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Original Research: COPD |

Risk of COPD From Exposure to Biomass Smoke: A Metaanalysis FREE TO VIEW

Guoping Hu, MD, PhD; Yumin Zhou, MD, PhD; Jia Tian, MD; Weimin Yao, MD, PhD; Jianguo Li, MD, PhD; Bing Li, MD; Pixin Ran, MD, PhD, FCCP
Author and Funding Information

From the Guangzhou Institute of Respiratory Diseases (Drs Hu, Zhou, Tian, J. Li, and Ran), State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital, Guangzhou Medical University; the Department of Respiratory Medicine (Dr Hu), The Third Affiliated Hospital, Guangzhou Medical University; the Department of Respiratory Medicine (Dr J. Li), The Second Affiliated Hospital of Sun Yat-sen University; the Experiment of Medical Central of Guangzhou Medical University (Dr B. Li); Guangzhou, Guangdong; and the Department of Respiratory Medicine (Dr Yao), Affiliated Hospital of Guangdong Medical College, Zhanjiang, China.

Correspondence to: Pixin Ran, MD, PhD, FCCP, Guangzhou Institute of Respiratory Diseases, State Key Laboratory of Respiratory Diseases, The First Affiliated Hospital, Guangzhou Medical University, 151 Yanjiang Rd, Guangzhou, Guangdong, 510120, China; e-mail: pxran@gzhmc.edu.cn


For editorial comment see page 3

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (www.chestpubs.org/site/misc/reprints.xhtml).


© 2010 American College of Chest Physicians


Chest. 2010;138(1):20-31. doi:10.1378/chest.08-2114
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Background:  Although many studies have suggested that biomass smoke is a risk factor for COPD, the relationship between the two has not been firmly established. In particular, the extent of the association between exposure of biomass smoke and COPD in different populations, as well as the relationship between biomass smoke and cigarette smoke, is not clear. To ascertain the relationship between biomass smoke and COPD, we performed a metaanalysis.

Methods:  We searched MEDLINE, EMBASE, and the Latin American and Caribbean Literature in Health Sciences Database and analyzed 15 epidemiologic (11 cross-sectional and four case-control) studies that met our criteria. Data were extracted and analyzed independently by two investigators using a standardized protocol.

Results:  Overall, people exposed to biomass smoke have an odds ratio (OR) of 2.44 (95% CI, 1.9-3.33) for developing COPD, relative to those not exposed to biomass smoke. Biomass smoke exposure was clearly identified as a risk factor for developing COPD in both women (OR, 2.73; 95% CI, 2.28-3.28) and men (OR, 4.30; 95% CI, 1.85-10.01), and in both the Asian population (OR, 2.31; 95% CI, 1.41-3.78) and the non-Asian population (OR, 2.56; 95% CI, 1.71-3.83). This risk factor has also been revealed in patients with chronic bronchitis (OR, 2.56; 95% CI, 1.77-3.70) and COPD (OR, 2.65; 95% CI, 1.75-4.03), and in cigarette smokers (OR, 4.39; 95% CI, 1.40-4.66) and non-cigarette smokers (OR, 2.55; 95% CI, 2.06-3.15).

Conclusions:  Exposure to biomass smoke is a risk factor for COPD.

Figures in this Article

Wood and other forms of biomass (animal dung, crop residues, and so forth) are commonly used as sources of energy in developing countries1,2 and are estimated to be used for 50% of household cooking and heating fuels worldwide. Combustion of biomass produces a large amount of smoke that spreads into the environment as air pollutants. Exposure to such biomass smoke has been documented as playing an important role in mortality and morbidity globally.1,3

Over the past decade, COPD has become a major public health problem, with increasing prevalence throughout the world,4 and this prevalence results from an interaction between host and environmental factors. As widely recognized, tobacco smoke is the most important risk factor for COPD; however, exposure to biomass smoke can be as hazardous where indoor ventilation is inefficient.2,5 Recently, considerable attention has been devoted to the relationship between smoke from biomass combustion and declined pulmonary function in COPD.6-32 Although most studies did show an association, controversies over this issue remain. Moreover, the extent of such an association remains largely unknown. We searched the literature on the association between biomass smoke and COPD to determine whether ethnicity, sex, smoking status, study design, phenotype of COPD, and duration of exposure to biomass smoke have different effects on the development of COPD.

Search of the Literature

Papers published in the MEDLINE database, the Latin American and Caribbean Literature in Health Sciences Database, and EMBASE were searched (up to January 2009) with key words including “COPD,” “chronic bronchitis,” “emphysema,” “chronic obstructive pulmonary disease,” “biomass fuel,” “ biofuel,” “organic fuel,” “wood,” and “indoor air pollution.” Articles about relevant studies in the references were also obtained. Only studies published in the English language were included in the analysis. We communicated with some of the authors for additional data that did not appear in the text, and also communicated with the Chinese COPD Alliance and e-mailed researchers on COPD outside China for data from unpublished or ongoing studies.

