Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide [1]. The number of affected individuals and deaths from COPD are expected to increase as the population ages [2]. In Russia, the largest neighboring country of Kazakhstan, the prevalence of symptomatic COPD in the adult population was estimated to be 15.3% [3]. An estimated 1.4 million individuals in Kazakhstan may be affected by COPD. This estimate is based on studies that have been conducted in other countries in the World Health Organization European Region [4] but not in Kazakhstan, because a study on the prevalence of COPD has not been conducted in the country yet. COPD is significantly underdiagnosed in Kazakhstan. In 2013, the rate of reported COPD cases was 315.9 per 100,000 or around 53,000 registered cases of COPD [5]. A better understanding of the epidemiology and social and other determinants of the disease is needed in order to recognize the true magnitude of the problem and develop effective treatments and prevention strategies.

Various criteria for COPD have been used in population-based studies. The most common criterion is airflow obstruction detected by spirometry testing, defined as a postbronchodilator ratio of forced expiratory volume in 1 second (FEV₁) to forced vital capacity (FVC) less than 0.7 or 70% [6]. This criterion is simple to implement and widely used in epidemiological surveys. However, COPD prevalence based on this could be slightly biased—overestimated in old subjects and underestimated in younger ones [7]. For this reason, the fifth percentile of the FEV₁/FVC ratio or the lower limit of normal distribution of the FEV₁/FVC ratio defined for specific age-gender group is also recommended for use in epidemiological studies [8].

Respiratory symptoms [9] and physical examination [10] were frequently used to evaluate COPD prevalence. However, collecting such data is more important for establishing the clinical diagnosis of COPD [6]. The specific symptoms of COPD include progressive dyspnea, cough, and sputum production [6]. The COPD Assessment Test (CAT) was specially designed to measure how COPD symptoms lead to health status impairment. The test is derived from 8 items, and the score varies from 0 to 40. The cut-off point for the CAT is 10, after which regular treatment for symptoms is recommended [6]. Although a physical examination is rarely used in COPD diagnosis, particularly in detecting mild to moderate COPD, thoracic examination of patients with severe disease can usually reveal the following signs: hyperinflation, wheezing, diffusely decreased breath sounds, hyperresonance on percussion, and prolonged expiration [11].

Chest computed tomography (CT) scan is recommended for subjects with airflow limitation and signs and symptoms suggestive of COPD for making an accurate diagnosis of COPD to exclude other conditions [12]. CT scan also helps to separate COPD patients into 2 main phenotypes, emphysema and small airway disease [13]. CT images can be visually assessed by qualified observers to describe patterns of altered lung structure or quantified for assessment of the extent of emphysema, gas trapping, and airway abnormality [14]. Some studies suggested that severity of emphysema detected by CT scan is associated with greater lung function decline even if airway obstruction is not currently presented [15,16]. As a result, CT-detected emphysema may predict future development of airflow obstruction [16,17].

Tobacco smoking, occupational and environmental exposures including workplace dusts and chemicals, and smoke from home cooking and heating fuels are the main risk factors for COPD [8]. In addition, advanced age, chronic respiratory infections such as tuberculosis, low socioeconomic status, and being underweight may influence COPD development [18,19]. Genetic predisposition also plays an important role in COPD development. Originally described more than 50 years ago, α₁-antitrypsin deficiency may cause COPD and accounts for 1% to 2% of all COPD cases. There are other genome variants currently being investigated as candidates for COPD genes, but only the Z variant of α₁-antitrypsin is accepted as a COPD gene at the present time [20].

Comorbidities are frequently found in persons with COPD. Some of them have risk factors, which are the same as for COPD, particularly tobacco smoking and aging. Moreover, systemic inflammation and chronic hypoxia present in COPD patients may cause other health-related conditions. Common comorbidities include heart disease, lung cancer, osteoporosis, metabolic syndrome and diabetes, anemia, anxiety, cognitive decline, and sleep disorders [21,22]. Specific comorbidities increase mortality and poor outcome in COPD, so management of main comorbidities has been included to COPD guidelines [6].

The aim of the study is to assess the prevalence of COPD among smokers, ex-smokers, and never-smokers aged 40 to 59 years based on pulmonary function assessment (spirometry), structural changes (emphysema and large and small airway inflammation with thickening) identified by high-resolution CT, COPD symptoms, and exercise limitations. In addition, the study objectives include comparing the prevalence of health conditions considered as COPD comorbidities (heart disease, hypertension, metabolic syndrome, diabetes mellitus) in 3 study groups and their interaction with COPD.

We have completed the participant recruitment and study procedures. Currently, we are working on data processing and data analysis. The authors anticipate the preliminary results should be available by September 2017. Study results will be published in peer-reviewed scientific journals.

To the best of our knowledge, this is the first study in Kazakhstan that assesses COPD prevalence in the general population aged 40 to 59 years, specifically smokers, ex-smokers, and never-smokers. The study also aims to investigate COPD comorbidities and their interaction with COPD. Improving COPD preventive measures, COPD screening, identification, and registration requires obtaining this epidemiological information. Study strengths include its relatively large sample size and collection of comprehensive medical, behavioral, and other health-related data from each study participant.

However, there remain some limitations. First, the study is observational. Thus, the possibility of unidentified or unmeasured confounders exists. We are going to conduct the sensitivity analysis to assess a covariate that could eliminate the effect of smoking on COPD. Second, the study is cross-sectional, and the time when COPD occurred cannot be identified. The values of potential confounders measured in the study could differ from ones when symptoms of COPD first appeared and could be diagnosed. As a result, causal inferences cannot be made. Third, the study includes many components to be measured, which makes it impossible to employ probability sampling to select participants. The main risk of nonprobability sampling is that the distribution of important covariates in the sample will differ significantly from their distribution in the study population. We employ quota sampling to set quotas for gender and age, 2 important covariates for COPD, to balance them in the sample. In addition, we measure all known important covariates or confounders to make the desired adjustments in our data analysis. Fourth, the study is conducted in one city, Almaty, which reduces the generalizability of the study results to the country population. Finally, we expect some measurement bias that arises from errors in the data collection. For example, participants could avoid socially undesirable answers. To reduce the latter bias, all data collection procedures were tested and the research staff members were trained.