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Background: Respiratory infections are among the leading causes of death and disability globally. Respirable aerosol particles released by agricultural crop-residue burning (ACRB), practised by farmers in all global regions, are potentially harmful to human health. Our objective was to estimate the health and economic costs of ACRB in northern India. Methods: The primary outcome was acute respiratory infection (ARI) from India’s fourth District Level Health Survey (DLHS-4). DLHS-4 data were merged with Moderate-Resolution Imaging Spectroradiometer satellite data on fire occurrence. Mutually adjusted generalized linear models were used to generate risk ratios for risk factors of ARI. Overall disease burden due to ACRB was estimated in terms of disability-adjusted life years. Results: Seeking medical treatment for ARI in the previous 2 weeks was reported by 5050 (2%) of 252 539 persons. Living in a district with intense ACRB—the top quintile of fires per day—was associated with a 3-fold higher risk of ARI (mutually adjusted risk ratio 2.99, 95% confidence interval 2.77 to 3.23) after adjustment for socio-demographic and household factors. Children under 5 years of age were particularly susceptible (3.65, 3.06 to 4.34 in this subgroup). Additional ARI risk factors included motor-vehicle congestion (1.96, 1.72 to 2.23), open drainage (1.91, 1.73 to 2.11), cooking with biomass (1.73, 1.58 to 1.90) and living in urban areas (1.35, 1.26 to 1.44). Eliminating ACRB would avert 149 thousand disability-adjusted life years lost per year, valued at US$1.529 billion over 5 years. Conclusions: Investments to stop crop burning and offer farmers alternative cropresidue disposal solutions are likely to improve population-level respiratory health and yield major economic returns.
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Environment, Green Space and Pollution
Risk of acute respiratory infection from crop
burning in India: estimating disease burden and
economic welfare from satellite and national
health survey data for 250 000 persons
Suman Chakrabarti,
1
Mohammed Tajuddin Khan,
2
Avinash Kishore,
3
Devesh Roy
4
and Samuel P Scott
5
*
1
Department of Global Health, University of Washington, Seattle, WA, USA,
2
Department of Economics,
Oklahoma State University, Stillwater, OK, USA,
3
South Asia Office, International Food Policy Research
Institute, New Delhi, India,
4
Agriculture for Nutrition and Health, International Food Policy Research
Institute, Washington, DC, USA and
5
Poverty Health and Nutrition Division, International Food Policy
Research Institute, Washington, DC, USA
*Corresponding author. Poverty Health and Nutrition Division, International Food Policy Research Institute, 1201 Eye St,
NW, Washington, DC 20005-3915, USA. E-mail: samuel.scott@cgiar.org
Editorial decision 30 January 2019; Accepted 14 February 2019
Abstract
Background: Respiratory infections are among the leading causes of death and disability
globally. Respirable aerosol particles released by agricultural crop-residue burning (ACRB),
practised by farmers in all global regions, are potentially harmful to human health. Our ob-
jective was to estimate the health and economic costs of ACRB in northern India.
Methods: The primary outcome was acute respiratory infection (ARI) from India’s fourth
District Level Health Survey (DLHS-4). DLHS-4 data were merged with Moderate-Resolution
Imaging Spectroradiometer satellite data on fire occurrence. Mutually adjusted generalized
linear models were used to generate risk ratios for risk factors of ARI. Overall disease
burden due to ACRB was estimated in terms of disability-adjusted life years.
Results: Seeking medical treatment for ARI in the previous 2 weeks was reported by 5050
(2%) of 252 539 persons. Living in a district with intense ACRB—the top quintile of fires
per day—was associated with a 3-fold higher risk of ARI (mutually adjusted risk ratio
2.99, 95% confidence interval 2.77 to 3.23) after adjustment for socio-demographic and
household factors. Children under 5 years of age were particularly susceptible (3.65, 3.06
to 4.34 in this subgroup). Additional ARI risk factors included motor-vehicle congestion
(1.96, 1.72 to 2.23), open drainage (1.91, 1.73 to 2.11), cooking with biomass (1.73, 1.58 to
1.90) and living in urban areas (1.35, 1.26 to 1.44). Eliminating ACRB would avert 149 thou-
sand disability-adjusted life years lost per year, valued at US$1.529 billion over 5 years.
Conclusions: Investments to stop crop burning and offer farmers alternative crop-
residue disposal solutions are likely to improve population-level respiratory health and
yield major economic returns.
V
CThe Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association . 1113
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International Journal of Epidemiology, 2019, 1113–1124
doi: 10.1093/ije/dyz022
Advance Access Publication Date: 28 February 2019
Original article
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Key words: Respiratory health, agriculture, disease burden, India, air pollution
Introduction
Respiratory infections are the most common chronic disease
of children globally, are a leading cause of death in develop-
ing countries and make a large contribution to the overall
burden of disease as measured by disability-adjusted life
years (DALYs) lost.
1,2
Air pollution is a recognized contrib-
utor to respiratory disease, as airborne fine particulate mat-
ter (PM
2.5
) from the burning of solid fuels, vehicle exhaust,
windblown soil, construction and other sources can pene-
trate deep into lung tissue, triggering an inflammatory cas-
cade and oxidative stress.
3
PM
2.5
exposure has been linked
to increased asthma-related emergency-room visits and hos-
pitalizations,
3
progression of carotid intima-medial thick-
ness,
4
greater chronic obstructive pulmonary disease
mortality
5
and reduced life expectancy.
4
In India in 2015,
exposure to outdoor air pollution was found to be the third
leading risk factor (after high blood pressure and high fast-
ing plasma glucose) contributing to mortality among 79
behavioural, environmental and metabolic factors.
6
A recent
study found that 12.5% of the total deaths in India in 2017
were attributable to air pollution.
7
Delhi was the state with
the highest annual population-weighted mean PM
2.5
,fol-
lowed by Uttar Pradesh, Bihar and Haryana in north India.
Whereas indoor air pollution due to burning of solid fuels in
poor states like Uttar Pradesh and Bihar were important fac-
tors, the ambient particulate-matter pollution was highest in
the north Indian states of Uttar Pradesh, Haryana, Delhi,
Punjab and Rajasthan.
7
Delhi, India’s large capital city and home to 25 million
residents, is experiencing a public-health emergency due to
high levels of air pollution. Delhi was the most polluted
large city in the world in 2016, with an average annual
PM
2.5
of 122 mgm
–3
(micrograms per cubic meter)—12
times the World Health Organization (WHO)’s recom-
mended target of 10 mgm
–3
.
8
Air pollution in Delhi is par-
ticularly extreme during the winter months—a period
when farmers in the neighbouring upwind states of
Haryana and Punjab—where the burden of outdoor air
pollution is also high
9
—practise agricultural crop-residue
burning (ACRB).
