Chronic Obstructive Pulmonary Disease in Cameroon: Prevalence and Predictors—A Multisetting Community-Based Study

Massongo, Massongo; Balkissou, Adamou Dodo; Endale Mangamba, Laurent-Mireille; Poka Mayap, Virginie; Ngah Komo, Marie Elisabeth; Nsounfon, Abdou Wouoliyou; Kuaban, Alain; Pefura Yone, Eric Walter

doi:https://doi.org/10.1155/2023/1631802

Pulmonary Medicine

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Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Ethical Approval Consent Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 1631802 | https://doi.org/10.1155/2023/1631802

Chronic Obstructive Pulmonary Disease in Cameroon: Prevalence and Predictors—A Multisetting Community-Based Study

Massongo Massongo,¹Adamou Dodo Balkissou,²Laurent-Mireille Endale Mangamba,^3,4Virginie Poka Mayap,⁵Marie Elisabeth Ngah Komo,^1,5Abdou Wouoliyou Nsounfon,⁶Alain Kuaban,^1,5and Eric Walter Pefura Yone^1,5

Academic Editor: Yongchun Shen

Received28 Mar 2023

Revised22 Aug 2023

Accepted01 Sept 2023

Published13 Sept 2023

Abstract

Objective. Little is known concerning chronic obstructive pulmonary disease (COPD) in Sub-Saharan Africa (SSA), where the disease remains underdiagnosed. We aimed to estimate its prevalence in Cameroon and look for its predictors. Methods. Adults aged 19 years and older were randomly selected in 4 regions of Cameroon to participate in a cross-sectional community-based study. Data were collected in the participant’s home or place of work. Spirometry was performed on selected participants. COPD was defined as the postbronchodilator forced expiratory volume in 1 second/forced vital capacity ratio limit of normal, using the global lung initiative (GLI) equations for Black people. Binomial logistic regression was used to seek COPD-associated factors. The strength of the association was measured using the adjusted odds ratio (aOR). Results. A total of 5055 participants (median age (25^th-75^th percentile) = 43 (30–56) years, 54.9% of women) were enrolled. COPD prevalence (95% confidence interval (95% CI)) was 2.9% (2.4, 3.3)%. Independent predictors of COPD (aOR (95% CI)) were a high educational level (4.7 (2.0, 11.1)), living in semiurban or rural locality (1.7 (1.4, 3.0)), tobacco smoking (1.7 (1.1, 2.5)), biomass fuel exposure (1.9 (1.1, 3.3)), experience of dyspnea (2.2 (1.4, 3.5)), history of tuberculosis (3.6 (1.9, 6.7)), and history of asthma (6.3 (3.4, 11.6)). Obesity was protective factor (aOR (0.2, 0.5)). Conclusion. The prevalence of COPD was relatively low. Alternative risk factors such as biomass fuel exposure, history of tuberculosis, and asthma were confirmed as predictors.

1. Introduction

Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation causing respiratory symptoms such as cough and shortness of breath, acute episodes known as exacerbations, and a progressive decline of lung function, which can lead to chronic respiratory failure and disability, with a great impact on quality of life. This is a growing and threatening public health problem worldwide, with an estimated 251 million cases in 2016 [1]. The number of COPD cases has been increasing in the past decades. According to Adeyole et al.’s systematic review relying on 123 studies and more than 877, 000 participants, the estimated COPD cases increased by 68.9% worldwide and 102% in Africa between 1990 and 2010 [2]. Spirometry-diagnosed COPD prevalence varies widely from one country or region to another, and even within countries and regions, ranging from less than 3% to more than 35% [3–5]. This prevalence appears to be low in the poorest regions of the world and to rise when the mean number of pack-years smoked increases [4].

Mortality associated with COPD is also increasing, and projections put it as the 4^th leading cause of death worldwide as well as in low-income countries (LIC) by 2030 [6]. Up to 90% of those deaths currently occur in low- and middle-income countries (LMIC) [1]. The most commonly cited cause of COPD is exposure to tobacco smoke. Other well-known risk factors for COPD include advancing age [5, 7, 8], occupational and outdoor exposures [8–11], and low birth weight or childhood respiratory disorders [8]. However, evidence is growing on alternative risk factors such as biomass fuel [7, 11–14], history of tuberculosis (TB) [15–22], or socioeconomic status [5, 7, 23], especially in LMIC. Much more, over 3 billion people are exposed to biomass fumes, while only 1 billion are exposed to tobacco smoking [12].

