Literature DB >> 27597659

Socio-economic factors, gender and smoking as determinants of COPD in a low-income country of sub-Saharan Africa: FRESH AIR Uganda.

Frederik van Gemert1, Niels Chavannes2, Bruce Kirenga3, Rupert Jones4, Sian Williams5, Ioanna Tsiligianni1, Judith Vonk6, Janwillem Kocks1, Corina de Jong1, Thys van der Molen1.   

Abstract

In Uganda, biomass smoke seems to be the largest risk factor for the development of COPD, but socio-economic factors and gender may have a role. Therefore, more in-depth research is needed to understand the risk factors. The aim of this study was to investigate the impact of socio-economic factors and gender differences on the COPD prevalence in Uganda. The population comprised 588 randomly selected participants (>30 years) who previously completed the FRESH AIR Uganda study. In this post hoc analysis, the impact of several socio-economic characteristics, gender and smoking on the prevalence of COPD was assessed using a logistic regression model. The main risk factors associated with COPD were non-Bantu ethnicity (odds ratio (OR) 1.73, 95% confidence interval (CI) 1.06-2.82, P=0.030), biomass fuel use for heating (OR 1.76, 95% CI 1.03-3.00, P=0.038), former smoker (OR 1.87, 95% CI 0.97-3.60, P=0.063) and being unmarried (OR 0.087, 95% CI 0.93-2.95, P=0.087). A substantial difference in the prevalence of COPD was seen between the two ethnic groups: non-Bantu 20% and Bantu 12.9%. Additional analysis between these two groups showed significant differences in socio-economic circumstances: non-Bantu people smoked more (57.7% vs 10.7%), lived in tobacco-growing areas (72% vs 14.8%) and were less educated (28.5% vs 12.9% had no education). With regard to gender, men with COPD were unmarried (OR 3.09, 95% CI 1.25-7.61, P=0.015) and used more biomass fuel for heating (OR 2.15, 95% CI 1.02-4.54, P=0.045), and women with COPD were former smokers (OR 3.35, 95% CI 1.22-9.22, P=0.019). Only a few socio-economic factors (i.e., smoking, biomass fuel use for heating, marital status and non-Bantu ethnicity) have been found to be associated with COPD. This applied for gender differences as well (i.e., for men, marital status and biomass fuel for heating, and for women being a former smoker). More research is needed to clarify the complexity of the different risk factors.

Entities:  

Mesh:

Year:  2016        PMID: 27597659      PMCID: PMC5011937          DOI: 10.1038/npjpcrm.2016.50

Source DB:  PubMed          Journal:  NPJ Prim Care Respir Med        ISSN: 2055-1010            Impact factor:   2.871


Introduction

Chronic obstructive pulmonary disease (COPD) is a major health problem in low- and middle-income countries (LMICs).[1] In 2010, COPD was the fourth leading cause of death globally, and it was expected to be the third by 2030.[2,3] Unfortunately, the prediction has been overtaken by reality: at this moment, COPD is the third leading cause of mortality worldwide.[4,5] Approximately 90% of COPD deaths occur in LMICs.[6] Despite these high numbers, COPD is an unknown disease in most of the rural areas of sub-Saharan Africa, both in terms of public awareness and in public health planning. The people are unaware of the potential damage to respiratory and non-respiratory health caused by tobacco and biomass smoke.[7-9] Biomass fuel use is the third largest contributor to the global burden of disease.[10] Although the development of COPD is multifactorial, biomass smoke is probably the largest risk factor for COPD in LMICs.[11-13] Worldwide, around 3 billion people, most of them living in LMICs, rely on the use of open fires and burning of biomass fuels (wood, animal dung, crop residues, straw and charcoal) for cooking and heating in poorly ventilated conditions.[14] Solid fuel burning is incomplete and produces high levels of household air pollution with a range of more than 250 health-damaging pollutants, including carbon monoxide, nitrogen and sulphur oxides, as well as a variety of pollutants, irritants, carcinogens, co-carcinogens and free radicals.[12,13,15] Until recently, data on the prevalence of COPD, the risk factors and socio-economic determinants in LMICs were scarce.[9,16,17] In 2012, a prospective cross-sectional observational study (FRESH AIR Uganda) was conducted to assess the prevalence of COPD and its risk factors in a rural district of Uganda. Among adults above the age of 30 years, the prevalence of spirometry-based COPD was 16.2% (52.6% women), as defined according to the methods used in FRESH AIR Uganda.[18] The prevalence of COPD was remarkably high (39%) among adults aged 30–39 years, both for men (37%) and for women (40%). In addition to tobacco smoking, particularly by young men, >90% of the participants were exposed to smoke caused by biomass fuel use.[18] The FRESH AIR Uganda study was conducted in rural Masindi district (population 350,000) of Uganda, a low-income country with an average life expectancy of 52 years (men 48 and women 57).[19] Masindi district is one of the poorest districts of Uganda, where the poverty line ($1.25 a day) is consistently above 40%.[19] Poverty is known to be a risk factor for COPD, but the socio-economic factors that contribute to this are unclear, particularly in LMICs.[3,20] The socio-economic status (SES) is an important determinant of overall health status.[17] In contrast to poverty, which is often quantified as a minimum level of income to meet the basic needs of life, SES is defined as an individual overall position or standing, and it can be indicated by a compilation of measurements including income, as well as education, employment, location of residence, cooking tradition, biomass fuel use and housing.[21] Using the data of FRESH AIR Uganda, we performed a post hoc analysis to examine the association of socio-economic factors, gender and smoking with COPD.