Banned in November 2015 by the National Green
Tribunal,
10
ACRB is still widely practised due to weak en-
forcement of the ban, political economy issues and lack of
alternatives to burning among poor farmers.
11
In Punjab
alone, an estimated 44–51 million metric tonnes of residue
are burned each year, with rice being the primary source.
12
Winds carry suspended particles hundreds of miles, gener-
ating a thick cloud of smog above northern India visible by
satellite. Recently, the contribution of ACRB in north-
western India to air pollution in Delhi has been quantified,
with estimates ranging between 7 and 78% of PM
2.5
enhancements during the burning season being attributable
to ACRB.
13
Moreover, previous work has shown that
ACRB results in an unrecoverable decrease in pulmonary
function among children aged 10–13 years.
14
Among dif-
ferent sources of outdoor air pollution, ACRB was respon-
sible for an estimated 66 200 deaths in 2015 in India.
6
In
addition to affecting human health, ACRB deteriorates soil
fertility, releases greenhouse gases that contribute to global
warming and results in the loss of biodiversity.
11
Key Messages
Burning of agricultural crop residue to clear fields is a major contributor to air pollution. When rice farmers in north-
western India burn their fields, fine particulate matter (PM
2.5
) concentrations in Delhi, the highly populated capital city
located downwind of burning areas, spike to about 20 times beyond the World Health Organization’s threshold for
safe air.
Our results suggest that living in areas where crop burning is intense—measured using daily satellite imaging data over a
5-month period—is associated with a 3-fold higher risk of acute respiratory infection—one of the leading global causes of
lost disability-adjusted life years. Children are particularly susceptible to the health effects of crop burning.
Solutions to eliminate crop burning exist but require further investments. We found that crop-burning abatement
would be highly cost-effective and, in northern India, would avert disability-adjusted life years equivalent to
US$1.529 billion over a 5-year period.
Reducing crop burning would benefit human health.
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The Indian government has demonstrated an interest in
combatting air pollution and respiratory illness but so far
has fallen short in addressing the air-quality crisis. India
was the first country to develop national targets aimed at
reducing deaths from non-communicable diseases (NCDs)
including respiratory illness, by 2025, following the
WHO’s Global Action Plan for the Prevention and Control
of NCDs 2013–2020.
15
However, a major barrier to
further political action against crop burning is the lack of
rigorous evidence linking this practice to health outcomes.
To our knowledge, there are no available estimates of the
economic costs and societal disease burden associated with
crop burning. Therefore, we sought to estimate the risk of
acute respiratory infection (ARI) attributable to crop burn-
ing, among other socio-demographic and household fac-
tors, and to quantify the costs to society of this practice.
Methods
Outcome variable
Data on ARI were obtained from the fourth round of
India’s District Level Household Survey (DLHS-4)
16
—a
national demographic health survey sampled to be repre-
sentative at district and state levels. Data were used from
households in three Indian states—Haryana (in northern
India), Andhra Pradesh and Tamil Nadu (both in southern
India)—interviewed between September 2013 and
February 2014, the two southern states serving as compa-
rators without ACRB. In DLHS-4, household heads were
asked whether any household member had suffered from
an illness in the previous 15 days. If the response was ‘yes’,
information on illness type was collected for affected per-
sons. For individuals who had reported symptoms of ARI
in the previous 15 days, a follow-up question was asked
about whether and where the individual received medical
treatment. Our primary outcome was seeking treatment
for ARI in the previous 15 days at a private or public medi-
cal facility among those who also reported ARI symptoms.
Explanatory variables
Data on ACRB were obtained from the National
Aeronautics and Space Administration (NASA) Moderate-
Resolution Imaging Spectroradiometer (MODIS) Terra
satellite Fire Information for Resource Management
System (FIRMS) database.
17
The MODIS fire locations
provide daily information on spatial and temporal fire dis-
tribution. Each hotspot/active fire detection represents the
centre of a 1-km
2
area. Using Global Positioning System
coordinates and Arc Geographic Information System soft-
ware (Environmental Systems Research Institute), we
mapped the fires to state district centroids and boundaries.
The number of fires recorded by MODIS were counted
and summed by district and day. Thus, the primary explan-
atory variable used in the study was the number of fires
recorded per district per day from 1 September 2013 to 28
February 2014—a timeframe selected to be inclusive of the
period before and after peak ACRB. Additional risk factors
from DLHS-4 included age, sex, urban or rural residence,
education in years, access to electricity, source of cooking
fuel (biomass or other), whether a water-purification
method was used, water-drainage infrastructure (open or
closed drain), number of rooms in the home and whether
the kitchen was inside the home. Motor-vehicle congestion
was measured using an index ranging from 0 to 1 for the
number of vehicles per square kilometre at the district
level, using data from India’s 2011 Census.
18
The index
was constructed as [(vehicle density in district J – minimum
vehicle density in sample)/vehicle density range]. A value
of 0 implies that the district has the lowest vehicle density
among all districts in the sample. A value of 1 is typical of
congested urban districts.
A secondary contributor to poor air quality in northern
India is the burning of firecrackers during the Hindu festi-
val of lights, Diwali, which occurs between mid-October
and mid-November.
19
In Delhi, the time around Diwali is
associated with sharp increases in concentrations of respi-
rable particulate matter, total suspended particulate mat-
ter, sulphur dioxide and surface ozone.
2022
Thus, we
examined the period after Diwali—3–10 November in
2013—as an additional ARI risk factor.
Data merging
Individual-level data from DLHS-4 were merged with
MODIS data on the number of fires, by district and survey
date. The merged data set was a high-frequency panel with
daily temporal and spatial variations on exposure to
ACRB at the district level. Since ARI was reported for the
previous 15 days, data were merged using a recall-adjusted
survey date (interview date minus 7 days). The final sample
size was 252 539 individuals.
Visualization of ACRB and ARI co-occurrence
MODIS data on the number of fires were mapped for
Haryana—a state known for endemic ACRB. Reported
treatment seeking for ARI and ACRB occurrence was plot-
ted on a shared timescale to determine whether the two in-
dependently observed time series moved in tandem during
the study period. Separate plots were constructed for
Haryana (high occurrence of ACRB) vs southern states
(low occurrence of ACRB).
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Estimating the association between ACRB
and ARI
Associations between risk factors and reported ARI were
assessed through unadjusted and mutually adjusted
generalized linear models (GLMs), as ARI satisfies the
epidemiologic ‘rare disease assumption’.