In Sub-Saharan Africa, mostly made up of LIC, there has been a marked increase in COPD prevalence and mortality; studies, however, had been relatively scarce prior to 2010. Their numbers increased during the past decade, although most of them originated from Eastern [24–26] and Southern [13, 27–31] Africa. We found only 3 recent COPD-related studies originating from West or Central Africa: 2 of these were hospital-based and human immunodeficiency syndrome (HIV) related [32, 33], and only one was community-based, which is part of the present study [34]. Much more, COPD-related awareness is poor among health workers as well as the community [13, 27]. We aimed to assess COPD prevalence and look for its determinants among an adult Cameroonian population.

2. Materials and Methods

2.1. Design, Setting, and Period

This was a community-based cross-sectional study, led from 2014 to 2018 in 5 localities (2 urban, 2 semiurban, and 1 rural) in Cameroon, a central African country with an estimated 23794164 inhabitants in 2018 [35], who are divided into 4 cultural groups: Fang-Beti, Grass fields, Sawa, and Sudano-Sahelian. Participants were recruited during 4 distinct periods of 5-6 months each: from December 2013 to May 2014 in Yaoundé, from December 2015 to April 2016 in Bandjoun, from December 2016 to May 2017 in Douala, and from December 2017 to April 2018 in Garoua and Figuil. Yaounde is the nation’s capital, located in the Centre Region, with a cosmopolitan but mainly Fang-Beti population that was estimated at 1817524 in 2018. Douala is the country’s economic capital, with a population of 1907479 inhabitants, who are also cosmopolitan and primarily of the Sawa cultural group. Yaounde and Douala were 2 urban settings. Bandjoun (West Region, 65 021 inhabitants) and Garoua (North Region, 265 583 inhabitants) were the 2 semiurban settings, with Grass fields and Sudano-Sahelian as the main cultural groups, respectively. Figuil (North Region, 67 997 inhabitants) was the only rural setting with a mainly Sudano-Sahelian population. Each of these localities was divided into count zones used in the 2005 national census [35] and included various numbers of health areas defined by the national health system.

2.2. Population and Sampling

Adults aged 19 years and above, living in the study area, and free from cognitive, auditory, or language impairments, were randomly invited to participate following a 3-level clustered sampling. At the first level, given numbers of count zones (CZ) and health areas (HA) were randomly selected in urban and semiurban/rural settings, respectively. At the second level, households were selected using a systematic random sampling, with a rate of 1 over 2-3 in CZ and 1 over 1-14 in HA. At the third level, all people years in the selected households were invited to participate in the study. Those who declined our invitation and those who did not complete the study questionnaire were excluded. Given the limited time and device availability, a spirometry was randomly proposed to half of the included participants.

We calculated our required sample size with the StatCalc tool of Epi Info version 7.2.3.1 (EPI INFO™, Center for Disease Control and Prevention, USA), using a dedicated formula for population surveys. We considered a 95% confidence interval, 5% expected prevalence found by Pefura-Yone et al. in Yaounde and Foumbot (Cameroon) a year before this study [32], a 1% margin of error, a design effect of 2, and a 10% nonresponse rate. The obtained sample size was 4501.

2.3. Data Collection and Variables

Seventh year trained medical students collected data, during a face-to-face interview using an electronic questionnaire. The anamnestic data included demographic (gender, age, and residency) and socioeconomic (level of education, marital status, ethnicity, and socioeconomic level) data, tobacco smoking and alcohol consumption history, biomass fuel exposure, and medical history (tuberculosis, asthma, pneumonia, high blood pressure, heart disease, and diabetes). Respiratory symptoms (chronic cough, chronic expectoration, and dyspnea), anthropometric parameters (weight, height, body mass index, and related categories), and spirometry data (forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and FEV1 over FVC ratio (FEV1/FVC), as well as their lower limits of normal (LLN)) were also collected.

Anamnestic data were collected by questioning the subject or a relative. Demographic data, socioeconomic data, habits, and symptoms were self-reported. Data on medical history were those reported as diagnosed by a health professional. The height was measured by a wooden measuring board of local manufacture graduated in centimeters, and the weight by an electronic weighing scale. The body mass index (BMI) was calculated as weight (in kg)/height² (in m²) and used to divide the study population into 4 weight categories: lean (), normal ( and <25), overweight ( and <30), and obese (). Biomass fuel exposure was defined by the use for more than 6 months of either coal, charcoal, wood (logs or chips), sawdust, dung, or crop residues for heating or cooking purposes. Tobacco smoking was categorized as “never smoked” (less than 20 cigarettes for the whole lifespan) and “ever smoked” which was subsequently quantified in pack-years.