Results

Patient characteristics

Of the 588 participants, 95 (16.2%) were classified as having spirometry-based COPD and 493 (83.8%) as non-COPD (Table 1). Wood was used as main domestic fuel by 558 (94.9%) participants; grass was used by 534 (91%) participants; and crop residues were used by 501 (85.2%) participants; they were applied to light the fire. With regard to their cooking place, 490 (83.3%) participants cooked in a separate building as kitchen.
Table 1

Comparison of symptoms between non-COPD and COPD participants and within gender

 non-COPD
COPD
P value
 TotalMenWomenTotalMenWomenTotalMenWomen
Population493 (83.8%)246 (49.9%)247 (50.1%)95 (16.2%)45 (47.4%)50 (52.6%)   
Age44.9 (13.5)44.9 (13.1)44.9 (14.0)46.6 (13.9)45.5 (11.5)47.7 (15.5)0.2580.7930.211
Cough89 (18.1%)40 (16.3%)49 (19.8%)29 (30.5%)15 (33.3%)14 (28.0%)0.0050.0070.198
Phlegm102 (20.7%)48 (19.5%)54 (21.9%)21 (22.1%)13 (28.9%)8 (16.0%)0.7560.1550.352
Wheeze32 (6.5%)16 (6.5%)16 (6.5%)16 (16.8%)9 (20.0%)7 (14.0%)0.0010.0030.070
          
Chest infections      0.3810.0070.541
 None50 (10.1%)30 (12.2%)20 (8.1%)9 (9.5%)4 (8.9%)5 (10.0%)   
 1–2 per year272 (55.2%)134 (54.5%)138 (55.9%)46 (48.4%)15 (33.3%)31 (62.0%)   
 >2 per year171 (34.7%)82 (33.3%)89 (36.0%)40 (42.1%)26 (57.8%)14 (28.0%)   

Data are N (%) or mean (s.d.).

Abbreviation: COPD, chronic obstructive pulmonary disease.

Participants with COPD coughed and wheezed more than those without COPD, particularly among men (all P values<0.007). There were no significant differences in age and gender between participants with and without COPD. More than 90% of the participants had at least one chest infection a year. More details are depicted in Table 1.

Socio-economic factors, ethnicity and tobacco smoking as determinants of COPD

Participants with COPD were more often active or former smokers compared with participants without COPD (P=0.046), they used more biomass fuel for heating (P=0.035) and they were more often of non-Bantu ethnicity (P=0.020); a trend was shown for being unmarried (P=0.055). No other significant differences in socio-economic factors were found between subjects with and without COPD (Table 2).
Table 2

Comparison of socio-economic and risk factors between non-COPD and COPD participants

 Totalnon-COPDCOPDP value
Gender   0.652
 Men291 (49.5%)246 (49.9%)45 (47.4%) 
 Women297 (50.5%)247 (50.1%)50 (52.6%) 
     
Unmarried   0.055
 Yes113 (19.2%)88 (17.8%)25 (26.3%) 
     