23
To test for the
association of ACRB with ARI, for individual i belonging
to household h in district s and survey date t, we ran GLM
models using Equation (1):
ARIihst ¼aþACRBstb1þXihstXþihst :(1)
In the unadjusted model, ACRBst and each risk factor in
Xihst were individually related to ARI, whereas, in the mutu-
ally adjusted model, all risk factors were considered simulta-
neously. ACRBst is the daily district-level exposure to intense
ACRB, Xihst is the set of risk factors that an individual was ex-
posed to and ihst is the individual-specific error term. The rel-
ative risk ratio b1 represents the ratio of the risk (probability)
of ARI in the exposed group to the risk of ARI in the reference
group.
24
Exposure to pollution from ACRB was deemed in-
tense if the household member was interviewed on a day
when MODIS detected 100 or more fires (the top quintile of
fires per day per district) in their district of residence.
Sensitivity analyses
To test the sensitivity of our estimates from Equation (1),
we ran regressions on sub-samples of young children
(under 5 years), the elderly (over 60 years) and place of
residence (rural vs urban). Using Equation (1), we also
estimated models using the natural logarithm of the num-
ber of fires per day in a district as a continuous explanatory
variable to obtain the marginal effect of a 1% increase in
daily fire occurrence.
Health and economic benefits to society from
crop burning
In estimating the disease burden attributable to ACRB, we
considered the total population in three Indian states likely
to be affected by this practice: Haryana, Punjab and Delhi.
These states were chosen based on previous ACRB stud-
ies
11,14,25
and from satellite images of the fires (Figure 1).
Maps showing district-level population density, population
of young children and urbanization in the studied states
can be found in Supplementary Figure 1, available as
Supplementary data at IJE online.
Four steps were taken to estimate the direct disease bur-
den attributable to ACRB and firecracker burning. First,
the total estimated number of cases of ARI that would be
averted if exposure to these risk factors were eliminated in
the population, or the population attributable fraction
(PAF), was calculated using the ‘punaf’ STATA routine.
26
Second, state-specific DALY rates attributable to ARIs
were obtained for Haryana, Punjab and Delhi from India
state-level Disease Burden Initiative Collaborators.
9
Third,
these DALY rates were multiplied by our estimated PAF
values for ACRB or firecrackers and by state population to
estimate total DALYs attributable to these two risk factors
in each state. For Punjab, to account for the higher inci-
dence of ACRB in this state relative to Haryana, the DALY
rates were apportioned based on the ratio of percentage
area burned in Punjab relative to Haryana (67% in Punjab
vs 29% in Haryana, i.e. 2.3 times).
11
Fourth, total DALYs
saved were converted to per-year economic benefits by
multiplying the state-specific per-capita gross domestic
product (GDP) by the total DALYs attributable to ACRB
and firecrackers.
27
For firecrackers, we also subtracted
US$1 billion (US$200 million per year over 5 years)—the
estimated loss in revenue from the sale of firecrackers dur-
ing Diwali.
28
Benefits were compounded for 5 years using
a 3% discount rate, as is standard practice when account-
ing for financial returns at a future date.
29
Results
Crop burning in Haryana during October and November
in 2013 was most intense in the northern districts and was
primarily practised in districts where paddy cultivation
was also practised (Figure 2).
Figure 1. Typical crop-residue-burning episode and affected geographi-
cal area. State borders are overlaid on a MODIS satellite image from 5
November 2012. Active fires are shown as dots (NASA 2012).
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The frequency of reported ARI symptoms in Haryana
closely paralleled the number of fires observed by the
MODIS satellite in this state, with ARI symptoms being
more frequently reported in urban than in rural areas
(Figure 3). In south Indian states (Andhra Pradesh and
Tamil Nadu), where ACRB is not practised and firecracker
burning during Diwali is much less prevalent, ARI fre-
quency and the number of fires were low.
In Haryana, 5.4% of surveyed individuals reported
suffering from ARI symptoms in the previous 15days,
whereas the reported ARI symptoms in southern states
were only 0.1% (Table 1). Among those who reported
suffering from ARI, 83% also reported receiving treat-
ment for ARI at a private or public medical facility.
Whereas high-intensity fire exposure was virtually absent
in south India, 17.5% of individuals in Haryana lived in a
district where 100 or more fires per day were observed by
satellite. In Haryana, compared with southern states,
more households cooked with biomass (15.3 vs 0.2%)
and drained water into an open drain (90.6 vs 70.4%)
and fewer households treated water before drinking (15.8
vs 26.9%).
Living in a district with intense ACRB was the leading
risk factor for ARI, with 100 fires per day in the district
being associated with a 3-fold higher risk of ARI in the mu-
tually adjusted model (adjusted risk ratio, 95% confidence
interval: 2.99, 2.77 to 3.23) (Figure 4). Other risk factors
included the week after Diwali (2.45, 2.21 to 2.72), being
less than 5 years of age (2.21, 2.17 to 2.61), living in a dis-
trict with high motor-vehicle congestion (1.96, 1.71 to
2.23), draining water into open drains (1.91, 1.73 to 2.11),
using biomass for cooking (1.73, 1.58 to 1.90), living in
Figure 3. Temporal association between incidence of agricultural crop-residue burning and acute respiratory infection among Indians. Dashed lines
in lower two panels indicate urban areas and solid lines indicate rural areas. Data on number of fires were sourced from NASA-MODIS-FIRMS.
17
Data
on reported ARI were sourced from Indian DLHS-4.
16
ARI, acute respiratory infection.
Figure 2. Average number of fires per day during October and November
2013 in districts in Haryana. Fire data from NASA-MODIS-FIRMS, 2013.
17
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Table 1. Summary statistics for Indians surveyed in three states between September 2013 and February 2014
Haryana Andhra Pradesh and Tamil Nadu All
N90 327 162 212 252 539
Outcome
a
Reported ARI in previous 2 weeks, % 5.4 0.1 2.0
N4832 224 5056
Treatment seeking by those who reported ARI
Treatment at a private facility 76.5 52.7 75.5
Treatment at a public facility 6.5 35.7 7.8
Treatment at home 3.5 3.1 3.4
No treatment 9.0 1.8 8.7
N90 327 162 212 252 539
Exposure
Days when district had 100 or more fires, % 17.5 0.0 6.3
1–7 days after Diwali, % 7.6 0.0 2.7
Covariates
Children less than 5 years of age, % 8.0 7.1 7.4
Adults 60 years and older, % 10.1 12.4 11.6
Females, % 46.8 51.5 49.8
Urban residence, % 41.5 42.5 42.1
Less than 5 years of education, % 47.9 50.8 49.7
Motor-vehicle index 0.2 0.1 0.1
Household has electricity, % 97.2 98.6 98.1
Cooks with biomass, %15.3 0.2 5.6
Treats water before drinking, % 15.8 26.9 23.0
Drains water into open drain, % 90.6 70.4 77.6
Fewer than two rooms in house, % 39.7 46.1 43.8
Kitchen is inside house, % 69.3 69.4 69.4
Data were taken from the fourth round of the District Level Household Survey (International Institute for Population Sciences 2015) other than days when the
district had 100 or more fires, which were derived from NASA-MODIS-FIRMS data (National Aeronautics and Space Administration).
a
Only individuals who reported ARI in the previous 2 weeks and sought treatment at a private or public medical facility were classified as having the outcome
of interest.