Spirometry measurements were performed using a digital turbine pneumotachograph (Spiro USB, Care fusion, Yorba Linda-USA), following American Thoracic Society standards and European Respiratory Society guidelines [36, 37]. Each selected participant was comfortably seated and was asked to perform the procedures after suitable explanations. Prebronchodilator procedures were first performed, measuring FEV1, FVC, and FEV1/FVC. Those with , defined as airflow limitation (AFL), were asked to inhale 400 μg of pressurized salbutamol before undergoing a postbronchodilator procedure 20 minutes later, to check the reversibility of obstruction. Those with no AFL were considered to not have any ventilator obstructive defect. The LLNs for postbronchodilator FEV1/FVC were determined using the global lung initiative (GLI) 2012 Data Conversion tool (Global Lung Initiative Version 1.3.2, ©2012 PH Quanjer, S Tanojevic, TJ Colze, J Stocks) for Black people.

COPD was defined as a LLN. Prebronchodilator FEV1 was used to classify COPD in severity stages, according to the first Global Obstructive Lung Disease (GOLD) classification [38].

2.4. Data Management and Analysis

The collected data were recorded directly in the Epidata entry 3.1 software. Analyses were done using R, Version 4.0.2. Qualitative data were presented as counts (proportions) and quantitative variables as medians (25th-75th percentiles).

The association between COPD and independent variables was tested by using binary logistic regression and determined by the odds ratios (OR) and their 95% confidence interval (95% CI). A univariate analysis was first performed with the crude OR (cOR). Variables associated with COPD with a value < 0.10 were eligible for a step-down sequential multiple logistic regression to determine the adjusted OR (aOR). The latter stopped when any variable with a nonsignificant association ( value ≥ 0.05) had been removed, unless it had been identified as a confounder (removal causing model destabilization or a >30% change in aOR of another variable).

3. Results

3.1. Population Characteristics

A total of 11118 subjects were invited to participate in the study, 305 (2.7%) of them declined our invitation, and 144 (1.3%) were excluded for not completing the questionnaire. Among the 10669 participants eligible, 5055 had a valid prebronchodilator spirometry, and 5021 ultimately provided fully valid data (Figure 1).

The age of our participants ranged from 19 to 96 years, with a median (25^th-75^th percentile) of 43 (30–56) years. Forty percent of them were less than 40 years of age. The sex ratio was 0.8. Nearly 20% of our sample was illiterate, and 12.7% had reached the university level. More than 2/3 of the participants came from an urban setting, and only 20% were ranked as having a high socioeconomic level. The Semi-Bantu ethnic group (54.7%) was the most frequent. Eight hundred and twenty-seven (16.4%) of the subjects admitted they had smoked tobacco, and 81.0% of them had pack-years (PY). Only 3755 participants, of whom only 20% admitted regular consumption, reported alcohol consumption. Biomass fuel exposure was present in 68.2% of the whole sample and 96.1% of the periurban-rural settings. A history of tuberculosis, pneumonia, asthma, and diabetes mellitus were present in less than 3% of the sample for each. Chronic cough and chronic expectoration had a low frequency. Sixty percent of the enrolled population were overweight or obese. These baseline characteristics are presented in Table 1.

3.2. Spirometry and COPD

A total of 5021 participants performed valid spirometry with postbronchodilator measurements. Median prebronchodilator FEV1 and FVC were 2.5 (1.9, 3.1) L and 3.0 (2.3, 3.6) L, respectively. One hundred and forty-five participants met the definition of COPD, giving a prevalence (95% CI) of 2.9% (2.4, 3.3). Data on COPD severity and risk factors are shown in Table 2. When the sample was restricted to -year-old, 111/2 977 of them met the definition of COPD, giving a prevalence of 3.7% (3.0, 4.4).

3.3. COPD Predictors

In univariate analysis, several factors were associated with COPD. There was a positive correlation with age as both a continuous variable and a categorical one, with an increasing crude OR with respect to age category, taking 19-39 years as reference. Men were more likely to have COPD than women (3.4% vs. 2.4%), but the difference was not significant. Compared with illiterate participants, educated ones were more prone to COPD, and cORs were proportional to the level of education. COPD was also associated with a low socioeconomic level, Semi-Bantu, and other ethnic groups compared to Bantu, tobacco smoking, biomass fuel exposure, history of tuberculosis, history of asthma, chronic bronchitis, and dyspnea. Obese people were less likely to present COPD compared with people of normal weight. Data from univariate analysis are detailed in Table 3.

Among the 16 variables eligible for multivariate step-down analysis, 3 (alcohol consumption, chronic bronchitis, and chronic expectoration) were withdrawn due to excessive missingness. Thus, 13 variables entered the process of multiple logistic regression analysis. Smoking was kept only in its dichotomic form. At the end of this process, seven factors emerged as independent predictors of COPD: educational level, with the highest aOR corresponding to the university level; living in a semiurban or rural locality; tobacco smoking; biomass fuel exposure; experience of dyspnea; history of tuberculosis; and history of asthma. Obesity appeared to be protective against COPD, after adjustment for other confounders. The multivariate analysis is detailed in Table 4.