Biomass fuel use indoors   0.335
 Yes546 (92.9%)460 (93.3%)86 (90.5%) 
     
Biomass fuel use outdoors   0.704
 Yes544 (92.5%)457 (92.7%)87 (91.6%) 
     
Biomass fuel for heating   0.035
 Yes104 (17.7%)80 (16.2%)24 (25.5%) 
     
Smoking   0.046
 Active122 (20.7%)98 (19.9%)24 (25.3%) 
 Former87 (14.8%)67 (13.6%)20 (21.1%) 
 None379 (64.5%)328 (66.5%)51 (53.7%) 
     
Ethnicity   0.020
 Non-Bantu270 (45.9%)216 (43.8%)54 (56.8%) 
 Bantu318 (54.1%)277 (56.2%)41 (43.2%) 
     
Education   0.433
 None118 (20.1%)97 (19.7%)21 (22.1%) 
 Primary358 (60.9%)302 (61.3%)56 (58.9%) 
 Secondary91 (15.5%)74 (15.0%)17 (17.9%) 
 Tertiary21 (3.6%)20 (4.1%)1 (1.1%) 
     
Employment   0.585
 Farmers441 (75.0%)370 (75.1%)71 (74.7%) 
 Business43 (7.3%)37 (7.5%)6 (6.3%) 
 Teachers16 (2.7%)15 (3.0%)1 (1.1%) 
 Others69 (11.7%)57 (11.6%)12 (12.6%) 
 Unemployed19 (3.2%)14 (2.8%)5 (5.3%) 
     
Village tobacco-growing area   0.249
 Yes241 (41.0%)197 (40.0%)44 (46.3%) 
     
Sleeping area   0.711
 Same room as kitchen44 (7.5%)35 (7.1%)9 (9.5%) 
 Separate room54 (9.2%)45 (9.1%)9 (9.5%) 
 Separate house490 (83.3%)413 (83.8%)77 (81.1%) 

Data are N (%).

Abbreviation: COPD, chronic obstructive pulmonary disease.

In the multivariable logistic regression model on the presence of COPD (Table 3), significant associations were found with biomass fuel use for heating (odds ratio (OR) 1.76, 95% confidence interval (CI) 1.03–3.00, P=0.038) and non-Bantu ethnicity (OR 1.73, 95% CI 1.06–2.82, P=0.030). Borderline significant associations were found with being unmarried (OR 1.66, 95% CI 0.93–2.95, P=0.087) and being a former smoker (OR 1.87, 95% CI 0.97–3.60, P=0.063). No significant associations between the presence of COPD and educational level, employment, village in tobacco-growing areas, time cooking indoors or outdoors, sleeping area and cooking area were found.
Table 3

Results of multivariable analysis of risk factors for COPD for all subjects and stratified by gender

 All subjects
Men
Women
 OR (95% CI)P valueOR (95% CI)P valueOR (95% CI)P value
Age (years)1.01 (0.99–1.02)0.4851.01 (0.98–1.04)0.5591.00 (0.98–1.03)0.886
 
Gender
 Women1 (reference)     
 Men0.74 (0.44–1.26)0.269    
       
Unmarried
 No1 (reference) 1 (reference)   
 Yes1.66 (0.93–2.95)0.0873.09 (1.25–7.61)0.015  
       
Biomass fuel for heating
 No1 (reference) 1 (reference)   
 Yes1.76 (1.03–3.00)0.0382.15 (1.02–4.54)0.045  
       
Smoking
 Never1 (reference) 1 (reference) 1 (reference) 
 Active1.57 (0.84–2.92)0.1561.53 (0.68–3.45)0.3101.27 (0.40–4.04)0.692
 Former1.87 (0.97–3.60)0.0631.51 (0.61–3.72)0.3753.35 (1.22–9.22)0.019
       
Ethnicity
 Bantu1 (reference) 1 (reference)   
 non-Bantu1.73 (1.06–2.82)0.0301.94 (0.94–4.03)0.075  

Abbreviations: CI, confidence interval; OR, odds ratio.

Risk factors for COPD stratified by gender

The logistic regression models on the presence of COPD stratified by gender showed for men an association with being unmarried (OR 3.09, 95% CI 1.25–7.61, P=0.015) and biomass fuel use for heating (OR 2.15, 95% CI 1.02–4.54, P=0.045), and for women an association with being a former smoker (OR 3.35 (95% CI 1.22–9.22, P=0.019; Table 3)).