Figure 4. Risk of acute respiratory infection among multiple determinants. 95% confidence intervals are shown for unadjusted (grey) and mutually ad-
justed (black) risk ratios. The dashed vertical line at 1.0 on the x-axis indicates no risk difference between groups. The mutually adjusted model was
adjusted for all other factors shown. Data were taken from DLHS-4
16
other than living in intense crop-burning district (100 or more fires per day),
which was derived from NASA-MODIS-FIRMS data,
17
and vehicle density, which was derived from India Census 2011 data.
18
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urban areas (1.35, 1.26 to 1.44) and being 60 years or
older (1.14, 1.03 to 1.25). Protective factors included hav-
ing access to electricity (0.40, 0.35 to 0.46), treating water
before drinking (0.75, 0.69 to 0.82), having fewer than
two rooms in the house (0.87, 0.82 to 0.93) and being fe-
male (0.92, 0.86 to 0.97).
Analysis on subgroups (Table 2) showed that children
under 5 years old are at particularly high risk of ARI from
ACRB (3.65, 3.06 to 4.34) and open drainage (2.85, 2.14
to 3.80). The elderly were particularly susceptible to ARI
associated with firecracker burning (2.53, 1.84 to 3.49)
and cooking with biomass (1.92, 1.44 to 2.55). Individuals
residing in urban areas were at a higher risk of ARI from
ACRB and Diwali, whereas those in rural areas were at
greater risk of ARI from open drains and cooking with bio-
mass. Morever, children and elderly living in urban areas
were at a higher risk for ARI associated with ACRB than
those living in rural areas. When looking at sex differences
in risk factors of ARI, motor-vehicle congestion was a
stronger risk factor among females (2.18, 1.80 to 2.64)
than among males (1.78, 1.49 to 2.14). Similarly, cooking
with biomass was a greater risk factor for females (1.84,
1.61 to 2.12) compared with males (1.64, 1.44 to 1.86).
Results using a continous explanatory variable to measure
ACRB were similar (Supplementary Table 1, available as
Supplementary data at IJE online).
We estimated that 14.4% of all ARI cases were attribut-
able to ACRB and 6.6% are attributable to burning fire-
crackers in 2013 (Table 3). For Haryana, Punjab and Delhi
combined, the total number of DALYs averted from elimi-
nating ACRB was estimated to be 149 thousand years, val-
ued at US$1.529 billion for 5 years. DALYs averted by
eliminating firecrackers were estimated at 42 thosuand
years, valued at US$357 million for 5 years.
Discussion
Our study has three novel findings. First, exposure to
outdoor air pollution from intense ACRB. firecracker
burning and motor vehicles are three leading risk factors
for ARI in northern India. Second, children under 5 years
old are at high risk for ARI from ACRB, whereas other
leading household-level risk factors for ARI are exposure
to open drains, cooking with biomass and urban residence.
Third, the DALY benefits attributable to complete ACRB
and firecracker abatement in three states in northern
India are estimated at 149 and 42 thousand years, respec-
tively, valued at about US$1.9 billion cumulative for
5 years.
Apart from ACRB and firecrackers, open drainage and
the use of biomass for cooking fuel were associated with
higher ARI, whereas access to electricity and water
filtration were protective factors. We also found a small
protective effect of having fewer rooms in the house; this
may just reflect poverty and the fact that poor households
have lower healthcare-seeking practices and thus were not
classified as having ARI in our analyses. The coefficients
were in the expected direction
1,3,14,22,3039
and the relative
magnitude of the effects is important for policy focus.
Programmes and policies must simultaneously address in-
door and outdoor pollution, through a combination of
bans and agricultural subsidies; increasing access to im-
proved cooking fuels like liquefied petroleum gas, electrifi-
cation and promotion of induction cooking stoves; and the
construction of improved drainage systems for households.
In addition, behavioural-change communication cam-
paigns for the promotion of water treatment and reduction
of firecracker use are likely to strengthen macro-level poli-
cies and outcomes.
We found a relatively stronger effect of outdoor air pol-
lution (crop burning, firecrackers, motor vehicles) on ARI
compared with indoor air pollution (cooking with bio-
mass). Recently, a large-cluster randomized–controlled
trial in Malawi found no benefit of switching to cleaner
cooking stoves on child pneumonia.
40
The authors suggest
that daily exposure to pollution from other sources may
have negated the effect of the intervention, which only tar-
geted indoor pollution. There is a large ongoing effort by
the central Indian government to expand the use of liquid
petroleum gas for cooking to 80% of households by
2019
41
and it will be of interest to assess the impact of
these policy efforts on health outcomes in the next few
years.
In our subgroup analyses, we found that motor-vehicle
congestion was a larger risk factor for ARI in women com-
pared with men. This finding is in line with literature sug-
gesting that women may be more susceptible to pollutants
compared with men for biological and social reasons.
42,43
We also found that firecracker burning was a stronger risk
factor in urban compared with rural areas, but that open
drainage was a stronger risk factor in rural areas compared
with urban areas. These findings are as expected, given rel-
atively intense firecracker burning and less open drainage
in urban centres compared with rural areas.
Our findings are in line with previous investigations
reporting on exposure to environmental air pollution and
respiratory health. Wildfires, which are becoming increas-
ingly common with climate change, have been shown to
have a broad range of negative population-level health
impacts, including respiratory infection.
44
A few studies
have reported more frequent hospital visits for respiratory
infections following wildfires in the USA and Canada.