Study Selection

All potentially relevant manuscripts were reviewed independently by two investigators. Areas of disagreement or uncertainty were adjudicated by other investigators. For studies to be included in the metaanalysis, they had to meet the following criteria: (1) they had to contain a case-control or cross-sectional study design; (2) they had to have compared at least two groups (COPD vs control, or biomass smoke vs control); (3) they had to show odds ratios (ORs) to estimate the association between COPD and biomass smoke with corresponding 95% CIs, or with sufficient data for calculation; and (4) they had to be independent from other studies. Studies with the same data sets as already published studies were not deemed to be independent. As such, only studies with large sample sizes and sufficient information for data extraction were metaanalyzed. No limitations were set for participants’ ages or definition of exposure to biomass smoke as used in individual studies. Two major phenotypes of COPD, emphysema and chronic bronchitis, were included, although some cases are not characterized by airflow limitation that is not fully reversible. Chronic bronchitis was clinically diagnosed as chronic productive cough for 3 months in each of 2 successive years with no known causative factors. Because epidemiologic surveys have shown undiagnosed COPD in about two-thirds of subjects,33,34 studies in which the diagnostic criterion was a history of ever-diagnosed COPD, chronic bronchitis, or emphysema were excluded from the primary metaanalysis but used in a sensitivity analysis. This is because if some undiagnosed COPD were included in the control group, it could result in a differential diagnostic bias between biomass-exposed and nonexposed subjects. Case-control studies with a demonstrated source and matching control, criteria of exposure, and definition of COPD, or cross-sectional studies with demonstrated criteria of exposure and definition of COPD, were considered high-quality studies.

Data Extraction

All data were extracted independently by two investigators using a standardized protocol and data-collection forms. Disagreements were resolved by discussion. The studies were recorded as follows: first author, year of publication, study design (case-control study or cross-sectional study), characteristics of the study subjects (definition of exposure and nonexposure to biomass smoke, diagnosis criteria of COPD, sample size, age, gender, ethnicity, status of cigarette smoking, and duration of exposure to biomass smoke), measures of outcome and exposure, the ORs of COPD associated with biomass smoke, and standard errors (overall and in each subgroup, according to gender and smoking status).

Statistical Analysis

Metaanalysis was performed using Stata, version 7.0, statistical software (Stata Corporation; College Station, TX). The heterogeneity among studies was examined with the χ2-based Q statistic test.35 Depending on the presence of heterogeneity between studies, either a random effect model or a fixed effect model was used.36 The ORs of COPD associated with biomass smoke were estimated using nonexposure to biomass smoke as the reference. Subgroup analyses were performed with stratifications by cigarette-smoking status, sex, study design, ethnicity, duration of biomass smoke, and the phenotypes (lung function test-diagnosed COPD, emphysema, or chronic bronchitis). We further examined the relationship between exposure to biomass smoke and COPD by including the studies in which the diagnostic criterion was a history of having been given a diagnosis of COPD, chronic bronchitis, or emphysema. The significance of pooled ORs was determined by z test. All statistical tests were rendered two tailed, and P < .05 was considered significant. Potential for publication bias was assessed using the Egger test and funnel plots.37 Another method of identifying publication bias was the number of unpublished studies that would have to exist to negate the results of the metaanalysis.

Characteristics of the Included Studies

A detailed flow chart of the review process is presented in Figure 1. The initial search resulted in 984 hits of potential interest. Nine hundred fifty-seven studies were excluded upon review of the titles and abstracts. Among the remaining 27 articles6-32 on biomass smoke and the incidence of COPD, some were further excluded because of duplicated publication,6 lack of adequate data for the metaanalysis,25,30,31 or inclusion/exclusion criteria that made the study unrepresentative of the population.22-24,26-29,32 The data from the article by Liu et al6 is part of the data used by Zhong et al.7 The article by Liu et al6 provided information about the association between biomass smoke and COPD in female and cigarette nonsmokers, which was not provided by Zhong et al’s7 article. Therefore, the article by Liu et al6 was excluded from the primary metaanalysis but was used in a subgroup analysis. Subsequently, only 15 articles7-21 were included in our primary metaanalysis, comprising four case-control13,15,18,19 and 11 cross-sectional studies7-12,14,16,17,20,21 of 3,719 COPD patients and 34,969 healthy controls. The characteristics of the included studies are presented in Table 1. Studies included five publications that dealt with the phenotype of chronic bronchitis, six with COPD, and four with both chronic bronchitis and COPD. The reasons for excluding studies are presented in Table 2. No unpublished or ongoing studies were included.