Cooking tradition as COPD determinant

There were no significant differences between non-COPD and COPD participants concerning the cooking data, including exposure to biomass smoke (hours per day and number of years), and sleeping area. There were similarly no significant differences between cooking data as risk factors and COPD prevalence.

Discussion

Main findings

The main risk factors associated with the COPD found during the FRESH AIR survey were being of non-Bantu ethnicity and biomass fuel use for heating; a trend was found with former smokers and marital status. An association with biomass fuel use for cooking, both indoors and outdoors, was not found, as almost everybody used biomass fuel for cooking. The other tested socio-economic factors also did not differ between subjects with and without COPD. Among men, a risk factor for COPD was being unmarried and the use of biomass fuel for heating; a borderline risk factor was being of non-Bantu ethnicity. Among women, being a former smoker was a risk factor for COPD.

Interpretation of findings in relation to previously published work

Participants with COPD were more often active or former smokers compared with participants without COPD and used more biomass fuel for heating (P values 0.046 and 0.035, respectively). This is well known from the literature.[3,12] However, it was striking to see in the multivariable analysis that former smoking, and not active smoking, was borderline associated with a higher prevalence of COPD. This was particularly true for women, whereas in men no association was found at all. These findings were not confirmed by other studies. A possible explanation for this could be the so-called ‘healthy smoker effect’.[22] This refers to the fact that people who quit smoking often do this motivated by smoking-related symptoms, leaving the less suffering and relatively healthy group still smoking. In our study, this was only seen in women. In Ugandan men, the decision to quit smoking is probably not related to health- or smoking-related symptoms, but it may be influenced by other factors (e.g., cultural factors) that need to be discovered. In addition, tobacco smoke potentiates the detrimental effects of biomass smoke;[11,23,24] active smokers, who are also exposed to biomass smoke, have an increasing risk of airflow obstruction.[25,26] Given that almost every participant in our study was exposed to biomass fuel used for cooking, this variable could not be investigated. However, the use of biomass fuel for heating was significantly associated with the prevalence of COPD, especially in men. This indicates that biomass fuel use is indeed an important risk factor for COPD. For men, being unmarried seemed to increase the risk of developing COPD. This could be explained by the fact that married men are less exposed to biomass fuel for cooking, as women in Uganda, and probably other countries of sub-Saharan Africa, have the responsibility for domestic cooking.[7,27] Although women were more exposed to biomass smoke (both number of years and hours per day), no association with COPD was found. However, during cooking, women and perhaps unmarried men have several periods of intense exposure to biomass smoke each day, particularly when fires are started or stirred.[7,27] More research is needed to understand the individual exposure to household air pollution, as the exposure is spatially and temporally highly variable.[28] For men, the biomass fuel exposure for heating increased the chance of COPD: the context of this finding is not clear yet. A substantial difference in the prevalence of COPD was seen between the two ethnic groups: the prevalence of COPD among non-Bantu people was 20% (20.3% men and 19.7% women) and among Bantu people it was 12.9% (10.5% men and 14.9% women). Interestingly, additional analyses showed substantial differences between the two ethnic groups in SES. Bantu refers to a primarily large and complex linguistic grouping of people in Africa. Their cultural pattern is extremely diverse and are the most prosperous. They occupy the southern and western parts of Uganda.[19,29] In general, non-Bantu people are the poorer ethnic group, and they inhabit a geographical area stretching semi-arid eastern and northern parts of Uganda.[19,29] Compared with the Bantu people, non-Bantu smoked more (57.7% vs 10.7%, P<0.001)), were less educated (no education 28.5% vs 12.9%, particularly women: 51.6% vs 17.1%, P<0.001) and lived more in tobacco-growing areas (72.0% vs 14.8%, P<0.001). After adjustment for these socio-economic factors in the multivariable model, the association between ethnicity and COPD remained significant, in contrast to the single socio-economic risk factors (tobacco smoking, education and living in tobacco-growing areas). An explanation for this could be that ethnicity was associated with a combination of all these socio-economic factors, and that this combination was more important than any single factor. As such, ethnicity could be seen as a variable indicating SES. However, other unmeasured factors, such as lifestyle, cultural or genetic factors, that differ between the ethnic groups could also explain this association between COPD and ethnicity. Further research is necessary to confirm this.