4548
A study in Australia showed that an increase in PM
10
of
10 lgm
–3
was associated with a 15% increase risk of
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Table 2. Relative risk models for acute respiratory infection on population subgroups
Children (<5 years) Elderly (>59.9 years) Rural Urban Females Males
ARR 95 % CI ARR 95 % CI ARR 95 % CI ARR 95 % CI ARR 95 % CI ARR 95 % CI
Intense ACRB, 0/1 3.646 [3.06, 4.34] 2.982 [2.34, 3.80] 2.913 [2.63, 3.22] 3.245 [2.88, 3.65] 3.076 [2.75, 3.45] 2.929 [2.64, 3.25]
Diwali week, 0/1 2.037 [1.56, 2.65] 2.532 [1.84, 3.49] 2.042 [1.76, 2.37] 2.949 [2.54, 3.42] 2.526 [2.16, 2.95] 2.397 [2.08, 2.76]
District vehicle index, 0–1 1.625 [1.16, 2.27] 1.807 [1.16, 2.81] 2.886 [2.45, 3.40] 1.002 [0.80, 1.26] 2.182 [1.80, 2.64] 1.784 [1.49, 2.14]
Exposed to open drain, 0/1 2.848 [2.14, 3.80] 1.636 [1.25, 2.13] 2.444 [2.09, 2.86] 1.558 [1.37, 1.77] 1.789 [1.56, 2.06] 2.025 [1.76, 2.33]
Cooks with biomass, 0/1 1.303 [1.03, 1.65] 1.918 [1.44, 2.55] 1.769 [1.59, 1.97] 1.216 [0.96, 1.54] 1.844 [1.61, 2.12] 1.639 [1.44, 1.86]
Urban residence, 0/1 1.511 [1.29, 1.76] 1.260 [1.03, 1.54] 1.423 [1.29, 1.56] 1.291 [1.18, 1.41]
Kitchen is inside house, 0/1 1.139 [0.95, 1.37] 1.156 [0.92, 1.45] 1.036 [0.94, 1.14] 0.990 [0.87, 1.12] 1.005 [0.90, 1.12] 1.049 [0.95, 1.16]
Female, 0/1 0.834 [0.72, 0.97] 0.901 [0.74, 1.09] 0.880 [0.81, 0.96] 0.956 [0.87, 1.05]
Fewer than two rooms in house, 0/1 0.768 [0.65, 0.91] 0.954 [0.78, 1.17] 0.890 [0.81, 0.97] 0.890 [0.80, 0.99] 0.849 [0.77, 0.94] 0.898 [0.82, 0.98]
Treats drinking water, 0/1 0.663 [0.54, 0.82] 0.775 [0.61, 0.99] 0.553 [0.47, 0.65] 0.868 [0.78, 0.96] 0.771 [0.68, 0.87] 0.739 [0.66, 0.83]
Has electricity, 0/1 0.348 [0.25, 0.49] 0.383 [0.26, 0.57] 0.409 [0.35, 0.48] 0.344 [0.27, 0.44] 0.422 [0.34, 0.52] 0.377 [0.31, 0.46]
Below 5 years of education, 0/1 0.793 [0.63, 0.99] 1.159 [1.05, 1.28] 1.040 [0.93, 1.16] 1.183 [1.07, 1.31] 1.036 [0.94, 1.14]
Children (<5 years), 0/1 2.152 [1.90, 2.43] 2.745 [2.39, 3.15] 2.128 [1.86, 2.43] 2.633 [2.32, 2.98]
Elderly (>59.9 years), 0/1 1.104 [0.97, 1.26] 1.175 [1.01, 1.36] 1.031 [0.89, 1.19] 1.233 [1.08, 1.41]
N18 760 29 282 146 163 106 376 125 866 126 673
ARR, mutually adjusted risk ratio, i.e. adjusted for all other factors shown; CI, confidence interval. Data were taken from DLHS-4 (International Institute for Population Sciences 2015) other than intense ACRB (days
when district had 100 or more fires), which was derived from NASA-MODIS-FIRMS data (National Aeronautics and Space Administration).
1120 International Journal of Epidemiology, 2019, Vol. 48, No. 4
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respiratory infection among indigenous people living in
dry areas with frequent vegetation fires.
49
Second-hand to-
bacco smoke has also been related to ARI. The odds of
hospital admission for ARI were 1.55 times higher in chil-
dren under 2 years of age who were exposed to second-
hand tobacco smoke compared with children who were
not.
50
Respiratory infections were found to be the single
greatest contributor to the global burden of disease attrib-
utable to second-hand smoke in children under 5 years of
age.
51
Others have looked at ambient particulate-matter
concentration (from multiple sources) as a predictor of re-
spiratory outcomes. A meta-analysis of four studies in
areas with relatively low average annual PM
2.5
concentra-
tions (12–25 lgm
–3
, compared with 153 lgm
–3
in Delhi
during the study period
52
) found a 12% increased risk of
ARI in children under 5 years of age per 10 lgm
–3
increase
in PM
2.5
.
53
A long-term study in the USA found that a
1-interquartile range increase in ozone predicted a 4%
increase in respiratory infection among children aged
0–4 years.
54
Given that most studies relating poor air qual-
ity to respiratory health come from developed countries
with much lower ambient concentrations of pollutants as
well as less poverty and better health services, direct com-
parison to the currently studied population in northern
India may be limited.
Our study has several strengths. First, to our knowl-
edge, this is the only study to systematically estimate the ef-
fect of exposure to ACRB on ARI in India. Second, we
leveraged two high-resolution data sets: crop-burning data
from MODIS and data on reported ARI among all age
groups of men and women in Haryana from DLHS-4,
which has a large sample size, is representative at the dis-
trict level and were collected when ACRB peaks. Third, we
accounted for a comprehensive set of risk factors in our
mutually adjusted statistical model, thus our study pro-
vides an important benchmark in examining the relative
contribution of these risk factors for ARI. Fourth, we pro-
vide the first available estimates of ACRB abatement in
terms of DALYs and US dollars.
Our study is not without limitations. First, the DLHS
survey was not conducted in Punjab—the state with the
highest incidence of ACRB in October—thus we could not
estimate the contribution of ACRB on ARI in that state.
Second, to our knowledge, high-frequency, high-resolution
data on particulate-matter concentration in the ambient air
of Haryana or Punjab are not available; consequently, we
could not estimate the dose–response function linking
ACRB and the resulting increase in air pollution to health
outcomes. Third, in our mutually adjusted models, we con-
trolled for other factors that may also increase outdoor air
pollution. However, it is possible that we missed some fac-
tors, such as a fall in the ambient temperature, which may
worsen the effect of air pollution on population health.
Fourth, the effects of firecracker burning were estimated
by specifying a dummy variable for the week after Diwali.