Figure Jump LinkFigure 1. Studies about the association between exposure to biomass smoke and COPD identified in MEDLINE, EMBASE, and from the Latin American and Caribbean Literature in Health Sciences Database 1965-2008.Grahic Jump Location
Table Graphic Jump Location
Table 1 —Studies Included in the Metaanalysis

B = both cigarette smokers and nonsmokers; CB = chronic bronchitis (defined as cough or phlegm on most days for > 3 months per year for at least 2 consecutive years; F = female; FM = both female and male; GOLD = Global Initiative on Obstructive Lung Disease; LPG = liquid petroleum gas; N = nonsmoker.

Table Graphic Jump Location
Table 2 —Characteristics and Reasons for Exclusion of Studies Excluded From the Metaanalysis of Biomass Smoke and COPD

See Table 1 for expansion of abbreviations.

a 

This article was included in a subgroup analysis.

Association Between Exposure to Biomass Smoke and COPD

Fifteen studies7-21 examined the association between exposure to biomass smoke and COPD. A random effect model was used for the analysis because heterogeneity existed among the studies (χ2 = 126.11; P < .001). The pooled analysis found a significant elevation in risk (OR, 2.44; 95% CI, 1.79-3.33; z = 5.65; P < .001) of COPD for those exposed to biomass smoke, compared with those without the exposure (Fig 2).

Figure Jump LinkFigure 2. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass nonsmokers among the whole population.Grahic Jump Location

No significant heterogeneity was observed after stratification of the group of OR by cigarette-smoking status among three studies in which the participants were cigarette smokers 10,11,162 = 3.28; P = .194) and among seven studies in which the participants were non-cigarette smokers 6,8,10,11,14,16,172 = 6.93; P = .327). The pooled OR showed that biomass smoke was a significant risk factor for COPD for cigarette smokers and nonsmokers (for cigarettes smokers, OR = 4.39; 95% CI, 3.38-5.70; z = 11.12; P < .001; and for nonsmokers, OR = 2.55; 95% CI, 2.06-3.15; z = 8.56; P < .001) (Table 3).

Table Graphic Jump Location
Table 3 —Association Between Biomass Smoke and COPD in Total and by Subgroup

See Table 1 for expansion of abbreviation.

a 

Analyzed with a random effect model.

b 

Analyzed with a fixed effect model.

Significant heterogeneity was observed after stratification of the group of OR by ethnicity in six Asian population studies7,10,11,16,17,192 = 65.43; P < .001) and in nine non-Asian population studies8,9,12-15,18,20,212 = 42.73; P < .001). Using a random effect model, the pooled analysis showed that exposure to biomass smoke significantly increases the risk of developing COPD in both Asian and non-Asian populations (for Asians, OR = 2.31; 95% CI, 1.41-3.78; z = 3.32; P = .001; and for non-Asians, OR = 2.56; 95% CI, 1.71-3.83; z = 4.72; P < .001) (Table 3).

Significant heterogeneity was observed after stratification of the group of OR by study design in four case-control studies13,15,18,192 = 8.30; P = .04) and in 11 cross-sectional studies7-12,14,16,17,20,212 = 89.89; P < .001). The reasons for the heterogeneity are discussed below. Using a random effect model, the pooled analysis showed that biomass smoke significantly elevated the risk of developing COPD for subjects in both case-control and cross-sectional studies (for case-control studies: OR = 5.70; 95% CI, 2.95-11.02; z = 5.18; P < .001; and for cross-sectional studies: OR = 1.93; 95% CI, 1.42-2.62; z = 4.42; P < .001) (Table 3).

Domestic cooking is a major part of daily life for most women in rural areas. A woman may spend several hours daily in the kitchen while biomass is being used. With stratification by gender, heterogeneity was found among three studies in which the participants were male16,19,202 = 6.50; P = .039). The pooled results showed that biomass smoke was a significant risk factor for men for developing COPD (OR, 4.30; 95% CI, 1.85-10.01; z = 3.381; P = .001) (Table 3). No heterogeneity was observed in 11 studies6,8,9,11,13,14,16-20 in which the participants were women (χ2 = 15.48; P = .116). Using a fixed effect model, the pooled analysis revealed that biomass smoke was a significant risk factor for women for developing COPD (OR, 2.73; 95% CI, 2.28-3.28; z = 10.75; P < .001) (Fig 3, Table 3).

