The effect of exposure to biomass smoke on respiratory symptoms in adult rural and urban Nepalese populations
The effect of exposure to biomass smoke on respiratory symptoms in adult rural and urban Nepalese populations
Om P Kurmi 3
Sean Semple 2
Graham S Devereux 1
Santosh Gaihre 2
Kin Bong Hubert Lam 0
Steven Sadhra 0
Markus FC Steiner 2
Padam Simkhada 5
William CS Smith 4
Jon G Ayres 0
0 Institute of Occupational and Environmental Medicine, University of Birmingham , Birmingham B15 2TT , UK
1 Department of Child Health, Royal Aberdeen Children's Hospital, University of Aberdeen , Aberdeen AB25 2ZG , UK
2 Scottish Centre for Indoor Air, Division of Applied Health Sciences, School of Medicine, University of Aberdeen , Aberdeen AB24 3FX , UK
3 Clinical Trials Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford , Oxford OX3 7LF , UK
4 School of Medicine and Dentistry, University of Aberdeen , Aberdeen AB24 3FX , UK
5 School of Health and Related Research, University of Sheffield , Sheffield S10 2TN , UK
Background: Half of the world's population is exposed to household air pollution from biomass burning. This study aimed to assess the relationship between respiratory symptoms and biomass smoke exposure in rural and urban Nepal. Methods: A cross-sectional study of adults (16+ years) in a rural population (n = 846) exposed to biomass smoke and a non-exposed urban population (n = 802) in Nepal. A validated questionnaire was used along with measures of indoor air quality (PM2.5 and CO) and outdoor PM2.5. Results: Both men and women exposed to biomass smoke reported more respiratory symptoms compared to those exposed to clean fuel. Women exposed to biomass were more likely to complain of ever wheeze (32.0 % vs. 23.5%; p = 0.004) and breathlessness (17.8% vs. 12.0%, p = 0.017) compared to males with tobacco smoking being a major risk factor. Chronic cough was similar in both the biomass and non-biomass smoke exposed groups whereas chronic phlegm was reported less frequently by participants exposed to biomass smoke. Higher PM2.5 levels (≥2 SDs of the 24-hour mean) were associated with breathlessness (OR = 2.10, 95% CI 1.47, 2.99) and wheeze (1.76, 1.37, 2.26). Conclusions: The study suggests that while those exposed to biomass smoke had higher prevalence of respiratory symptoms, urban dwellers (who were exposed to higher ambient air pollution) were more at risk of having productive cough.
Respiratory symptoms; Breathlessness; Phlegm; Solid fuel; Household air pollution
While the major cause of respiratory health problems
among adults in the developed world is smoking, exposure
to particles generated from biomass smoke is a major
cause of respiratory diseases in low income countries [1,2].
Several studies have reported higher prevalence of
respiratory ill-health among adults exposed to biomass
smoke with estimated risk ratios between 1.2 and 7.9 .
Most studies showing an association between household
air pollution and respiratory health problems used proxy
measurements of exposure such as the number of hours
spent on cooking, or simply ever used particular fuels. In
addition, not all of the important confounders, particularly
socio-economic status, smoking, fuel types and age, have
been adjusted for in earlier studies. Although positive
associations between chronic bronchitis and household air
pollution have been reported, there were large variations
in the prevalence of different respiratory symptoms [4,5].
A meta-analysis  reported positive associations between
the use of solid fuels and both chronic obstructive
pulmonary disease (COPD) (OR = 2.80, 95% CI 1.85, 4.00) and
chronic bronchitis (OR = 2.32, 95% CI 1.92, 2.80) but also
highlighted considerable heterogeneity in design,
measurement, and sizes of effect estimates in studies from different
low and middle income countries. To date, only one study
from Nepal has reported a relationship between directly
measured household exposure and respiratory symptoms
although the exposure assessment was carried out only
during cooking .