The independent effects of firecracker burning would
Table 3. Economic benefits of government actions to abate agricultural crop-residue burning and burning of firecrackers
Haryana Punjab Delhi Total
Row ACRB Firecrackers ACRB Firecrackers ACRB Firecrackers ACRB Firecrackers
1 DALY rates for ARI per 100,000
population
a
1311 1311 887 887 799 799
2 State population, millions
b
25.4 25.4 27.7 27.7 16.8 16.8
3 Proportion of ARI cases attributed
to risk factor
c
0.14 0.06 0.33 0.06 0.14 0.06 0.14 0.07
4 DALYs saved by eliminating risk
factor, thousand years
d
47.9 19.6 81.4 14.5 19.3 7.9 148.5 420
5 Per-capita state GDP, US$/person
e
2637.26 2637.26 1739.67 1739.67 4575.16 4575.16
6 Economic value of DALYs saved
per yr, US$ million/yr
f
126.2 41.7 141.6 15.2 88.2 26.1 356.0 83.1
7 Economic value of DALYs saved
over 5 yrs, US$million
g
542.0 179.1 607.9 65.4 378.7 112.2 1528.6 356.7
a
From Figure 4 in Dandona et al. (2017).
b
From Indian Population Census 2011 (Office of the Registrar General & Census Commissioner 2011).
c
From PUNAF after estimating Equation (1), with the PAR for Punjab multiplied by 2.3 to account for higher ACRB in Punjab relative to Haryana (Lohan
et al. 2018).
d
Row 1 Row 2 Row 3.
e
From NITI Aayog (National Institution for Transforming India 2017).
f
Row 4 Row 5. One billion in economic benefits subtracted due to losses faced by firecracker industry (KV Lakshmana 2017).
g
From Row 6 for 5 years disco unted at 3% per year.
DALY, disability-adjusted life years; GDP, gross domestic product.
International Journal of Epidemiology, 2019, Vol. 48, No. 4 1121
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ideally be estimated in the absence of ACRB to avoid con-
founding, but this was not possible due to co-occurrence of
these events. Therefore, our estimate of the contribution of
firecracker burning is likely biased. Finally, we note that
low variability in the district vehicle index in the urban
sample prevents us from being able to accurately examine
the effect of motor-vehicle density on ARI in cities, and
data at a higher spatial resolution would allow better risk
assessment in this case.
Conclusion
We found that living in an area where crop burning is prac-
tised was a leading risk factor for respiratory disease in
northern India. Whereas the total burden of diseases from
air pollution declined between 1990 and 2016 due to
efforts to reduce the burning of solid fuel for household
use, outdoor air pollution increased by 16.6%.
6
We found
that ACRB leads to over US$300 million losses in eco-
nomic value every year and a 3-fold risk of ARI to those
exposed in the general population. Any investments made
for the abatement of ACRB are likely to be highly favour-
able in terms of their return on investment, when com-
pared with other public-health interventions or
actions.
55,56
ACRB is a growing problem in India and in
other countries.
3,39,57,58
The promotion of sustainable eco-
nomic development should include investments to stop
crop burning.
Supplementary data
Supplementary data are available at IJE online.
Acknowledgements
We acknowledge the permission to use data and imagery from
LANCE FIRMS operated by the NASA/GSFC/Earth Science Data
and Information System (ESDIS). We thank Pramod Kumar Joshi
for his feedback on the manuscript prior to submission. This work
was supported by the Indian Council of Agricultural Research. The
funders had no role in study design, data collection and analysis, de-
cision to publish or preparation of the manuscript.
Conflict of interest: None declared.
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... Fire-originated and background PM 10 . Daily average fire-originated PM 10 concentrations ranged from 58.7 to 171.9 μg/m 3 across the eight provinces in UNT (mean: 106.5 μg/m 3 ), and the numbers of burning days ranged from 64 days (Lamphun) to 122 days (Lampang) (Fig. 1). The daily average background concentration Fig. 3 and Supplementary Figures (S2 and S3). ...
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... In the case of crop residue burning, most existing work focuses on air pollution at local and urban scales, highlighting the influence of agricultural fires on regional air quality 7,[9][10][11]14,16,20,21 . However, impacts of fire emissions are likely to extend over a much larger area due to dispersion and transport 1,4,11,16 . ...
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Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000–98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67–90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000–11,000) each year, valued at 3.2 (95% CI: 0.49–7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.
... Several scientists are actively involved in the environmental monitoring field due to their concern over this critical problem. In [1], the authors conducted a detailed review of acute respiratory infections due to burning from an Indian perspective using satellite and national-health survey data. The primary purpose of this study was to analyze economic and health-related costs in Northern India. ...
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A deterministic decision support system is developed for the assessment of various Indian cities based on the air quality parameters in this research. The present study shapes the assessment of cities as a multi-criteria decision making (MCDM) problem due to the involvement of many indicators. To solve the present assessment problem, an MCDM method, namely, Distance based approach (DBA) that mainly works on the Euclidean distance calculation for each city from the optimal point and ranks the cities on the basis of their calculated distances. The city scoring minimum distance value is ranked at top position and the city with the maximum distance value on the last position.
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Background: Air pollution is a major planetary health risk, with India estimated to have some of the worst levels globally. To inform action at subnational levels in India, we estimated the exposure to air pollution and its impact on deaths, disease burden, and life expectancy in every state of India in 2017. Methods: We estimated exposure to air pollution, including ambient particulate matter pollution, defined as the annual average gridded concentration of PM2.5, and household air pollution, defined as percentage of households using solid cooking fuels and the corresponding exposure to PM2.5, across the states of India using accessible data from multiple sources as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three Socio-demographic Index (SDI) levels as calculated by GBD 2017 on the basis of lag-distributed per-capita income, mean education in people aged 15 years or older, and total fertility rate in people younger than 25 years. We estimated deaths and disability-adjusted life-years (DALYs) attributable to air pollution exposure, on the basis of exposure-response relationships from the published literature, as assessed in GBD 2017; the proportion of total global air pollution DALYs in India; and what the life expectancy would have been in each state of India if air pollution levels had been less than the minimum level causing health loss. Findings: The annual population-weighted mean exposure to ambient particulate matter PM2·5 in India was 89·9 μg/m3 (95% uncertainty interval [UI] 67·0-112·0) in 2017. Most states, and 76·8% of the population of India, were exposed to annual population-weighted mean PM2·5 greater than 40 μg/m3, which is the limit recommended by the National Ambient Air Quality Standards in India. Delhi had the highest annual population-weighted mean PM2·5 in 2017, followed by Uttar Pradesh, Bihar, and Haryana in north India, all with mean values greater than 125 μg/m3. The proportion of population using solid fuels in India was 55·5% (54·8-56·2) in 2017, which exceeded 75% in the low SDI states of Bihar, Jharkhand, and Odisha. 1·24 million (1·09-1·39) deaths in India in 2017, which were 12·5% of the total deaths, were attributable to air pollution, including 0·67 million (0·55-0·79) from ambient particulate matter pollution and 0·48 million (0·39-0·58) from household air pollution. Of these deaths attributable to air pollution, 51·4% were in people younger than 70 years. India contributed 18·1% of the global population but had 26·2% of the global air pollution DALYs in 2017. The ambient particulate matter pollution DALY rate was highest in the north Indian states of Uttar Pradesh, Haryana, Delhi, Punjab, and Rajasthan, spread across the three SDI state groups, and the household air pollution DALY rate was highest in the low SDI states of Chhattisgarh, Rajasthan, Madhya Pradesh, and Assam in north and northeast India. We estimated that if the air pollution level in India were less than the minimum causing health loss, the average life expectancy in 2017 would have been higher by 1·7 years (1·6-1·9), with this increase exceeding 2 years in the north Indian states of Rajasthan, Uttar Pradesh, and Haryana. Interpretation: India has disproportionately high mortality and disease burden due to air pollution. This burden is generally highest in the low SDI states of north India. Reducing the substantial avoidable deaths and disease burden from this major environmental risk is dependent on rapid deployment of effective multisectoral policies throughout India that are commensurate with the magnitude of air pollution in each state. Funding: Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.