Figure Jump LinkFigure 3. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass non-smokers among women.Grahic Jump Location

According to the definition of COPD, patients can be classified into three subgroups: COPD, emphysema, and chronic bronchitis. However, the studies included in our metaanalysis only have two phenotype groups: COPD and chronic bronchitis. Significant heterogeneity was observed among eight chronic bronchitis studies9-11,13,15-17,20 and nine COPD studies7,9,12-15,18,19,21 (for chronic bronchitis studies: χ2 = 28.89; P < .001; and for COPD studies: χ2 = 80.06; P < .001) (Table 3). The pooled result showed that biomass smoke was a risk factor for chronic bronchitis and COPD (for chronic bronchitis: OR = 2.57; 95% CI, 1.79-3.70; z = 5.12; P < .001; and for COPD: OR = 2.77; 95% CI, 1.80-4.27; z = 4.605; P < .001).

Another study29 not included in our metaanalysis showed that the incidence of COPD decreased markedly after household coal stoves were improved, suggesting biomass smoke as a risk factor for COPD.

Sensitivity of the Analysis

We further examined the relationship between exposure to biomass smoke and COPD by including three studies in which diagnostic criteria included a history of ever-diagnosed COPD, chronic bronchitis, or emphysema. Eighteen studies7-24 were examined for association between exposure to biomass smoke and COPD, and heterogeneity was found among the studies (χ2 = 177.720; P = .000). Therefore, a random effect model was used for the analysis. The pooled analysis showed a significant elevation in the risk (OR, 2.23; 95% CI, 1.70-2.93; z = 5.74; P = .000) of developing COPD for those exposed to biomass smoke, compared with those without the exposure. (Fig 4).

Figure Jump LinkFigure 4. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass nonsmokers among the whole population, including the three articles in which the COPD diagnostic criterion is the history of having been given a diagnosis of COPD, chronic bronchitis, or emphysema.Grahic Jump Location

Table 4 shows the subgroup analysis of the association between exposure to biomass smoke and COPD when we included three studies that were based on a history of having received a diagnosis of COPD, chronic bronchitis, or emphysema. All these subgroup analyses showed that biomass smoke was a risk factor for developing COPD, except for the male sex subgroup, for which no statistical significance was reached.

Table Graphic Jump Location
Table 4 —Association Between Biomass Smoke and COPD in Total and by Subgroups When Including the Three Articles Whose Diagnostic Criterion Is a History of Ever-Diagnosed COPD, Chronic Bronchitis, or Emphysema

See Table 1 for expansion of abbreviation.

a 

Analyzed with a random effect.

b 

Analyzed with a fixed effect model.

Duration of Biomass Smoke and COPD

Exposure-response data were available in seven studies of COPD.8,12,13,15,16,18,19 The results are summarized in Table 5. Several smoke exposure metrics were used, making it impractical to combine results across studies. All seven of the exposure-response analyses found a positive trend, with correlation between developing COPD and increasing level or duration of exposure to biomass smoke.

Table Graphic Jump Location
Table 5 —Exposure-Response Relationships Between Biomass Smoke and COPD

H-y = hour-years (average number of hours of exposure to biomass smoke daily multiplied by the number of years of exposure to biomass smoke).

Funnel Plot Analysis

Figures 5 and 6 show funnel plots of the natural logarithm of OR estimates for the studies against their standard errors among all populations and among women. For all 15 studies, the P = .025 that was derived using the Egger test and the asymmetry of the distribution suggest the potential for publication bias ( Fig 5A). Further investigation showed that another seven unpublished articles may be needed to negate the results of the metaanalysis ( Fig 5B). When we included only the 11 studies in which the subjects were women, the Egger test value of P = .133 and the symmetry of the distribution suggested that a publication bias was not likely to be a problem in this analysis ( Fig 6A). Further investigation showed that only one additional unpublished article could negate the results of the metaanalysis ( Fig 6B).

Figure Jump LinkFigure 5.  Begg funnel plot with pseudo-95% CIs of results of 15 studies7-21 that examined biomass smoke and COPD (A). Filled funnel plot with pseudo-95% CIs of results of 15 studies7-21 that examined biomass smoke and COPD (B). Log of OR represents the natural logarithm of the OR of individual studies; SE of Log of OR represents the standard error in the natural logarithm of the OR of individual studies. The transverse represents SE of Log of OR. The vertical axis represents Log of OR. A dot in the figure represents a study. The horizontal line represents the combined effect and the two slopes represent the 95% CIs of the combined effects. A box represents an unpublished study that would have to exist to negate the results of the metaanalysis. OR = odds ratio. Grahic Jump Location
Figure Jump LinkFigure 6.  Begg funnel plot with pseudo 95% confidence limits of results of 11 studies that examined biomass smoke and chronic obstructive pulmonary disease6,8,9,11,13,14,16-20 among women (A). Filled funnel plot with pseudo-95% CIs of results of 11 studies7-21 that examined biomass smoke and COPD among women (B). Log of odds ratio represents the natural logarithm of the OR of individual studies; se of Log of odds ratio represents the standard error in the natural logarithm of the OR of individual studies. The transverse represents se of Log of OR. The vertical axis represents Log of OR. A dot in the figure represents a study. The horizontal represents the combined effect and the two slopes represent the 95% CIs of the combined effects. A box represents an unpublished study that would have to exist to negate the results of the metaanalysis. Grahic Jump Location