This study aimed to investigate the relationship between
respiratory symptoms and lung function and direct
measures of exposure to emissions from biomass and
nonbiomass (particularly liquefied petroleum gas [LPG]) fuels in
rural and urban households in Nepal. We previously
reported a 20% prevalence of COPD in the biomass exposed
population compared to 11% in the urban population based
on spirometry data . In this report we focus on the
Design and study sample
This cross-sectional study was carried out between April
2006 and February 2007. The biomass-exposed population
(98.9% used wood) was sampled from two village
development committees (VDCs) in the Kathmandu Valley. Four
wards (out of nine) in each VDC were randomly selected
and all individuals in the selected wards aged ≥16 years
were eligible if they met the inclusion criteria (no doctor
diagnosed major respiratory or cardiovascular health
problems and agreement to 24-h continuous airborne
exposure monitoring in their homes). The non-exposed
population (98.4% used LPG) were selected from six
wards (from a total of 35) in the Kathmandu municipality:
three were selected randomly near the ring road and the
other three selected from 1–2 km inside the ring road.
Further details were published elsewhere [7,8]. The sample
size was based on lung function assuming a prevalence of
COPD of 10% in the non-exposed and 20% in the exposed
populations, the latter being twice the reported prevalence
for Nepalese populations .
An interviewer-administered questionnaire was used to
collect data on smoking, socio-economic status, literacy,
kitchen characteristics, cooking details, history of fuel use,
and respiratory symptoms. The questionnaire was
translated into Nepalese and back translated into English by an
independent translator and a pilot study was conducted to
identify issues of logistics and understanding. The study
protocol was approved by the Nepal Health Research
Council. Written, informed consent was obtained from all
Measurements of exposure
Levels of particulate matter with an aerodynamic
diameter <2.5 μm (PM2.5) were measured over a continuous
24-h period in most dwellings (n = 490) using
photometric devices (SidePak AM510 and DustTrak Model
8520, TSI Inc., Shoreview, MN, USA), from which mean
24-h PM2.5 (in μg/m ) was derived. We report here the
results from 442 households (206 biomass burning; 236
non-biomass burning) which had at least 20 h data.
Outdoor PM2.5 concentrations were measured in both rural
and urban areas on the veranda (for logistic and security
reasons) in 118 homes (46 biomass burning, 72
nonbiomass burning). Indoor 24-h carbon monoxide (CO)
concentrations (in ppm) were measured in 126 homes (40
biomass burning, 86 non-biomass burning) using HOBO
CO loggers (MicroDAQ, Contoocook, NH, USA). The
direct reading photometric instruments were calibrated using
data from co-located gravimetric samplers .
Assessment of respiratory outcomes
Respiratory symptoms were based on the Medical Research
Council (MRC) questionnaire and included cough, phlegm,
breathlessness, and wheezing/whistling. Breathlessness was
measured using the five-level modified MRC (mMRC)
dyspnoea scale . In this report we define breathlessness
as those falling into Grade 2 or above. Participants who
reported to have cough or bring up phlegm first thing in the
morning for at least three months each year were considered
to have chronic cough or phlegm, respectively. Chronic
bronchitis was defined as the presence of both chronic
cough and chronic phlegm. Participants also underwent
spirometry as reported elsewhere . For comparability with
previous studies, we defined airflow obstruction in two ways:
(i) forced expiratory volume in 1 s (FEV1) to forced vital
capacity (FVC) ratio less than the lower limit of normal
(LLN); or (ii) FEV1/FVC <0.70 .
Height and weight were measured using standard
protocols , from which body mass index (BMI) was
computed (in kg/m2). Participants were classified as non-,
exand current smokers, where the latter two categories had
smoked at least 20 packs of cigarettes or 360 g of tobacco
in a lifetime, or at least one cigarette per day or one cigar a
week for one year. We also collected information on
exposure to environmental tobacco smoke and current
occupation. We used monthly household income (in Nepalese
Rupees where 1 US$ ≈ 100 Rs) and educational level as
proxies for socio-economic status.
Statistical analyses were performed using STATA (version
12, College Station, TX, USA). Results for PM2.5 and CO
concentrations are expressed as geometric means and
geometric standard deviations unless indicated otherwise.
Mean indoor PM2.5 concentrations >2 standard deviations
(SD) of the arithmetic means over the entire sampling
window were also calculated from the real time exposure data
and are reported here to assess any associations between
dependent variables and maximal exposures (i.e. during
cooking). Baseline demographic characteristics were
compared between biomass and non-biomass exposed
participants separately for men and women by regression taking
into account the household clustering effect. Regression
models were constructed to evaluate the effect of pollutants
(biomass, exposure to PM2.5 and CO independently) on
respiratory symptoms. All known and potential confounders
(age, income, educational level, smoking status, and BMI)
were adjusted for to obtain regression coefficients (β) with
robust variance estimates to allow for household clustering.