4. Discussion

Our study revealed a 2.9% prevalence of COPD-LLN among Cameroonians aged 19 years and older. Independent predictors of this condition were advanced educational level, suburban or rural residency, tobacco smoking, biomass fuel exposure, the presence of dyspnea, history of tuberculosis, and asthma, while being obese appeared to be a protective factor. Factors associated with COPD in univariate analysis included age, male gender, low socioeconomic level, non-Bantu ethnic groups, and chronic bronchitis.

Regarding epidemiological and clinical features, we can group these factors in 3 main COPD-related categories: (1) risk factors (age, tobacco smoking, biomass fuel exposure, history of tuberculosis, and asthma), (2) clinical manifestations (dyspnea and chronic bronchitis), and (3) confounders (gender, level of education, residency, socioeconomic level, ethnic group, alcohol consumption, and weight category).

In our study, we chose 19-year-olds as the threshold rather than 40-year-olds. This was driven by the knowledge of local features of COPD, which may involve a younger population, compared with western settings. LMIC-specific risk factors such as biomass fuel exposure or a history of tuberculosis, combined with a lower prevalence of asthma, are thought to allow this higher involvement of young subjects in COPD.

Our COPD prevalence was relatively low, even when the sample was limited to years old (3.7%, 95% -4.4%). However, this prevalence is consistent with the recent findings on FEV1/FEV LLN-based COPD in SSA, as shown in Table 5. Of the 8 studies that used FEV1/FVC less than the LLN to define COPD (15, 24 28–30, 32–34), 5 showed a and half of them were ≤5% (Table 5). Studies that used the fixed FEV1/FVC ratio to define COPD were more likely to show a higher COPD prevalence [7, 25, 26, 33, 39, 40]. This phenomenon is thought to reveal an overestimation of COPD when using a fixed ratio, due to its physiological decline with age. In fact, FEV1/FVC values ranging between 0.65 and 0.7 are common in the elderly and are frequently higher than the related LLN. However, no matter the definition used to define COPD, data in Table 5 show that study populations -40 years [15, 25, 26, 28, 33, 39, 41] had higher COPD prevalence (range 9.3%–22.8%) than those -20 years (range 2.0%–8.03%) [11, 24, 29, 31, 32, 34]. This supports the identification of advancing age as a significant risk factor for COPD, wherever the study occurs.

Nevertheless, age was the sole known risk factor that did not remain significantly associated with COPD after multivariate analysis in our study, even though it has been widely documented in recent SSA-related literature [5, 7, 8, 25, 26, 31, 33, 34, 42]. Our age distribution, involving an overrepresentation of younger people ( years and years) who were at lower risk of COPD, as well as the great number of confounding factors involved, could partially explain this result. Tobacco smoking was confirmed as a strong predictor of COPD in this study, and the known dose-effect phenomenon has been clearly demonstrated. However, tobacco smoking exposure (16.4%) was quite lower in our sample than exposure to biomass fuel (68.2%), and its aOR was smaller than any of the other 3 risk factors (biomass fuel exposure, history of tuberculosis, and history of asthma). This is consistent with the observation that more people are exposed to biomass fuel than to tobacco smoking worldwide [12, 14] and especially in SSA [26, 28, 43]. This also assumes that in our setting or in LMIC, these alternative etiologies are more powerful than tobacco in causing COPD. Such an assumption is confirmed by recent studies that assessed simultaneously tobacco smoking and at least one of the four other risk factors as predictors of COPD, summarized in Table 6 [8, 15, 19–21, 24–26, 28, 30, 32, 42]. Of the 9 studies that assessed tobacco smoking in LMIC (excluding the Korean study [19] and 2 others derived from the BOLD study [8, 15]), only Woldeamanuel et al.’s one in Ethiopia showed tobacco smoking to be more strongly associated with COPD than the other risk factors [25]. Interestingly, even in the BOLD study, which occurred mainly in high-income countries and included only South Africa as a SSA country (over 14), Hooper et al. found an OR for a history of tuberculosis higher than that for tobacco smoking [15]. When restricted to never smokers, the database analysis found a history of asthma as a significant associated risk factor, while passive smoking was not [8]. Those studies also reveal a history of tuberculosis as the most potently associated risk factor in LMIC.