Metaanalytic methods are powerful tools for studying cumulative data from individual studies with small sample sizes and low statistical power. Pooling the effects from individual studies by a metaanalysis may increase the statistical power and can help detect modest risk differences among study groups. The large data set of this pooled analysis enabled us to investigate aspects of biomass smoke and subgroup-specific associations that could not be addressed adequately in previous studies. Although the metaanalysis can be a useful tool in environmental epidemiology, problems associated with the methodology may limit its benefits. Our analyses showed a risk association between biomass smoke and COPD. In our analyses of racial/ethnic subgroups, we detected a significant association between biomass smoke and COPD in both Asian and non-Asian subjects, and we also showed that this association did not differ across ethnic groups. The pooled OR was increased to 2.31 (95% CI, 1.41-3.78) for Asian subjects and 2.56 (95% CI, 1.71-3.83) for non-Asian subjects.

COPD usually arises from an interaction between host and environmental factors. Cigarette smoke is an important risk factor. To investigate whether there is an interaction between biomass smoke and cigarette smoke, and to investigate the source of heterogeneity, we stratified the group by cigarette-smoking status, and found that there was no significant heterogeneity among cigarette smoking studies and non-cigarette smoking studies, which showed that the heterogeneity among all the 15 studies may arise from the difference in smoking status. We also showed that associations differed with cigarette-smoking status. The pooled OR increased to 4.39 (95% CI, 3.38-5.70) for those exposed to biomass smoke or cigarette smoke. For those exposed to biomass smoke but not cigarette smoke, the pooled OR only increased to 2.55 (95% CI, 2.06-3.05). Our results imply that biomass smoke may interact with cigarette smoking in the pathogenesis of COPD.

Biomass fuels such as crop residues or woods are used in more than one-half the world’s households and a significant proportion of this activity takes place in conditions where much of the airborne effluent is released into the indoor living area.2 The persons most frequently affected are women, who do most of the cooking for households in rural villages. We made a subgroup analysis stratified by sex. Heterogeneity was found among the male studies, but not among the female studies. Our results also showed a stronger association between biomass smoke and COPD among men than among women (for men: OR = 4.30; 95% CI, 1.85-10.01; and for women: OR = 2.73; 95% CI, 2.28-3.28). A possible explanation may be the difference in cigarette-smoking status, although there was no statistically significant difference in the distribution of cigarette smoking between the case and control groups in each study. In the female studies, the number of cigarette smokers was very small, or there were no cigarette smokers at all, compared with the large and varying number of cigarette smokers in the male studies. Also, biomass smoke may interact with cigarette smoking in the pathogenesis of COPD, which may lead to heterogeneity among male studies and a stronger association between biomass smoke and COPD among men than among women. Although we could not combine the results across studies, all the present studies showed a positive trend of COPD with increasing level or duration of exposure to biomass smoke.

We stratified the group by study design and found that biomass smoke was a risk factor in both case-control and cross-sectional studies. However, we also observed a stronger risk association with biomass smoke pooled from case-control studies than pooled from cross-sectional studies. The reason may be the difference in duration of exposure to biomass smoke. The current spirometric classification for severity of COPD includes four stages: stage I, mild; stage II, moderate; stage III, severe; stage IV, very severe. The cross-sectional studies included patients in all four stages of COPD, whereas the case-control studies included fewer stage I patients. Thus, the patients in the case-control studies may have had a longer duration of biomass smoke exposure. Finally, with improvements in living standards, more and more people use liquefied petroleum gas as the cooking fuel, and the participants in some studies reported only their current use of cooking fuels, ventilating fans, and winter heating, rather than a combination of current and historical use, which may be another confounding factor.

COPD patients include three subpopulations, based on how the disease is diagnosed: emphysema, chronic bronchitis, and spirometry-diagnosed COPD. Our results showed that biomass smoke is a risk factor for developing chronic bronchitis (OR = 2.57) and COPD diagnosed according to lung function (OR = 2.77). According to epidemiology studies, about two-thirds of COPD patients have not been given this diagnosis. Using a history of ever having received a diagnosis of COPD, chronic bronchitis, or emphysema as a diagnostic criterion can lead to many COPD patients being included in the healthy control group. We did a sensitivity analysis including the three studies in which the diagnostic criterion was a history of having received a diagnosis of COPD, and showed that biomass smoke is still a risk factor for COPD. In addition, all the subgroup analyses showed that biomass smoke is a risk factor for COPD, except for the subgroup analysis that included only men, which showed no association between biomass smoke and COPD. However, we still believe that exposure to biomass smoke is a risk factor for COPD in men because this subgroup analysis was part of a sensitivity analysis and the main analysis showed a strong association between biomass smoke and COPD in men. The study by Chapman et al29 showed that the incidence of COPD decreased markedly after household coal stoves were improved, which also showed that biomass smoke is a risk factor for COPD.