Strengths and limitations of this study

FRESH AIR Uganda was one of the first observational surveys on the prevalence of COPD performed in a rural area of sub-Saharan Africa. The survey used well-trained local healthcare workers and was performed in 30 villages, randomly selected with a probability proportional to their size.[18] The sample size was relatively small, but had enough power to detect differences in COPD prevalence between men and women in Masindi district. However, it was not powered to detect significant differences in COPD prevalence among other sub-groups (e.g., ethnicity or occupational groups). In addition, it was not possible to detect a difference in COPD prevalence associated with exposure to biomass smoke, as the exposure was almost uniform in this rural area.[18] Finally, this study was a post hoc analysis, and the results need caution with interpretation. Further, properly designed prospective studies are needed to confirm our findings.

Implications for future research, policy and practice

Tobacco smoking is known to be a major cause of COPD, but recent literature has shown that the use of biomass fuels for cooking and heating is an important risk factor as well, particularly in LMICs.[12,13] A person living in a rural area of sub-Saharan Africa is exposed to a variety of other risk factors for the development of COPD during all stages of life: perinatal factors (maternal exposure to biomass smoke or tobacco smoke, low birth weight and pre-term birth), childhood exposure (respiratory tract infections, exposure to biomass smoke, childhood asthma, second-hand smoking, occupational exposure, poor nutrition and kerosene-based lamps) and adult exposure (occupational exposure, agricultural smoke, exposure to biomass smoke, cigarette smoking, second-hand smoking, kerosene-based lamps and outdoor air pollution).[30-32] More information is needed to understand the full extent and influence of these risk factors. Low socio-economic circumstances such as poverty are associated with most of these risk factors, as well as poor access to healthcare, poor living conditions and water supply/sanitation. All these factors may cause health effects (intrauterine growth restriction, malnutrition, respiratory tract infections) and therefore increase the risk of developing COPD.[3,17,20,33-35] The influence of socio-economic factors is therefore very complex: more research is needed to identify these partly modifiable risk factors on the development of COPD.[17,34,35] The general lack of knowledge leads to failure to make simple steps in avoiding exposure to biomass smoke.[30,36] The nature of the communities also determines the health-seeking behaviour, both traditional (local herbs) and western (dispensaries and health centres), most of the time with a lack of successful results and not addressing the problem of exposure.[7] Reduction of tobacco smoking and exposure of biomass smoke, as well as second-hand tobacco smoke, smoke from kerosene lamps and occupational air pollution, are major controllable factors to tackle the burden of COPD. Public awareness and control of (household) environment are important steps in preventing respiratory and non-respiratory diseases.[36] More research is vital, with prospective studies and a larger sample size, to perform further comparisons among the sub-groups, and to understand the impact of COPD and other (non)-respiratory diseases.

Conclusions

The risk factors for the development of COPD in Masindi district of Uganda are complex. Although tobacco smoking remains an important cause of COPD, almost everybody in this district, and probably in many other rural areas of sub-Saharan Africa, is exposed to biomass smoke and other risk factors. Only a few socio-economic factors have been found to be significantly associated with COPD (biomass fuel use for heating and non-Bantu ethnicity); for others, a trend was found (former smoker and marital status). This applied for gender differences as well (i.e., marital status and biomass fuel for heating for men, and former smoker for women). Between the two ethnic groups of Masindi district in Uganda, a difference in the prevalence of COPD was found, which could possibly be explained by the combination of several unfavourable socio-economic circumstances in the non-Bantu people. Research is needed to elucidate the complexity of the different risk factors in the development of COPD. Any intervention to reduce the incidence of COPD must combine raising awareness about the damaging effects of biomass fuel use and tobacco smoking, with clean-cooking solutions and tobacco smoking cessation to support at-risk communities. Researchers, policymakers and government, stakeholders, health professionals and communities will have to work together to control the growing burden of COPD, and start prevention and intervention programmes.