The association of COPD with its clinical manifestations (dyspnea on exertion and chronic bronchitis) was anticipated and has been described by other authors [28, 42]. Low socioeconomic status has been associated with COPD [5, 7, 23]. This was found in the present study only in the univariate analysis, probably due to the impact of other confounders. The association between COPD and low economic status seems logical as the poorest people are more exposed to potentially risk factors such as tuberculosis and biomass fuel, and even tobacco smoking. Being literate, which is not usually associated with low socioeconomic status, was strongly associated with COPD in our study, in a dose-effect manner. Similar results were shown by Hooper et al. on never smokers and Lamprecht et al., both from the BOLD study [8, 15]. An approach to explain this discrepancy could be that these findings are linked to differences in perceptions, attitudes, and awareness with regard to tobacco smoking (which is the most common risk factor worldwide). Literate people in developed and western settings do not smoke much as they are more aware of tobacco’s noxiousness, while in Cameroon, student smoking has been associated with classmates smoking, advancing in age, and attending public education [44, 45]. The higher frequency of biomass smoke exposure in semiurban or rural residences compared to urban ones (1 428/1 % vs. 2 018/, ) could explain the association of the former with COPD.

As reported by Lamprecht et al. in 2011 and Hooper et al. in 2012 from the BOLD study, obese people were less likely to have COPD in our study. This is also consistent with the results found by Pefura-Yone et al. in his facility-based case-control study in 2015, where COPD was associated with a 1 kg/m²-decrease in BMI ( (1.01-1.35), ). Smoking is usually accompanied by a decreased propensity to eat; thus, obesity is less likely to occur among smokers and subsequently among COPD carriers. This was verified in our study, where the prevalence of obesity was significantly higher in never smokers than in ever smokers (1 243/4 vs. , ). Conversely, obesity is often associated with higher socioeconomic status in our setting, the latter also being protective against COPD. This was also verified in our study, where obesity prevalence according to socioeconomic status ranged from 23.7% (473/1992) in the low class to 32.2% (325/1009) in the high class ().

As for any cross-sectional study, this one could not check the causality between identified risk factors and COPD. However, those we found here are well known and have been identified as such in prospective studies [20, 28]. Choosing the LLN threshold to define COPD could make the comparison hazardous between our results and those of studies that used 0.7 ratio, which are more frequent. Limitations of the fixed ratio have been discussed above. Moreover, recent studies on COPD are more likely to use the LLN than the fixed FEV1/FVC ratio since there is an increasing awareness of these limitations. In our study, the use of for COPD definition gave a prevalence of 3.4% (2.9, 3.9)%, which does not significantly differ from the one found using the LLN threshold. A similar argument can be made about our threshold age of 19 years rather than 40 years, which has been widely used so far. The latter was historically chosen because of the large predominance of tobacco smoking as an etiology, given the delay required before developing COPD. Another factor for choosing 40-year threshold was the rarity of asthma development after that age, minimizing the risk of confusion. Recent observations and findings on alternative risk factors, such as history of tuberculosis and biomass exposure, should encourage the use of a lower age threshold for COPD definition. Self-reporting of various exposures may have misclassified some participants. However, the consistency of our results with known epidemiology and related literature makes us feel that there has been no significant bias.

To the best of our knowledge, our study is the largest community-based study ever conducted in Africa to estimate the prevalence of COPD and identify its predictors. Moreover, we think that the rigorous randomized selection process of participants and the multicenter nature of the study made our sample more representative of the Cameroonian population. The large sample size allowed simultaneous analysis of multiple factors, leading to the identification of 8 predictors after multivariate analysis.

5. Conclusion

The current study confirmed previous findings about the low prevalence of COPD in SSA compared to western settings, as well as new risk factors. It is thereby contributing to improving knowledge on COPD in this subregion. Being aware of these findings may encourage the population to avoid hazardous behaviours, clinicians to actively seek COPD when facing those risk factors, and finally decision-makers to undertake efficient measures against the identified factors.

Data Availability

The dataset used for our analysis are available upon demand to the supervisor of the study. Any request should be done by email to pefurayone@gmail.com.

Ethical Approval

The Ethics Committees of both the Faculty of Medicine and Pharmaceutical Sciences of the Douala University and the Faculty of Medicine and Biomedical Sciences of the University of Yaounde approved the study. Furthermore, the health authorities of the 4 regions in which the study was carried out (Center, Littoral, West, and North) provided administrative authorization for the study.

Informed consents were obtained from all participants.

Conflicts of Interest

The authors declare no conflict of interest.