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In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.
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Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012–2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%–78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1–3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires.
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Background 18% of the world’s population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016. Methods Using all available data sources, the India State-level Disease Burden Initiative estimated burden (metrics were deaths, disability-adjusted life-years [DALYs], prevalence, incidence, and life expectancy) from 333 disease conditions and injuries and 84 risk factors for each state of India from 1990 to 2016 as part of GBD 2016. We divided the states of India into four epidemiological transition level (ETL) groups on the basis of the ratio of DALYs from communicable, maternal, neonatal, and nutritional diseases (CMNNDs) to those from non-communicable diseases (NCDs) and injuries combined in 2016. We assessed variations in the burden of diseases and risk factors between ETL state groups and between states to inform a more specific health-system response in the states and for India as a whole. Findings DALYs due to NCDs and injuries exceeded those due to CMNNDs in 2003 for India, but this transition had a range of 24 years for the four ETL state groups. The age-standardised DALY rate dropped by 36·2% in India from 1990 to 2016. The numbers of DALYs and DALY rates dropped substantially for most CMNNDs between 1990 and 2016 across all ETL groups, but rates of reduction for CMNNDs were slowest in the low ETL state group. By contrast, numbers of DALYs increased substantially for NCDs in all ETL state groups, and increased significantly for injuries in all ETL state groups except the highest. The all-age prevalence of most leading NCDs increased substantially in India from 1990 to 2016, and a modest decrease was recorded in the age-standardised NCD DALY rates. The major risk factors for NCDs, including high systolic blood pressure, high fasting plasma glucose, high total cholesterol, and high body-mass index, increased from 1990 to 2016, with generally higher levels in higher ETL states; ambient air pollution also increased and was highest in the low ETL group. The incidence rate of the leading causes of injuries also increased from 1990 to 2016. The five leading individual causes of DALYs in India in 2016 were ischaemic heart disease, chronic obstructive pulmonary disease, diarrhoeal diseases, lower respiratory infections, and cerebrovascular disease; and the five leading risk factors for DALYs in 2016 were child and maternal malnutrition, air pollution, dietary risks, high systolic blood pressure, and high fasting plasma glucose. Behind these broad trends many variations existed between the ETL state groups and between states within the ETL groups. Of the ten leading causes of disease burden in India in 2016, five causes had at least a five-times difference between the highest and lowest state-specific DALY rates for individual causes. Interpretation Per capita disease burden measured as DALY rate has dropped by about a third in India over the past 26 years. However, the magnitude and causes of disease burden and the risk factors vary greatly between the states. The change to dominance of NCDs and injuries over CMNNDs occurred about a quarter century apart in the four ETL state groups. Nevertheless, the burden of some of the leading CMNNDs continues to be very high, especially in the lowest ETL states. This comprehensive mapping of inequalities in disease burden and its causes across the states of India can be a crucial input for more specific health planning for each state as is envisioned by the Government of India’s premier think tank, the National Institution for Transforming India, and the National Health Policy 2017.
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Background The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2015 provides an up-to-date analysis of the burden of lower respiratory tract infections (LRIs) in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 25 years and shows how the burden of LRI has changed in people of all ages. Methods We estimated LRI mortality by age, sex, geography, and year using a modelling platform shared across most causes of death in the GBD 2015 study called the Cause of Death Ensemble model. We modelled LRI morbidity, including incidence and prevalence, using a meta-regression platform called DisMod-MR. We estimated aetiologies for LRI using two different counterfactual approaches, the first for viral pathogens, which incorporates the aetiology specific risk of LRI and the prevalence of the aetiology in LRI episodes, and the second for bacterial pathogens, which uses a vaccine-probe approach. We used the Socio-demographic Index, which is a summary indicator derived from measures of income per capita, educational attainment, and fertility, to assess trends in LRI-related mortality. The two leading risk factors for LRI disability-adjusted life-years (DALYs), childhood undernutrition and air pollution, were used in a decomposition analysis to establish the relative contribution of changes in LRI DALYs. Findings In 2015, we estimated that LRIs caused 2·74 million deaths (95% uncertainty interval [UI] 2·50 million to 2·86 million) and 103·0 million DALYs (95% UI 96·1 million to 109·1 million). LRIs have a disproportionate effect on children younger than 5 years, responsible for 704000 deaths (95% UI 651 000–763 000) and 60.6 million DALYs (95ÙI 56·0–65·6). Between 2005 and 2015, the number of deaths due to LRI decreased by 36·9% (95% UI 31·6 to 42·0) in children younger than 5 years, and by 3·2% (95% UI –0·4 to 6·9) in all ages. Pneumococcal pneumonia caused 55·4% of LRI deaths in all ages, totalling 1 517388 deaths (95% UI 857940–2 183791). Between 2005 and 2015, improvements in air pollution exposure were responsible for a 4·3% reduction in LRI DALYs and improvements in childhood undernutrition were responsible for an 8·9% reduction. Interpretation LRIs are the leading infectious cause of death and the fifth-leading cause of death overall; they are the second-leading cause of DALYs. At the global level, the burden of LRIs has decreased dramatically in the last 10 years in children younger than 5 years, although the burden in people older than 70 years has increased in many regions. LRI remains a largely preventable disease and cause of death, and continued efforts to decrease indoor and ambient air pollution, improve childhood nutrition, and scale up the use of the pneumococcal conjugate vaccine in children and adults will be essential in reducing the global burden of LRI.