Materials and methods

The FRESH AIR Uganda study

The intended sample size of the FRESH AIR Uganda study was 600 participants, determined to give an acceptable degree of reliability in estimating the prevalence of spirometry-based COPD.[37] Eventually, 588 randomly selected participants were asked about their living circumstances and exposure to risk factors.[18] A screening questionnaire assessed gender, tribal and ethnic origin, education, living conditions, occupation, biomass fuel use, tobacco smoking, symptoms, MRC dyspnoea score and chest infections.[18] A household air pollution questionnaire gave information on type and place of cook stoves, type of fuels, preparation of meals, time activity pattern and cooking during pregnancy. Both questionnaires were developed from different validated questionnaires, and were pre-tested and completed during a face-to-face interview.[18] Subsequently, to assess COPD prevalence, pre- and post-bronchodilator spirometry was performed by well-trained local healthcare workers.[18] The study used the lower limit of normal threshold—i.e., participants below the fifth percentile of the predicted FEV1/FVC ratio—as the defining criterion of COPD to avoid under-diagnosis in young participants and over-diagnosis in older participants.[38,39] The study was approved by the Makerere University School of Medicine Ethics Committee and the Uganda National Council for Science and Technology (HS 2012-1142). All participants signed an informed consent form, or in case of illiteracy thumb-printed and signed by the village leader. More details about the study participants, study procedure and COPD diagnosis of FRESH AIR Uganda are reported elsewhere.[18]

Socio-economic status, ethnicity and tobacco smoking

FRESH AIR Uganda measured the SES using education, employment, location of residence, exposure to household air pollution, cooking tradition, sleeping and cooking areas as variables. Furthermore, we also included variables such as gender, ethnicity and tobacco smoking to capture possible inequalities within communities. Ethnicity was defined as Bantu speaking versus non-Bantu speaking based on linguistic grouping of the 55 tribes living in Masindi district.[29,40,41]

Statistical methods

Demographic and socio-economic characteristics of subjects with and without COPD were compared using chi-square tests for categorical variables, Student’s t-test for normally distributed continuous variables and Mann–Whitney U-tests for non-normally distributed variables. The association between socio-economic factors and COPD was assessed using a multivariable logistic regression model adjusted for age, gender and smoking habits. Socio-economic factors were selected using backward selection, and the final model retained all socio-economic variables with a P value <0.1. The socio-economic factors tested were marital status, biomass fuel use for cooking (indoors and outdoors), biomass fuel use for heating, ethnicity, education, employment, village in tobacco-growing area and sleeping area. The analyses were also stratified for gender to assess risk factors for COPD in men and women separately. P values <0.05 were considered statistically significant, and P values of 0.05–0.10 were considered trends. We performed the statistical analyses with the Statistical Package for the Social Science SPSS 20 (IBM SPSS, New York, USA).