Among 1648 participants (762 men and 886 women)
enrolled, 846 (51%) used biomass and 802 (49%) used
nonbiomass fuels (primarily LPG), respectively. The proportion
of current smokers, underweight, illiterate and those having
lower income were higher among biomass users (Table 1).
Around 35% of rural women had smoked at some point in
their lives compared to only 9% of urban dwellers.
The geometric mean (± geometric SD) 24-h indoor PM2.5
concentration in biomass using homes was significantly
greater than in non-biomass using homes (455 ± 2.4 vs.
101 ± 2.0 μg/m3, p <0.001), although there was no significant
difference in outdoor air pollution between biomass and
non-biomass using homes (129 ± 2.7 vs. 115 ± 2.5 μg/m3,
p = 0.249). PM2.5 measured concurrently on the veranda
and 100 m from five biomass burning houses showed
substantially higher concentrations (129 ± 1.5 μg/m3)
compared to the outdoor environment (7.4 ± 2.8 μg/m3). Mean
peak indoor PM2.5 (defined as >2 SD of the mean level over
the entire sampling window) was 1790 μg/m3 in homes
using biomass and 141 μg/m3 in non-biomass homes
(arithmetic means 2828 and 335 μg/m3, respectively).
The 24-h CO concentrations in kitchens using biomass
fuel were significantly higher than in non-biomass fuel
Table 1 Demographic data of 1648 Nepalese adult men and women according to household fuel type
Monthly household income (Rs*); mean (SD)
Height (cm); mean (SD)
Weight (kg); mean (SD)
Body mass index (kg/m2); n (%)
Educational level; n (%)
Undergraduate or higher
Up to 12 years of formal education
Up to 10 years of formal education
<10 years of formal education
Farmer; n (%)
Smoking status; n (%)
Age started smoking (years); n (%)
Environmental tobacco smoke exposure; n (%)
≥10 years of current fuel use; n (%)
*Nepalese Rupees (1 US$ ≈ 100 Rs).
homes (13.4 ± 2.2 vs. 2.0 ± 2.0 ppm, p <0.001). The levels of
PM2.5 and CO were much higher during cooking
particularly in those houses where biomass was used as cooking
fuel (Additional file 1: Figure S1).
In general symptom prevalence increased with age
(Additional file 2: Table S1). Table 2 presents age-adjusted
prevalence of breathlessness, wheeze, and chronic
bronchitic symptoms. Dsypnoea (mMRC ≥ Grade 2) and
wheezing (ever or on most days/nights) were more common
among biomass users compared to those who used
nonbiomass fuel (p <0.001), with age-adjusted prevalence of
dyspnoea being 17.8% (95% CI 14.1, 21.5%) among female
and 12.0% (8.9, 15.1%) among male biomass fuel users,
compared to 7.6% (5.1, 10.1%) and 2.5% (1.0, 4.1%) among
cleaner fuel users. Likewise, wheezy chest was
approximately three times more likely to be reported by those
using biomass fuel. On the other hand, male non-biomass
users reported significantly more chronic phlegm (12.9%;
95% CI 9.6, 16.3%) compared to biomass users (3.0%; 1.4,
4.7%, p <0.001). Such difference was not observed in
females. When restricting to non-smokers, all symptom
prevalence was lower, although not statistically
different from the entire sample. Dyspnoea and wheeze were
more prevalent among biomass users. In contrast the
prevalence of chronic phlegm was higher in non-biomass
users (p <0.001), both in males and in females (p = 0.041).
Adjusting for potential confounders, those using
biomass were associated with a significantly increased risk
in breathlessness and wheeze. The increase in risk for
dyspnoea was larger among men (OR = 7.88; 95% CI 2.84,
21.88) than among women (3.90; 2.00, 7.79), but the
opposite was true for wheeze (Table 3). There was a negative
association between biomass use and chronic phlegm
prevalence, although this was significant only in men
(OR = 0.21; 95% CI 0.09, 0.47). The magnitude of risk
estimate was smaller when other measures of indoor
pollutants (24-h mean PM2.5, PM2.5 >2 SD and CO)
were used. Whilst the level of PM2.5 was much lower
outdoors compared to indoors, there was a positive
relationship between outdoor PM2.5 and chronic phlegm
in both sexes, although neither reached statistical
significance. Restriction to non-smokers made no material
changes in the risk estimates, with statistical
significance disappeared in wheeze among males due to the
reduction in power (data not shown).