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Background: WHO estimates exposure to air pollution from cooking with solid fuels is associated with over 4 million premature deaths worldwide every year including half a million children under the age of 5 years from pneumonia. We hypothesised that replacing open fires with cleaner burning biomass-fuelled cookstoves would reduce pneumonia incidence in young children. Methods: We did a community-level open cluster randomised controlled trial to compare the effects of a cleaner burning biomass-fuelled cookstove intervention to continuation of open fire cooking on pneumonia in children living in two rural districts, Chikhwawa and Karonga, of Malawi. Clusters were randomly allocated to intervention and control groups using a computer-generated randomisation schedule with stratification by site, distance from health centre, and size of cluster. Within clusters, households with a child under the age of 4·5 years were eligible. Intervention households received two biomass-fuelled cookstoves and a solar panel. The primary outcome was WHO Integrated Management of Childhood Illness (IMCI)-defined pneumonia episodes in children under 5 years of age. Efficacy and safety analyses were by intention to treat. The trial is registered with ISRCTN, number ISRCTN59448623. Findings: We enrolled 10 750 children from 8626 households across 150 clusters between Dec 9, 2013, and Feb 28, 2016. 10 543 children from 8470 households contributed 15 991 child-years of follow-up data to the intention-to-treat analysis. The IMCI pneumonia incidence rate in the intervention group was 15·76 (95% CI 14·89-16·63) per 100 child-years and in the control group 15·58 (95% CI 14·72-16·45) per 100 child-years, with an intervention versus control incidence rate ratio (IRR) of 1·01 (95% CI 0·91-1·13; p=0·80). Cooking-related serious adverse events (burns) were seen in 19 children; nine in the intervention and ten (one death) in the control group (IRR 0·91 [95% CI 0·37-2·23]; p=0·83). Interpretation: We found no evidence that an intervention comprising cleaner burning biomass-fuelled cookstoves reduced the risk of pneumonia in young children in rural Malawi. Effective strategies to reduce the adverse health effects of household air pollution are needed. Funding: Medical Research Council, UK Department for International Development, and Wellcome Trust.
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Summary Background 18% of the world’s population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016. Methods Using all available data sources, the India State-Level Disease Burden Initiative estimated burden (metrics were deaths, disability-adjusted life-years [DALYs], prevalence, incidence, and life expectancy) from 333 disease conditions and injuries and 84 risk factors for each state of India from 1990 to 2016 as part of GBD 2016. We divided the states of India into four epidemiological transition level (ETL) groups on the basis of the ratio of DALYs from communicable, maternal, neonatal, and nutritional diseases (CMNNDs) to those from non-communicable diseases (NCDs) and injuries combined in 2016. We assessed variations in the burden of diseases and risk factors between ETL state groups and between states to inform a more specific health-system response in the states and for India as a whole. Findings DALYs due to NCDs and injuries exceeded those due to CMNNDs in 2003 for India, but this transition had a range of 24 years for the four ETL state groups. The age-standardised DALY rate dropped by 36·2% in India from 1990 to 2016. The numbers of DALYs and DALY rates dropped substantially for most CMNNDs between 1990 and 2016 across all ETL groups, but rates of reduction for CMNNDs were slowest in the low ETL state group. By contrast, numbers of DALYs increased substantially for NCDs in all ETL state groups, and increased significantly for injuries in all ETL state groups except the highest. The all-age prevalence of most leading NCDs increased substantially in India from 1990 to 2016, and a modest decrease was recorded in the age-standardised NCD DALY rates. The major risk factors for NCDs, including high systolic blood pressure, high fasting plasma glucose, high total cholesterol, and high body-mass index, increased from 1990 to 2016, with generally higher levels in higher ETL states; ambient air pollution also increased and was highest in the low ETL group. The incidence rate of the leading causes of injuries also increased from 1990 to 2016. The five leading individual causes of DALYs in India in 2016 were ischaemic heart disease, chronic obstructive pulmonary disease, diarrhoeal diseases, lower respiratory infections, and cerebrovascular disease; and the five leading risk factors for DALYs in 2016 were child and maternal malnutrition, air pollution, dietary risks, high systolic blood pressure, and high fasting plasma glucose. Behind these broad trends many variations existed between the ETL state groups and between states within the ETL groups. Of the ten leading causes of disease burden in India in 2016, five causes had at least a five-times difference between the highest and lowest state-specific DALY rates for individual causes. Interpretation Per capita disease burden measured as DALY rate has dropped by about a third in India over the past 26 years. However, the magnitude and causes of disease burden and the risk factors vary greatly between the states. The change to dominance of NCDs and injuries over CMNNDs occurred about a quarter century apart in the four ETL state groups. Nevertheless, the burden of some of the leading CMNNDs continues to be very high, especially in the lowest ETL states. This comprehensive mapping of inequalities in disease burden and its causes across the states of India can be a crucial input for more specific health planning for each state as is envisioned by the Government of India’s premier think tank, the National Institution for Transforming India, and the National Health Policy 2017. Funding Bill & Melinda Gates Foundation; Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India; and World Bank Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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Disposal of paddy residue has turn out to be a huge problem in north-west Indian states, resulting farmers prefer to burn the residues in-situ. Paddy residue management is of utmost important as it contains plant nutrients and improves the soil-plant-atmospheric continuum. Burning biomass not only pollutes environment and results in loss of appreciable amount of plant essential nutrients. The objectives of the review paper is to access the amount of residue generation, its utilization in-situ and ex-situ, emphasize harmful effects of residue burning on human health, soil health and environment of north-west states of India specially in Punjab and Haryana. This paper also discusses the possible strategies, financial and socio-economic evaluation of the paddy residue management technologies and accentuates the assessment of range of potential policy instruments which would offer avenues for sustainable agriculture and environment. Timely availability of conservation agriculture (CA) machinery is of utmost significance to manage the paddy residues in-situ. Collection and transportation of voluminous mass of paddy residue is cumbersome, therefore, ex-situ residue management is still not an economically viable option. The agricultural waste opens vivid options for its versatile usage and is possible if residue is collected and managed properly. It is a prerequisite for surplus residues to be used for CA. There is an urge to create awareness among farming communities to incline them to understand importance of crop residues in CA for sustainability and resilience of Indian agriculture.
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Biomass burning (BB) is a significant air pollution source, with global, regional and local impacts on air quality, public health and climate. Worldwide an extensive range of studies has been conducted on almost all the aspects of BB, including its specific types, on quantification of emissions and on assessing its various impacts. China is one of the countries where the significance of BB has been recognized, and a lot of research efforts devoted to investigate it, however, so far no systematic reviews were conducted to synthesize the information which has been emerging. Therefore the aim of this work was to comprehensively review most of the studies published on this topic in China, including literature concerning field measurements, laboratory studies and the impacts of BB indoors and outdoors in China. In addition, this review provides insights into the role of wildfire and anthropogenic BB on air quality and health globally. Further, we attempted to provide a basis for formulation of policies and regulations by policy makers in China.