There are several limitations that should be considered when interpreting our results. First, some pooled ORs were obtained from heterogeneous studies. Second, the studies included in our metaanalysis were case-control and cross-sectional studies, not cohort studies, in which biomass smoke is assessed after disease onset. Thus, biomass smoke information in these studies is likely to be less accurate and possibly influenced by recall bias. Third, we must consider the possibility of a publication bias involved in our analysis, given that we combined data only from published reports, and that we may have excluded important unpublished data that did not show results consistent with our findings, although we did our best to contact the researchers for data from unpublished or ongoing studies, and although our result showed that only one additional unpublished article could negate the results of the metaanalysis in women. Finally, in most studies included in our metaanalysis, exposure was assessed using crude proxies such as whether the households were using solid fuels. This has proved useful, but better quantifying exposure will be necessary for establishing exposure-response relationships and for quantifying health risks more precisely.

In conclusion, our metaanalysis suggests that biomass smoke is associated with an increase in the risk of COPD. Given the high prevalence of biomass smoke, especially in rural areas, the public health consequences of biomass smoke with regard to COPD are important and suggest that COPD incidence could be reduced by interventions targeting biomass smoke.

Author contributions:Dr Hu: contributed to research conception, the first draft of the study protocol, literature searches and selected studies, additional data extraction, data management, statistical analyses, and the final version of the report.

Dr Zhou: contributed to the first draft of the study protocol, literature searches and selected studies, statistical analyses, and the final version of the report.

Dr Tian: contributed to data management and the final version of the report.

Dr Yao: contributed to additional data extraction and the final version of the report.

Dr J. Li: contributed to data management and the final version of the report.

Dr B. Li: contributed to the final version of the report.

Dr Ran: contributed to research conception and the final version of the report.

Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Other contributions: We are grateful to Dr Xiao Song Ma (Medical College of ShenZhen University, China) Guangqiao Zeng (Guangzhou Institute of Respiratory Disease, Guangzhou Medical University, China), Dr Jian Wang (Guangzhou Institute of Respiratory Disease, Guangzhou Medical University, China), and Professor Jim Hu (Department of Laboratory Medicine and Pathobiology, University of Toronto, ON, Canada) for their assistance in linguistic considerations.

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Menezes AM, Jardim JR, Pérez-Padilla R, et al. Prevalence of chronic obstructive pulmonary disease and associated factors: the PLATINO Study in São Paulo, Brazil. Cad Saude Publica. 2005;215:1565-1573. [CrossRef] [PubMed]
 
Shrestha IL, Shrestha SL. Indoor air pollution from biomass fuels and respiratory health of the exposed population in Nepalese households. Int J Occup Environ Health. 2005;112:150-160. [PubMed]
 
Xu F, Yin X, Shen H, Xu Y, Ware RS, Owen N. Better understanding the influence of cigarette smoking and indoor air pollution on chronic obstructive pulmonary disease: a case-control study in Mainland China. Respirology. 2007;126:891-897. [CrossRef] [PubMed]
 
Sezer H, Akkurt I, Guler N, Marakoğlu K, Berk S. A case-control study on the effect of exposure to different substances on the development of COPD. Ann Epidemiol. 2006;161:59-62. [CrossRef] [PubMed]
 
Jindal SK. A field study on follow up at 10 years of prevalence of chronic obstructive pulmonary disease & peak expiratory flow rate. Indian J Med Res. 1993;98:20-26. [PubMed]
 
Moreira MA, Moraes MR, Silva DG, et al. Comparative study of respiratory symptoms and lung function alterations in patients with chronic obstructive pulmonary disease related to the exposure to wood and tobacco smoke. J Bras Pneumol. 2008;349:667-674. [CrossRef] [PubMed]
 
Díaz E, Bruce N, Pope D, et al. Lung function and symptoms among indigenous Mayan women exposed to high levels of indoor air pollution. Int J Tuberc Lung Dis. 2007;1112:1372-1379. [PubMed]
 
LeVan TD, Koh WP, Lee HP, Koh D, Yu MC, London SJ. Vapor, dust, and smoke exposure in relation to adult-onset asthma and chronic respiratory symptoms: the Singapore Chinese Health Study. Am J Epidemiol. 2006;16312:1118-1128. [CrossRef] [PubMed]
 
Chapman RS, He X, Blair AE, Lan Q. Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: retrospective cohort study. BMJ. 2005;3317524:1050. [CrossRef] [PubMed]
 