There was an inverse association between FEV1 and
dyspnoea, ever wheeze and chest illness in the last 12 months
in women and with chronic phlegm in men after adjusting
for height, age, education, BMI, income and smoking status
(Additional file 3: Table S2).
This study shows that the risk of reporting wheeze (ever and
on most days and nights) and dyspnoea (mMRC scale ≥2)
were significantly higher among those exposed to biomass
smoke, particularly in women. These respiratory symptoms
were also positively associated with quantitative measures of
PM2.5 greater than two standard deviation of the mean but
Table 2 Respiratory symptoms* in Nepalese adult men and women according to household fuel type
mMRC scale ≥ Grade 2
On most days/nights
Chronic cough and phlegm
mMRC scale ≥ Grade 2
On most days/nights
Chronic cough and phlegm
*Adjusted for age.
12.0 (8.9, 15.1)
23.5 (19.5, 27.4)
18.2 (14.5, 21.9)
8.6 (4.7, 12.5)
15.3 (10.1, 20.4)
10.9 (6.4, 15.4)
8.7 (5.6, 11.7)
12.9 (9.6, 16.3)
1.1 (−0.2, 2.4)
32.0 (28.0, 36.0)
25.7 (21.8, 29.6)
13.8 (9.8, 17.9)
23.8 (19.1, 28.6)
20.9 (16.4, 25.5)
10.3 (7.4, 13.2)
1.1 (−0.1, 2.3)
17.8 (14.1, 21.5)
7.6 (5.1, 10.1)
7.88 (2.84, 21.88)
3.10 (1.53, 6.31)
2.48 (1.28, 4.82)
2.03 (0.99, 4.16)
1.94 (1.05, 3.59)
1.11 (0.41, 3.00)
mMRC scale ≥ Grade 2 3.90 (2.00, 7.79) <0.001
Ever 4.62 (2.71, 7.87) <0.001
On most days and nights 3.55 (2.06, 6.13) <0.001
Chronic cough 0.41 (0.15, 1.18) 0.098
Chronic phlegm 0.42 (0.15, 1.20) 0.106
Chronic bronchitis 0.30 (0.08, 1.11) 0.071
FEV1/FVC < 0.70 1.30 (0.67, 2.54) 0.436
FEV1/FVC < LLN 1.67 (0.66, 4.23) 0.281
*Adjustments for age, sex, educational level, income, BMI, smoking status.
†OR for 10-fold increase in pollutant level.
1.61 (0.96, 2.69)
1.20 (0.65, 2.19)
0.81 (0.46, 1.45)
0.86 (0.30, 2.45)
1.37 (0.74, 2.53)
1.73 (1.13, 2.66)
1.53 (0.93, 2.51)
0.94 (0.53, 1.67)
1.26 (0.62, 2.58)
2.67 (1.50, 4.78)
1.72 (1.20, 2.46)
1.29 (0.87, 1.91)
1.80 (1.19, 2.74)
1.73 (1.25, 2.39)
1.58 (1.10, 2.27)
0.90 (0.57, 1.41)
1.45 (0.77, 2.74)
1.24 (0.16, 9.63)
3.06 (1.28, 7.31)
3.31 (1.35, 8.13)
3.13 (1.17, 8.32)
1.38 (0.57, 3.37)
1.41 (0.52, 3.80)
1.54 (0.54, 4.36)
0.88 (0.19, 4.06)
8.33 (0.74, 93.27)
1.28 (0.43, 3.83)
1.35 (0.34, 5.31)
1.54 (0.46, 5.19)
0.41 (0.20, 5.12)
0.95 (0.36, 2.56)
not to the 24-h average mean, suggesting that peaks of
pollution may be more important than average exposures.
Those exposed to biomass smoke who had respiratory
symptoms were more likely to have lower lung function
and the prevalence of airflow obstruction was significantly
higher amongst the older individuals.