Funding

The International Primary Care Respiratory Group received an unrestricted grant from Mundipharma International to conduct the FRESH AIR Uganda survey.
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Authors:  Stephen S Lim; Theo Vos; Abraham D Flaxman; Goodarz Danaei; Kenji Shibuya; Heather Adair-Rohani; Markus Amann; H Ross Anderson; Kathryn G Andrews; Martin Aryee; Charles Atkinson; Loraine J Bacchus; Adil N Bahalim; Kalpana Balakrishnan; John Balmes; Suzanne Barker-Collo; Amanda Baxter; Michelle L Bell; Jed D Blore; Fiona Blyth; Carissa Bonner; Guilherme Borges; Rupert Bourne; Michel Boussinesq; Michael Brauer; Peter Brooks; Nigel G Bruce; Bert Brunekreef; Claire Bryan-Hancock; Chiara Bucello; Rachelle Buchbinder; Fiona Bull; Richard T Burnett; Tim E Byers; Bianca Calabria; Jonathan Carapetis; Emily Carnahan; Zoe Chafe; Fiona Charlson; Honglei Chen; Jian Shen Chen; Andrew Tai-Ann Cheng; Jennifer Christine Child; Aaron Cohen; K Ellicott Colson; Benjamin C Cowie; Sarah Darby; Susan Darling; Adrian Davis; Louisa Degenhardt; Frank Dentener; Don C Des Jarlais; Karen Devries; Mukesh Dherani; Eric L Ding; E Ray Dorsey; Tim Driscoll; Karen Edmond; Suad Eltahir Ali; Rebecca E Engell; Patricia J Erwin; Saman Fahimi; Gail Falder; Farshad Farzadfar; Alize Ferrari; Mariel M Finucane; Seth Flaxman; Francis Gerry R Fowkes; Greg Freedman; Michael K Freeman; Emmanuela Gakidou; Santu Ghosh; Edward Giovannucci; Gerhard Gmel; Kathryn Graham; Rebecca Grainger; Bridget Grant; David Gunnell; Hialy R Gutierrez; Wayne Hall; Hans W Hoek; Anthony Hogan; H Dean Hosgood; Damian Hoy; Howard Hu; Bryan J Hubbell; Sally J Hutchings; Sydney E Ibeanusi; Gemma L Jacklyn; Rashmi Jasrasaria; Jost B Jonas; Haidong Kan; John A Kanis; Nicholas Kassebaum; Norito Kawakami; Young-Ho Khang; Shahab Khatibzadeh; Jon-Paul Khoo; Cindy Kok; Francine Laden; Ratilal Lalloo; Qing Lan; Tim Lathlean; Janet L Leasher; James Leigh; Yang Li; John Kent Lin; Steven E Lipshultz; Stephanie London; Rafael Lozano; Yuan Lu; Joelle Mak; Reza Malekzadeh; Leslie Mallinger; Wagner Marcenes; Lyn March; Robin Marks; Randall Martin; Paul McGale; John McGrath; Sumi Mehta; George A Mensah; Tony R Merriman; Renata Micha; Catherine Michaud; Vinod Mishra; Khayriyyah Mohd Hanafiah; Ali A Mokdad; Lidia Morawska; Dariush Mozaffarian; Tasha Murphy; Mohsen Naghavi; Bruce Neal; Paul K Nelson; Joan Miquel Nolla; Rosana Norman; Casey Olives; Saad B Omer; Jessica Orchard; Richard Osborne; Bart Ostro; Andrew Page; Kiran D Pandey; Charles D H Parry; Erin Passmore; Jayadeep Patra; Neil Pearce; Pamela M Pelizzari; Max Petzold; Michael R Phillips; Dan Pope; C Arden Pope; John Powles; Mayuree Rao; Homie Razavi; Eva A Rehfuess; Jürgen T Rehm; Beate Ritz; Frederick P Rivara; Thomas Roberts; Carolyn Robinson; Jose A Rodriguez-Portales; Isabelle Romieu; Robin Room; Lisa C Rosenfeld; Ananya Roy; Lesley Rushton; Joshua A Salomon; Uchechukwu Sampson; Lidia Sanchez-Riera; Ella Sanman; Amir Sapkota; Soraya Seedat; Peilin Shi; Kevin Shield; Rupak Shivakoti; Gitanjali M Singh; David A Sleet; Emma Smith; Kirk R Smith; Nicolas J C Stapelberg; Kyle Steenland; Heidi Stöckl; Lars Jacob Stovner; Kurt Straif; Lahn Straney; George D Thurston; Jimmy H Tran; Rita Van Dingenen; Aaron van Donkelaar; J Lennert Veerman; Lakshmi Vijayakumar; Robert Weintraub; Myrna M Weissman; Richard A White; Harvey Whiteford; Steven T Wiersma; James D Wilkinson; Hywel C Williams; Warwick Williams; Nicholas Wilson; Anthony D Woolf; Paul Yip; Jan M Zielinski; Alan D Lopez; Christopher J L Murray; Majid Ezzati; Mohammad A AlMazroa; Ziad A Memish
Journal:  Lancet       Date:  2012-12-15       Impact factor: 79.321

8.  Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.

Authors: 
Journal:  Lancet       Date:  2014-12-18       Impact factor: 79.321

9.  Burden of disease in adults admitted to hospital in a rural region of coastal Kenya: an analysis of data from linked clinical and demographic surveillance systems.

Authors:  Anthony O Etyang; Kenneth Munge; Erick W Bunyasi; Lena Matata; Carolyne Ndila; Sailoki Kapesa; Maureen Owiti; Iqbal Khandwalla; Andrew J Brent; Benjamin Tsofa; Pamela Kabibu; Susan Morpeth; Evasius Bauni; Mark Otiende; John Ojal; Philip Ayieko; Maria D Knoll; Liam Smeeth; Thomas N Williams; Ulla K Griffiths; J Anthony G Scott
Journal:  Lancet Glob Health       Date:  2014-04       Impact factor: 26.763

Review 10.  Risk factors and early origins of chronic obstructive pulmonary disease.