Gunen H, Hacievliyagil SS, Yetkin O, Gulbas G, Mutlu LC, Pehlivan E. Prevalence of COPD: first epidemiological study of a large region in Turkey. Eur J Intern Med. 2008;197:499-504. [CrossRef] [PubMed]
 
Ko FW, Woo J, Tam W, et al. Prevalence and risk factors of airflow obstruction in an elderly Chinese population. Eur Respir J. 2008;326:1472-1478. [CrossRef] [PubMed]
 
Yaksic MS, Tojo M, Cukier A, Stelmach R. Profile of a Brazilian population with severe chronic obstructive pulmonary disease. J Pneumol. 2003;292:64-68. [CrossRef]
 
Vandevoorde J, Verbanck S, Gijssels L, et al. Early detection of COPD: a case finding study in general practice. Respir Med. 2007;1013:525-530. [CrossRef] [PubMed]
 
Frank TL, Hazell ML, Linehan MF, Frank PI. The diagnostic accuracies of chronic obstructive pulmonary disease (COPD) in general practice: The results of the MAGIC (Manchester Airways Group Identifying COPD) study. Prim Care Respir J. 2006;155:286-293. [CrossRef] [PubMed]
 
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;2111:1539-1558. [CrossRef] [PubMed]
 
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;73:177-188. [CrossRef] [PubMed]
 
Petitti DB. Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine. 2000; New York, NY Oxford University Press Inc
 

Figures

Figure Jump LinkFigure 1. Studies about the association between exposure to biomass smoke and COPD identified in MEDLINE, EMBASE, and from the Latin American and Caribbean Literature in Health Sciences Database 1965-2008.Grahic Jump Location
Figure Jump LinkFigure 2. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass nonsmokers among the whole population.Grahic Jump Location
Figure Jump LinkFigure 3. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass non-smokers among women.Grahic Jump Location
Figure Jump LinkFigure 4. Odds ratios and 95% CIs for COPD comparing biomass smokers with biomass nonsmokers among the whole population, including the three articles in which the COPD diagnostic criterion is the history of having been given a diagnosis of COPD, chronic bronchitis, or emphysema.Grahic Jump Location
Figure Jump LinkFigure 5.  Begg funnel plot with pseudo-95% CIs of results of 15 studies7-21 that examined biomass smoke and COPD (A). Filled funnel plot with pseudo-95% CIs of results of 15 studies7-21 that examined biomass smoke and COPD (B). Log of OR represents the natural logarithm of the OR of individual studies; SE of Log of OR represents the standard error in the natural logarithm of the OR of individual studies. The transverse represents SE of Log of OR. The vertical axis represents Log of OR. A dot in the figure represents a study. The horizontal line represents the combined effect and the two slopes represent the 95% CIs of the combined effects. A box represents an unpublished study that would have to exist to negate the results of the metaanalysis. OR = odds ratio. Grahic Jump Location
Figure Jump LinkFigure 6.  Begg funnel plot with pseudo 95% confidence limits of results of 11 studies that examined biomass smoke and chronic obstructive pulmonary disease6,8,9,11,13,14,16-20 among women (A). Filled funnel plot with pseudo-95% CIs of results of 11 studies7-21 that examined biomass smoke and COPD among women (B). Log of odds ratio represents the natural logarithm of the OR of individual studies; se of Log of odds ratio represents the standard error in the natural logarithm of the OR of individual studies. The transverse represents se of Log of OR. The vertical axis represents Log of OR. A dot in the figure represents a study. The horizontal represents the combined effect and the two slopes represent the 95% CIs of the combined effects. A box represents an unpublished study that would have to exist to negate the results of the metaanalysis. Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Studies Included in the Metaanalysis

B = both cigarette smokers and nonsmokers; CB = chronic bronchitis (defined as cough or phlegm on most days for > 3 months per year for at least 2 consecutive years; F = female; FM = both female and male; GOLD = Global Initiative on Obstructive Lung Disease; LPG = liquid petroleum gas; N = nonsmoker.

Table Graphic Jump Location
Table 2 —Characteristics and Reasons for Exclusion of Studies Excluded From the Metaanalysis of Biomass Smoke and COPD

See Table 1 for expansion of abbreviations.

a 

This article was included in a subgroup analysis.

Table Graphic Jump Location
Table 3 —Association Between Biomass Smoke and COPD in Total and by Subgroup

See Table 1 for expansion of abbreviation.

a 

Analyzed with a random effect model.

b 

Analyzed with a fixed effect model.

Table Graphic Jump Location
Table 4 —Association Between Biomass Smoke and COPD in Total and by Subgroups When Including the Three Articles Whose Diagnostic Criterion Is a History of Ever-Diagnosed COPD, Chronic Bronchitis, or Emphysema

See Table 1 for expansion of abbreviation.

a 

Analyzed with a random effect.

b 

Analyzed with a fixed effect model.