All respiratory symptoms were self-reported without
further clinical assessment, which may have resulted in
misclassification. People in low-income countries often consider
wheeze, breathlessness and bringing up phlegm to some
extent as normal which may result in under-reporting of
symptoms and if this was differentially expressed between
exposed and non-exposed groups this may underestimate
the true risk. The respiratory questionnaire used was
developed and validated in developed countries and although our
version was back translated to ensure best delivery of
questions interpretative issues may have arisen. For instance,
there is no terminology in Nepali for the term “wheeze”
which could have caused some confusion among
interviewees but efforts were made to minimise these by using
bilingual speakers from their local communities trained in
The major strengths of this study are its size, the use of a
comparator group (studying biomass smoke exposed and
non-exposed groups) and the adjustment for confounders,
often inadequately dealt with in previous studies. We
included young adults (≥16 years) because in Nepal cooking
is usually delegated to adolescents, particularly girls and
those living in the rural areas.
Previous work has studied populations using different
types of biomass smoke for varying lengths of time making
comparisons difficult especially when the issues of
confounding is inconsistently addressed. Behera and Jindal 
reported prevalences of respiratory symptoms for different
types of fuel users (biomass, kerosene, LPG and mixed) in
Indian women and found that respiratory symptoms did
not follow any clear pattern: chronic bronchitis was greater
in biomass users, cough greater in kerosene users and
breathlessness in mixed fuel users. Other studies [12,13]
have not found an association between exposures to wood
smoke and respiratory symptoms but a randomised
controlled trial study in Guatemala  of respiratory
symptoms in women involved in cooking reported that women
provided with improved cook stoves reported significantly
less wheeze compared to baseline but with no significant
reduction in other respiratory symptoms.
Our study recorded significantly higher “ever wheeze” in
the rural compared to the urban area, similar to previous
reports from Nepal (wood smoke) [6,15], Canada [16,17]
(smoke from burning agricultural residue and wood),
India  (biomass smoke), China [18,19] (coal smoke)
and Guatemala  (wood smoke). The presence of
significantly higher prevalence of wheeze (ever, on most
days/nights, and in the last 12 months) in the rural,
lifelong non-smoking population further suggests that the risk
of being wheezy is likely to increase in populations exposed
to biomass smoke. The risk of ever wheeze in biomass
smoke exposed women was 60% higher compared to men
but there were no significant differences between urban
males and females. The risk of ever wheeziness increased
with age for the biomass exposed population but not for
the non-exposed population suggesting that prolonged
exposure to biomass smoke increases the risk although in
other studies respiratory symptoms have generally
increased with age. Ex-smokers showed more than a six-fold
increase in ever wheeze in the biomass smoke exposed
population and a three-fold increase in the urban population
compared to life-long non-smokers whereas the additional
risks in current smokers were nearly three and two fold
respectively. The higher risk in the ex-smokers could be
explained as the ex-smokers gave up smoking only after they
were medically diagnosed with respiratory problems which
might have persuaded them to quit smoking. Similar results
for ex- and current smokers were found for wheeze in the
last 12 months.
Self-reported chronic phlegm was significantly higher
in non-smoking, non-biomass exposed men (6.7%) and
women (4.9%) compared to the exposed group (men
2.7%, women 2.0%), contrary to the findings reported by
other studies in Nepal  and other countries [11,19].
This finding is both marked and surprising and while
this might be due to bias in reporting, the risk in the
urban population might be real. Most of the urban
dwellers were exposed to biomass at some point in their
early years and there might have been a residual effect of
that early pollutant exposure but this should not
overwhelm current exposure. This finding could also possibly
be due to the higher urban outdoor air pollution
concentrations from vehicle generated pollutants although other
causes unrelated to air quality such as post nasal discharge
may be a possibility. There have been abundant studies in
industrialised countries on the short and long term health
effects of vehicle generated ambient air pollution, with
special emphasis on respiratory and cardiovascular health
effects [21-23] showing positive correlations between
these health outcomes and concentrations of ambient air
pollutants. It is possible that vehicle generated particles
released in ambient air are more toxic in the context of
mucus hyper-secretion compared to biomass smoke and
thus cause more respiratory symptoms but future studies
are needed to assess the differential toxicity due to
different types of fuel [24,25] and also compare with existing
toxicity data from vehicle generated particles.