Authors’ Contributions

Massongo M conducted the data curation, formal analysis, supervision, writing of the original draft, writing, review, and editing. Balkissou AD also conducted the data curation, formal analysis, supervision, methodology, writing, review, and editing. Endale Mangamba LM performed the data curation, writing, review, and editing. Poka Mayap V conducted the analysis, writing, review, and editing. Ngah Komo ME wrote, reviewed, and edited the paper. Nsounfon AW wrote, reviewed, and edited the paper. Kuaban A wrote, reviewed, and edited the paper. Pefura Yone EW conducted the conceptualization, methodology, project administration, supervision, writing, review, and editing. All authors approved the final draft.

Acknowledgments

We sincerely thank all local populations who accepted to participate in this study, as well as the medical, administrative, and traditional authorities in the 4 visited regions. We also warmly thank all members of the Respiratory Health Survey Group in Cameroon, who performed the data collection.

References

WHO, “Chronic obstructive pulmonary disease (COPD),” 2020, https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd).
View at: Google Scholar
D. Adeloye, S. Chua, C. Lee et al., “Global and regional estimates of COPD prevalence: systematic review and meta-analysis,” Journal of Global Health, vol. 5, no. 2, article 020415, 2015.
View at: Publisher Site | Google Scholar
C. E. Rycroft, A. Heyes, L. Lanza, and K. Becker, “Epidemiology of chronic obstructive pulmonary disease: a literature review,” International Journal of Chronic Obstructive Pulmonary Disease, vol. 7, pp. 457–494, 2012.
View at: Publisher Site | Google Scholar
P. Burney, A. Jithoo, B. Kato et al., “Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty—a BOLD analysis,” Thorax, vol. 69, no. 5, pp. 465–473, 2014.
View at: Publisher Site | Google Scholar
L. J. Finney, J. R. Feary, J. Leonardi-Bee, S. B. Gordon, and K. Mortimer, “Chronic obstructive pulmonary disease in sub-Saharan Africa: a systematic review [review article],” The International Journal of Tuberculosis and Lung Disease, vol. 17, no. 5, pp. 583–589, 2013.
View at: Publisher Site | Google Scholar
C. D. Mathers and D. Loncar, “Projections of global mortality and burden of disease from 2002 to 2030,” PLoS Medicine, vol. 3, no. 11, article e442, 2006.
View at: Publisher Site | Google Scholar
D. G. Fullerton, A. Suseno, S. Semple et al., “Wood smoke exposure, poverty and impaired lung function in Malawian adults,” The International Journal of Tuberculosis and Lung Disease, vol. 15, no. 3, pp. 391–398, 2011.
View at: Google Scholar
B. Lamprecht, M. A. McBurnie, W. M. Vollmer et al., “COPD in never smokers: results from the population-based burden of obstructive lung disease study,” Chest, vol. 139, no. 4, pp. 752–763, 2011.
View at: Publisher Site | Google Scholar
J. E. Myers and J. E. Cornell, “Respiratory health of brickworkers in Cape Town, South Africa. Symptoms, signs and pulmonary function abnormalities,” Scandinavian Journal of Work, Environment & Health, vol. 15, no. 3, pp. 188–194, 1989.
View at: Publisher Site | Google Scholar
U. G. Oleru and C. Onyekwere, “Exposures to polyvinyl chloride, methyl ketone and other chemicals. The pulmonary and non-pulmonary effect,” International Archives of Occupational and Environmental Health, vol. 63, no. 7, pp. 503–507, 1992.
View at: Publisher Site | Google Scholar
P. Martins, J. Rosado-Pinto, M. do Céu Teixeira et al., “Under-report and underdiagnosis of chronic respiratory diseases in an African country,” Allergy, vol. 64, no. 7, pp. 1061–1067, 2009.
View at: Publisher Site | Google Scholar
S. S. Salvi and P. J. Barnes, “Chronic obstructive pulmonary disease in non-smokers,” The Lancet, vol. 374, no. 9691, pp. 733–743, 2009.
View at: Publisher Site | Google Scholar
F. van Gemert, N. Chavannes, N. Nabadda et al., “Impact of chronic respiratory symptoms in a rural area of sub-Saharan Africa: an in-depth qualitative study in the Masindi district of Uganda,” Primary Care Respiratory Journal, vol. 22, no. 3, pp. 300–305, 2013.
View at: Publisher Site | Google Scholar
R. Kodgule and S. Salvi, “Exposure to biomass smoke as a cause for airway disease in women and children,” Current Opinion in Allergy and Clinical Immunology, vol. 12, no. 1, pp. 82–90, 2012.