Table Graphic Jump Location
Table 5 —Exposure-Response Relationships Between Biomass Smoke and COPD

H-y = hour-years (average number of hours of exposure to biomass smoke daily multiplied by the number of years of exposure to biomass smoke).

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Akhtar T, Ullah Z, Khan MH, Nazli R. Chronic bronchitis in women using solid biomass fuel in rural Peshawar, Pakistan. Chest. 2007;1325:1472-1475. [CrossRef] [PubMed]
 
Orozco-Levi M, Garcia-Aymerich J, Villar J, Ramírez-Sarmiento A, Antó JM, Gea J. Wood smoke exposure and risk of chronic obstructive pulmonary disease. Eur Respir J. 2006;273:542-546. [CrossRef] [PubMed]
 
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Albalak R, Frisancho AR, Keeler GJ. Domestic biomass fuel combustion and chronic bronchitis in two rural Bolivian villages. Thorax. 1999;5411:1004-1008. [CrossRef] [PubMed]
 
Menezes AM, Jardim JR, Pérez-Padilla R, et al. Prevalence of chronic obstructive pulmonary disease and associated factors: the PLATINO Study in São Paulo, Brazil. Cad Saude Publica. 2005;215:1565-1573. [CrossRef] [PubMed]
 
Shrestha IL, Shrestha SL. Indoor air pollution from biomass fuels and respiratory health of the exposed population in Nepalese households. Int J Occup Environ Health. 2005;112:150-160. [PubMed]
 
Xu F, Yin X, Shen H, Xu Y, Ware RS, Owen N. Better understanding the influence of cigarette smoking and indoor air pollution on chronic obstructive pulmonary disease: a case-control study in Mainland China. Respirology. 2007;126:891-897. [CrossRef] [PubMed]
 
Sezer H, Akkurt I, Guler N, Marakoğlu K, Berk S. A case-control study on the effect of exposure to different substances on the development of COPD. Ann Epidemiol. 2006;161:59-62. [CrossRef] [PubMed]
 
Jindal SK. A field study on follow up at 10 years of prevalence of chronic obstructive pulmonary disease & peak expiratory flow rate. Indian J Med Res. 1993;98:20-26. [PubMed]
 
Moreira MA, Moraes MR, Silva DG, et al. Comparative study of respiratory symptoms and lung function alterations in patients with chronic obstructive pulmonary disease related to the exposure to wood and tobacco smoke. J Bras Pneumol. 2008;349:667-674. [CrossRef] [PubMed]
 
Díaz E, Bruce N, Pope D, et al. Lung function and symptoms among indigenous Mayan women exposed to high levels of indoor air pollution. Int J Tuberc Lung Dis. 2007;1112:1372-1379. [PubMed]
 
LeVan TD, Koh WP, Lee HP, Koh D, Yu MC, London SJ. Vapor, dust, and smoke exposure in relation to adult-onset asthma and chronic respiratory symptoms: the Singapore Chinese Health Study. Am J Epidemiol. 2006;16312:1118-1128. [CrossRef] [PubMed]
 
Chapman RS, He X, Blair AE, Lan Q. Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: retrospective cohort study. BMJ. 2005;3317524:1050. [CrossRef] [PubMed]
 
Gunen H, Hacievliyagil SS, Yetkin O, Gulbas G, Mutlu LC, Pehlivan E. Prevalence of COPD: first epidemiological study of a large region in Turkey. Eur J Intern Med. 2008;197:499-504. [CrossRef] [PubMed]
 
Ko FW, Woo J, Tam W, et al. Prevalence and risk factors of airflow obstruction in an elderly Chinese population. Eur Respir J. 2008;326:1472-1478. [CrossRef] [PubMed]
 
Yaksic MS, Tojo M, Cukier A, Stelmach R. Profile of a Brazilian population with severe chronic obstructive pulmonary disease. J Pneumol. 2003;292:64-68. [CrossRef]
 
Vandevoorde J, Verbanck S, Gijssels L, et al. Early detection of COPD: a case finding study in general practice. Respir Med. 2007;1013:525-530. [CrossRef] [PubMed]
 
Frank TL, Hazell ML, Linehan MF, Frank PI. The diagnostic accuracies of chronic obstructive pulmonary disease (COPD) in general practice: The results of the MAGIC (Manchester Airways Group Identifying COPD) study. Prim Care Respir J. 2006;155:286-293. [CrossRef] [PubMed]
 
Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;2111:1539-1558. [CrossRef] [PubMed]
 
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;73:177-188. [CrossRef] [PubMed]
 
Petitti DB. Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine. 2000; New York, NY Oxford University Press Inc
 
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