Unsurprisingly production of cough and phlegm was
more common in both current and ex-smokers and also
in older age groups. Phlegm production was greatest in
the biomass exposed illiterate population indicating that
socio-economic status is a risk factor and living within a
kitchen with no ventilation increased phlegm production
in both the rural and urban populations but not statistically
significantly so. This indicates that exposure to kitchen
fumes (smoke from fuel burning and also mist from
cooking oil when heated) might be a risk factor, again a
potential surrogate indicator of exposure.
Biomass smoke exposed men and women reported more
breathlessness compared to their non-exposed counterparts
but the difference was only significant for biomass smoked
compared to non-biomass exposed males. Recorded
breathlessness in this study is lower compared to other published
studies. As the biomass smoke exposed area was in a hilly
region, the population might have attributed breathlessness
to exertion rather than to inhalation of biomass smoke,
especially as females do a lot of manual work in the fields. It
is also possible that our sample is relatively young (mean age
35 years), hence less likely to have (and admit to have)
dyspnoea. Cooking with kerosene increased the risk of
developing breathlessness in urban dwellers (results not
shown) but this result was based on a very small number of
individuals and could just be a chance finding.
Direct measurement of exposure as mean 24-h indoor
PM2.5 in this study failed to show any significant
associations with respiratory symptoms but some of the proxy
measurements such as use of biomass, illiteracy and poor
ventilation (data not shown) showed a relationship
suggesting that exposure to biomass smoke might be a risk for
respiratory symptoms. Of interest is that when we considered
peak smoke exposures (taken as mean PM2.5 >2 SDs above
the 24-h mean) a relationship between respiratory
symptoms and exposures emerged, suggesting that time spent at
concentrations that are considerably higher than
background may be more important than consistently high
The odds of presence of airflow obstruction was greater
amongst those exposed to biomass although it did not
reach statistical significance. The higher prevalence of
respiratory symptoms such as wheeze and breathlessness
without having airflow obstruction could be due to the high
proportion of younger individuals in our sample.
Alternatively, it is possible that spirometry was not sensitive
enough to detect the very early stage of airflow obstruction.
In summary, in this study the prevalence of wheeze in a
biomass smoke exposed population is in line with most of
the previous findings in Nepal and other low-income
countries but the results for cough differ. This ambiguity
regarding the cough and phlegm results from urban Nepal
should be interpreted with special attention. Future studies
in urban Nepal looking at respiratory symptoms should be
looking at all the aspects like toxicity of outdoor pollutants
and whether any other risk factors are confounding the
Additional file 1: Figure S1. Typical temporal profiles of PM2.5 and CO
Additional file 2: Table S1. Respiratory symptoms in Nepalese adult
men and women according to household fuel type, stratifying for age in
Additional file 3: Table S2. Regression coefficients of lung function
indices using robust variance estimates.
BMI: Body mass index; CI: Confidence interval; CO: Carbon monoxide;
COPD: Chronic obstructive pulmonary disease; ECRHS: European community
respiratory health survey; FEV1: Forced expiratory volume in 1 second;
FVC: Forced vital capacity; LLN: Lower limit of normal; LPG: Liquefied
petroleum gas; MRC: Medical Research Council; OR: Odds ratio;
PM: Particulate matter; SD: Standard deviation; VDC: Village development
The authors declare that they have no competing interests.
OK is the guarantor of the paper, taking responsibility for the integrity of the
work as a whole, from inception to published articles. OK, SSemple, PS, WCS
and JA conceived and designed the study, interpreted results, and
contributed to authorship of the manuscript. OK, GSD, MS, KBHL and
SSadhra processed the data, contributed to the analysis plan and editing the
manuscript. SG helped in the data entry, cleaning and editing the
manuscript. All authors read and approved the final manuscript.
We would like to thank all the participants for taking part in this study. We are
extremely grateful to the local research staff (Kundan Kumar Jha, Naniram
Timalsina, Anita Dhungana, Bishnumaya Adhikar, Indra Kumar Bohara, Bhola
Dhungana, Pratichha Dali and Rajeev Shrestha) for their help in sampling and
Krishna Kunwar, Lava Dhungana, Pradip Raj Dali and Bigyan Kafle for their help
in the selection of the sampling locations and co-ordinating with the house
members. We are also grateful to George Henderson for his help with arranging
the sampling equipment.
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