View at: Publisher Site | Google Scholar
R. Hooper, P. Burney, W. M. Vollmer et al., “Risk factors for COPD spirometrically defined from the lower limit of normal in the BOLD project,” The European Respiratory Journal, vol. 39, no. 6, pp. 1343–1353, 2012.
View at: Publisher Site | Google Scholar
A. M. B. Menezes, P. C. Hallal, R. Perez-Padilla et al., “Tuberculosis and airflow obstruction: evidence from the PLATINO study in Latin America,” The European Respiratory Journal, vol. 30, no. 6, pp. 1180–1185, 2007.
View at: Publisher Site | Google Scholar
R. I. Ehrlich, N. White, R. Norman et al., “Predictors of chronic bronchitis in south African adults,” The International Journal of Tuberculosis and Lung Disease, vol. 8, no. 3, pp. 369–376, 2004.
View at: Google Scholar
R. Perez-Padilla, R. Fernandez, M. V. Lopez Varela et al., “Airflow obstruction in never smokers in five Latin American cities: the PLATINO study,” Archives of Medical Research, vol. 43, no. 2, pp. 159–165, 2012.
View at: Publisher Site | Google Scholar
S. W. Lee, Y. S. Kim, D. S. Kim, Y. M. Oh, and S. D. Lee, “The risk of obstructive lung disease by previous pulmonary tuberculosis in a country with intermediate burden of tuberculosis,” Journal of Korean Medical Science, vol. 26, no. 2, pp. 268–273, 2011.
View at: Publisher Site | Google Scholar
C. H. Lee, M. C. Lee, H. H. Lin et al., “Pulmonary tuberculosis and delay in anti-tuberculous treatment are important risk factors for chronic obstructive pulmonary disease,” PLoS One, vol. 7, no. 5, article e37978, 2012.
View at: Publisher Site | Google Scholar
L. F. Idolor, T. S. D. Guia, N. A. Francisco et al., “Burden of obstructive lung disease in a rural setting in the Philippines,” Respirology, vol. 16, no. 7, pp. 1111–1118, 2011.
View at: Publisher Site | Google Scholar
A. Caballero, C. A. Torres-Duque, C. Jaramillo et al., “Prevalence of COPD in five Colombian cities situated at low, medium, and high altitude (PREPOCOL study),” Chest, vol. 133, no. 2, pp. 343–349, 2008.
View at: Publisher Site | Google Scholar
M. J. Hegewald and R. O. Crapo, “Socioeconomic status and lung function,” Chest, vol. 132, no. 5, pp. 1608–1614, 2007.
View at: Publisher Site | Google Scholar
T. Zoller, E. H. Mfinanga, T. B. Zumba et al., “Chronic airflow obstruction in Tanzania – a cross-sectional study,” BMC Pulmonary Medicine, vol. 18, no. 1, p. 11, 2018.
View at: Publisher Site | Google Scholar
G. G. Woldeamanuel, A. B. Mingude, and T. G. Geta, “Prevalence of chronic obstructive pulmonary disease (COPD) and its associated factors among adults in Abeshge District, Ethiopia: a cross sectional study,” BMC Pulmonary Medicine, vol. 19, no. 1, p. 181, 2019.
View at: Publisher Site | Google Scholar
N. F. Magitta, R. W. Walker, K. K. Apte et al., “Prevalence, risk factors and clinical correlates of COPD in a rural setting in Tanzania,” The European Respiratory Journal, vol. 51, no. 2, article 1700182, 2018.
View at: Publisher Site | Google Scholar
F. A. van Gemert, B. J. Kirenga, T. H. Gebremariam, G. Nyale, C. de Jong, and T. van der Molen, “The complications of treating chronic obstructive pulmonary disease in low income countries of sub-Saharan Africa,” Expert Review of Respiratory Medicine, vol. 12, no. 3, pp. 227–237, 2018.
View at: Publisher Site | Google Scholar
F. van Gemert, B. Kirenga, N. Chavannes et al., “Prevalence of chronic obstructive pulmonary disease and associated risk factors in Uganda (FRESH AIR Uganda): a prospective cross-sectional observational study,” The Lancet Global Health, vol. 3, no. 1, pp. e44–e51, 2015.
View at: Publisher Site | Google Scholar
C. M. North, B. Kakuhikire, D. Vořechovská et al., “Prevalence and correlates of chronic obstructive pulmonary disease and chronic respiratory symptoms in rural southwestern Uganda: a cross-sectional, population-based study,” Journal of Global Health, vol. 9, no. 1, article 010434, 2019.
View at: Publisher Site | Google Scholar
A. Kayongo, A. C. Wosu, T. Naz et al., “Chronic obstructive pulmonary disease prevalence and associated factors in a setting of well-controlled HIV, a cross-sectional study,” COPD: Journal of Chronic Obstructive Pulmonary Disease, vol. 17, no. 3, pp. 297–305, 2020.
