhou, J., Hu, L. J. & Huai, Q. Y. Evaluation of medical level in China by provinces based on principal component analysis and TOPSIS model. Chin J Gen Pract26, 4254-4260+4268 (2023). [Google Scholar]
  • 38.Danielsson, P., Olafsdottir, I. S., Benediktsdottir, B., Gislason, T. & Janson, C. The prevalence of chronic obstructive pulmonary disease in Uppsala, Sweden–the Burden of Obstructive Lung Disease (BOLD) study: cross-sectional population-based study. Clin Respir J6, 120–127 (2012). [DOI] [PubMed] [Google Scholar]
  • 39.Zhang, Y. et al. Analysis in medication treatment and its related factors among patients with chronic obstructive pulmonary disease aged 40 years or older in China, 2014-2015. Chin J Epidemiol41, 678–684 (2020). [DOI] [PubMed] [Google Scholar]
  • 40.Qian, X. B., Lv, X. Q., Wang, Y. & Fang, Z. Investigation of disease control and treatment patterns in 40 COPD outpatients. Mod Diagn Treat28, 1519–1520 (2017). [Google Scholar]
  • 41.Calle Rubio, M. et al. COPD Clinical Control: predictors and long-term follow-up of the CHAIN cohort. Respir Res22, 36 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Workman, B. & Nabors, L. Risk factors for adults with chronic obstructive pulmonary disease in the United States, utilizing state-based surveillance. COPD21, 2413712 (2024). [DOI] [PubMed] [Google Scholar]
  • 43.Kim, C. Y. et al. Longitudinal evaluation of the relationship between low socioeconomic status and incidence of chronic obstructive pulmonary disease: Korean Genome and Epidemiology Study (KoGES). Int J Chron Obstruct Pulmon Dis15, 3447–3454 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Caballero, A. et al. Prevalence of COPD in five Colombian cities situated at low, medium, and high altitude (PREPOCOL study). Chest133, 343–349 (2008). [DOI] [PubMed] [Google Scholar]
  • 45.Zhang, X., Chen, H., Gu, K. & Jiang, X. Association of body mass index and abdominal obesity with the risk of airflow obstruction: national health and nutrition examination survey (NHANES) 2007-2012. COPD19, 99–108 (2022). [DOI] [PubMed] [Google Scholar]
  • 46.Smith, M. et al. Prevalence and correlates of airflow obstruction in approximately 317,000 never-smokers in China. Eur Respir J44, 66–77 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Iyer, A. S. & Dransfield, M. T. The “Obesity Paradox” in chronic obstructive pulmonary disease: can it be resolved?. Ann Am Thorac Soc15, 158–159 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Han, M. K. et al. Gender and chronic obstructive pulmonary disease: why it matters. Am J Respir Crit Care Med176, 1179–1184 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Ding, Y. et al. Prevalence and risk factors of chronic obstructive pulmonary diseases in a Hlai community in Hainan Island of China. Clin Respir J12, 126–133 (2018). [DOI] [PubMed] [Google Scholar]
  • 50.Czarnota, P., MacLeod, J. L., Gupta, N., Manichaikul, A. & Shim, Y. M. Sex differences in chronic obstructive pulmonary disease: implications for pathogenesis, diagnosis, and treatment. Int J Mol Sci26, 2747 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Yan, X. et al. Epidemiology and risk factors of chronic obstructive pulmonary disease in Suzhou: a population-based cross-sectional study. J Thorac Dis12, 5347–5356 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Guo, X., Luo, Q., Chen, D. & Lin, H. Risk and protective factors of COPD among smokers: a cross-sectional study using NHANES database. Int J Chron Obstruct Pulmon Dis20, 1027–1038 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Song, X. R. et al. Analysis of pulmonary function test and medication treatment of patients with chronic obstructive pulmonary disease aged 40 years or older in community of Guangdong Province. Chin J Respir Crit Care Med21, 309–315 (2022). (in Chinese). [Google Scholar]
  • 54.Melzer, A. C. et al. Respiratory and bronchitic symptoms predict intention to quit smoking among current smokers with, and at risk for, chronic obstructive pulmonary disease. Ann Am Thorac Soc13, 1490–1496 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Nihlen, U., Nyberg, P., Montnemery, P. & Lofdahl, C. G. Influence of family history and smoking habits on the incidence of self-reported physician’s diagnosis of COPD. Respir Med98, 263–270 (2004). [DOI] [PubMed] [Google Scholar]
  • 56.Eisner, M. D. et al. Socioeconomic status, race and COPD health outcomes. J Epidemiol Community Health65, 26–34 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Jin, J., Sklar, G. E., Min Sen Oh, V. & Chuen Li, S. Factors affecting therapeutic compliance: A review from the patient’s perspective. Ther Clin Risk Manag4, 269–286 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Vaes, A. W. et al. Prevalence and prognostic importance of exercise limitation and physical inactivity in COPD. Breathe (Sheff)20, 230179 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Oostrik, L. et al. Physical activity and symptom burden in COPD: the Canadian obstructive lung disease study. Chronic Obstr Pulm Dis10, 89–101 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Durheim, M. T. et al. Six-minute-walk distance and accelerometry predict outcomes in chronic obstructive pulmonary disease independent of Global Initiative for Chronic Obstructive Lung Disease 2011 Group. Ann Am Thorac Soc12, 349–356 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1-2 (40.2KB, docx)
Appendix 1 (13.9KB, docx)

Data Availability Statement

Data would be available upon request to Dr. Fei Xu.

Articles from NPJ Primary Care Respiratory Medicine are provided here courtesy of Primary Care Respiratory Society UK/Macmillan Publishers Limited

RESOURCES