Authors:  Dirkje S Postma; Andrew Bush; Maarten van den Berge
Journal:  Lancet       Date:  2014-08-11       Impact factor: 79.321

View more
  10 in total

1.  Urban-Rural Disparities in Chronic Obstructive Pulmonary Disease Management and Access in Uganda.

Authors:  Nicole M Robertson; Emily M Nagourney; Suzanne L Pollard; Trishul Siddharthan; Robert Kalyesubula; Pamela J Surkan; John R Hurst; William Checkley; Bruce J Kirenga
Journal:  Chronic Obstr Pulm Dis       Date:  2019-01-04

2.  Illness representations of chronic obstructive pulmonary disease (COPD) to inform health education strategies and research design-learning from rural Uganda.

Authors:  Emily M Nagourney; Nicole M Robertson; Natalie Rykiel; Trishul Siddharthan; Patricia Alupo; Marysol Encarnacion; Bruce J Kirenga; Robert Kalyesubula; Shumonta A Quaderi; John R Hurst; William Checkley; Suzanne L Pollard
Journal:  Health Educ Res       Date:  2020-08-01

3.  Obstructive lung disease and quality of life after cure of multi-drug-resistant tuberculosis in Uganda: a cross-sectional study.

Authors:  Edwin Nuwagira; Anna Stadelman; Joseph Baruch Baluku; Joshua Rhein; Pauline Byakika-Kibwika; Harriet Mayanja; Ken M Kunisaki
Journal:  Trop Med Health       Date:  2020-05-19

4.  Symptoms and functional limitations related to respiratory health and carbon monoxide poisoning in Tanzania: a cross sectional study.

Authors:  Thomas Zoller; Elirehema H Mfinanga; Tresphory B Zumba; Peter J Asilia; Edwin M Mutabazi; David Wimmersberger; Francis Mhimbira; Frederick Haraka; Klaus Reither
Journal:  Environ Health       Date:  2022-04-02       Impact factor: 5.984

5.  Risk for development of active tuberculosis in patients with chronic airway disease-a systematic review of evidence.

Authors:  Yohhei Hamada; Christopher J Fong; Andrew Copas; John R Hurst; Molebogeng X Rangaka
Journal:  Trans R Soc Trop Med Hyg       Date:  2022-05-02       Impact factor: 2.184

6.  Pharmacological Management of People Living with End-Stage Chronic Obstructive Pulmonary Disease.

Authors:  Victoria Dalgliesh; Hilary Pinnock
Journal:  Drugs Aging       Date:  2017-04       Impact factor: 3.923

7.  Household cooking fuel type and childhood anaemia in sub-Saharan Africa: analysis of cross-sectional surveys of 123, 186 children from 29 countries.

Authors:  Iddrisu Amadu; Abdul-Aziz Seidu; Abdul-Rahaman Afitiri; Bright Opoku Ahinkorah; Sanni Yaya
Journal:  BMJ Open       Date:  2021-07-20       Impact factor: 2.692

8.  Chronic obstructive pulmonary disease associated with biomass fuel use in women: a systematic review and meta-analysis.

Authors:  Adama Sana; Serge M A Somda; Nicolas Meda; Catherine Bouland
Journal:  BMJ Open Respir Res       Date:  2018-01-12

9.  Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis.

Authors:  Lisha Luo; Yunquan Zhang; Junfeng Jiang; Hanghang Luan; Chuanhua Yu; Peihong Nan; Bin Luo; Mao You
Journal:  Int J Environ Res Public Health       Date:  2018-10-01       Impact factor: 3.390

10.  Geographical Variation of COPD Mortality and Related Risk Factors in Jiading District, Shanghai.

Authors:  Qian Peng; Na Zhang; Hongjie Yu; Yueqin Shao; Ying Ji; Yaqing Jin; Peisong Zhong; Yiying Zhang; Honglin Jiang; Chunlin Li; Ying Shi; Yingyan Zheng; Ying Xiong; Zhengzhong Wang; Feng Jiang; Yue Chen; Qingwu Jiang; Yibiao Zhou
Journal:  Front Public Health       Date:  2021-02-03
  10 in total

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