View at: Publisher Site | Google Scholar
J. Meghji, G. Nadeau, K. J. Davis et al., “Noncommunicable lung disease in sub-Saharan Africa. A community-based cross-sectional study of adults in urban Malawi,” American Journal of Respiratory and Critical Care Medicine, vol. 194, no. 1, pp. 67–76, 2016.
View at: Publisher Site | Google Scholar
E. W. Pefura-Yone, G. Fodjeu, A. P. Kengne, N. Roche, and C. Kuaban, “Prevalence and determinants of chronic obstructive pulmonary disease in HIV infected patients in an African country with low level of tobacco smoking,” Respiratory Medicine, vol. 109, no. 2, pp. 247–254, 2015.
View at: Publisher Site | Google Scholar
M. O. Akanbi, B. O. Taiwo, C. J. Achenbach et al., “HIV associated chronic obstructive pulmonary disease in Nigeria,” Journal of AIDS & Clinical Research, vol. 6, no. 5, p. 453, 2015.
View at: Publisher Site | Google Scholar
E. W. Pefura-Yone, A. P. Kengne, A. D. Balkissou et al., “Prevalence of obstructive lung disease in an African country using definitions from different international guidelines: a community based cross-sectional survey,” BMC Research Notes, vol. 9, no. 1, p. 124, 2016.
View at: Publisher Site | Google Scholar
BUCREP (BUreau Central de Recensement et d’Etude de la Population du Cameroun), “Population en chiffres,” 2010, http://www.bucrep.cm/.../20-3eme-rgph/presentation/57-population-en-chiffre.
View at: Google Scholar
American Thoracic Society, “Standardization of spirometry, 1994 Update. American Thoracic Society,” American Journal of Respiratory and Critical Care Medicine, vol. 152, no. 3, pp. 1107–1136, 1995.
View at: Publisher Site | Google Scholar
M. R. Miller, J. Hankinson, V. Brusasco et al., “Standardisation of spirometry,” The European Respiratory Journal, vol. 26, no. 2, pp. 319–338, 2005.
View at: Publisher Site | Google Scholar
R. A. Pauwels, A. S. Buist, P. M. A. Calverley, C. R. Jenkins, and S. S. Hurd, “Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease,” American Journal of Respiratory and Critical Care Medicine, vol. 163, no. 5, pp. 1256–1276, 2001.
View at: Publisher Site | Google Scholar
A. S. Buist, M. A. McBurnie, W. M. Vollmer et al., “International variation in the prevalence of COPD (the BOLD study): a population-based prevalence study,” Lancet, vol. 370, no. 9589, pp. 741–750, 2007.
View at: Publisher Site | Google Scholar
B. V. Girdler-Brown, N. W. White, R. I. Ehrlich, and G. J. Churchyard, “The burden of silicosis, pulmonary tuberculosis and COPD among former Basotho goldminers,” American Journal of Industrial Medicine, vol. 51, no. 9, pp. 640–647, 2008.
View at: Publisher Site | Google Scholar
I. M. Gathuru, C. H. Bunker, F. A. Ukoli, and E. E. Egbagbe, “Differences in rates of obstructive lung disease between Africans and African Americans,” Ethnicity & Disease, vol. 12, no. 4, 2002.
View at: Google Scholar
B. H. Mbatchou Ngahane, E. Afane Ze, C. Chebu et al., “Effects of cooking fuel smoke on respiratory symptoms and lung function in semi-rural women in Cameroon,” International Journal of Occupational and Environmental Health, vol. 21, no. 1, pp. 61–65, 2015.
View at: Publisher Site | Google Scholar
A. Pye, S. Ronzi, B. H. Mbatchou Ngahane, E. Puzzolo, A. H. Ashu, and D. Pope, “Drivers of the adoption and exclusive use of clean fuel for cooking in sub-Saharan Africa: learnings and policy considerations from Cameroon,” International Journal of Environmental Research and Public Health, vol. 17, no. 16, p. 5874, 2020.
View at: Publisher Site | Google Scholar
B. H. Mbatchou Ngahane, H. Atangana Ekobo, and C. Kuaban, “Prevalence and determinants of cigarette smoking among college students: a cross-sectional study in Douala, Cameroon,” Archives of Public Health, vol. 73, no. 1, p. 47, 2015.
View at: Publisher Site | Google Scholar
B. H. Mbatchou Ngahane, H. Luma, Y. N. Mapoure, Z. M. Fotso, and Z. E. Afane, “Correlates of cigarette smoking among university students in Cameroon,” The International Journal of Tuberculosis and Lung Disease, vol. 17, no. 2, pp. 270–274, 2013.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Massongo Massongo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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