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\begin{document}

\title{Lecture notes on health effects of air pollution}



\author[1]{Arindam Basu}%
\affil[1]{University of Canterbury}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\subsection*{Air Pollution and health}

{\label{589217}}

The quality of air we breathe vary between the times of the day (morning
versus evening), where we are (indoors or outdoors, whether we are close
to a high street as opposed to live in a rural household), the type of
fuel we burn, and overall activities of the industries and motor
vehicles. On an average, every day, and every year, these issues will
determine the quality of air we breathe and the the resulting health
effects.

Air pollution refers to contamination of air; such contamination results
from:

\begin{enumerate}
\tightlist
\item
  Human activities
\item
  Burning of fossil fuels
\item
  Interaction of sunlight with exhausts from motor vehicles which lead
  to formation of tropospheric ozone
\item
  Industrial activities a number fossil fuel combustion and other
  sources.
\end{enumerate}

Ambient Air Quality refers to the quality of air in outdoors that most
people would breathe. When we breathe air, the air passes through our
upper and lower respiratory tracts and components in the volume of air
enters our alveolar spaces. Our respiratory tract begins in our nostrils
and end in alveoli. The trachea bifurcates into bronchi and the bronchi
in turn into bronchioles - which in turn undergo several rounds of
further divisions till it reaches the stage of respiratory bronchioles
and alveoli -- the ballon like spaces surrounded by lung parenchyma.
When we breathe (inhale), we draw in air that reaches the depth of
alveoli. Some components in the air get trapped in the upper reaches of
the respiratory tract, the others cross the blood alveoli barriers in
the parenchyma of lungs and carried by blood stream, they reach the
other organs. //insert figure here of the anatomy of the lungs The air
we breathe is in the lowest layer of the atmosphere (atmosphere has
three layers - the troposphere -- where we are, the mesosphere, and the
stratosphere). While there is a continuity in the three layers, when we
discuss health effects of air pollution, it is the troposphere that we
are most concerned. Hence, ambient air quality is the quality of the air
that we can determine by measuring and calibrating the following
components:

\begin{enumerate}
\tightlist
\item
  Carbon dioxide
\item
  Ozone (tropospheric ozone, stratospheric ozone is different)
\item
  Oxides of nitrogen
\item
  Oxides of sulphur
\item
  Particulate matter of various sizes, from 10 um in diameter to smaller
  particulates (these particulates can arise from different combustion
  sources, say petrol, during wood fires, metals, diesel and other
  combustion products)
\end{enumerate}

The following table shows how they should be distributed in a litre of
air. Note that this distribution has limits on time when they are
observed. This suggests that the composition of the air varies from time
to time, and if we can take care of the emissions, it is possible for us
to influence the air quality. Therefore, if we find that the air quality
over a particular point in time has worsened, we can take measures to
improve the quality of air. Hence, when we consider health effects of
air pollution, we need to be mindful of the fact that these health
effects can be transient, or transitory, but also can reflect longer
term health effects as a result of exposure to air. Another point is
that, we breathe air under different circumstances, different volumes
throughout the day. When we exercise in the open or run in a park, our
rates of respiration may increase and our volume of intake of air will
be higher than when we are sleeping. Again, depending on our activities
and patterns of activities, our exposure to air pollutants will vary.
Hence, unlike say exposure to water or other contaminants, this makes it
difficult to measure exposure to air. Finally, when we inhale air, we
inhale a number of different chemical contaminants all at the same time
in the form of a mixture. While there have been studies that have
investigated the role of say PM10, or PM2.5, it does not mean that they
were able to isolate these particulate matters in some way and then
measure the health effects, they had to measure it in the form of a
mixture with the other components. Whether these components biologically
interact with these other components and in what ways would be open to
more investigations. Scientists measure air quality using different
types of samplers and tools to indicate the contents and contaminants.
One way to measure air quality would be to collect black soot on filter
papers and then weigh them. This would provide an estimate of the
visibility due to black carbon or elemental carbon. This would mean that
activities such as motor vehicles and industrial processes that would
generate high concentrations of carbon particulates in the air would be
captured in this process and we'd have an idea of the level of
pollutants in the air. These in turn can then the be associated with
health effects that we'd observe in the populations that were exposed to
them. Health effects due to air pollution can arise in two ways: short
term health effects and longer term health effects. Shorter term health
effects are studied using time series analyses, case control studies, or
case crossover studies. Longer term health effects are studied using
case control studies and cohort studies. In time series analyses, we
obtain information about air quality parameters using PM10, or PM2.5
from administrative databases for a number of years, or months. These
values vary over days to months, and seasons to seasons. These are
smoothed to identify patterns of their occurrence, that is, the rates
are rising or falling. Corresponding to these changes, we also chart
certain health effects, say admission to the hospitals for Asthma
symptoms, or heart disease, or total deaths. These admission patterns
may also follow a pattern, increasing or rising in some seasons and
dropping in other seasons. Then these two patterns are correlated with
each other. For example, xxxx et.al. (xxxx) conducted a study in
Christchurch on the association between air quality (measured by PM10
concentration) and overall mortality and hospital admissions due to
asthma. //insert their graph here and discuss Time series analyses
provide us with a window, so we know that if people are exposed to air
quality, then the hospitalisations or mortality will occur over a short
period of time. This short gap of time is referred to as ``lag period''.
The advantage of studying health effects of air pollution using this
strategy is that, we have to use very little resource in terms of time
and money to conduct this study and we can get insights into an
association between an exposure such as air quality which is routinely
measured every day and some health outcomes. While this is an
inexpensive way to measure the association, it does not in any way
definitive or good evidence of cause and effect, as we cannot
extrapolate the findings of these studies to imply or predict what will
happen to individuals if they were exposed to air pollution under such
circumstances as well. This is referred to as ecological fallacy that we
cannot extrapolate findings from a study where data are analysed in an
aggregated manner to imply findings for individuals. Case crossover
study is another way to study health effects due to climate change or
short exposure health events. In case crossover study, we take
``events'' or ``time periods'' as cases and controls. It is a spin on
the case control study design. So, if we are interested to study
association between air pollution and say something like mortality for
elderly people, then we can take a case period for a certain year (say
five days following the Guy Fawkes day in New Zealand in 2016). This is
the ``case period'' and we count the number of people above the age of
65 years who are admitted to the Emergency department with signs and
symptoms of heart attack. We also take a ``control'' time period (say
something like a month ago, when these people did not have any sign or
symptom of heart attack, we assume all these people had first time heart
attacks) that should be as similar as the case time period in terms of
season and duration; now we compare the odds of high air pollution or
say PM10 count in between these two time periods, and on this basis test
the hypothesis that PM10 is a risk factor for heart disease. For
example, XXX et.al. (xxxx) conducted a case crossover study design to
test the association between PM10 concentration and asthma related
hospitalisation in YYYY. \ldots{} Other types of study designs that have
been used for studying health effects of air pollution are case control
study designs or cohort studies. Cohort studies are particularly useful
for studying longer term health effects. For example, one can use proxy
measures for air pollution if one studies people over a long period of
time who have lived close to a high traffic street (say in houses that
are about 100 metres away from a heavy traffic zone) and compared health
outcomes for some diseases such as heart diseases or asthma related
events for individuals who have stayed in places where there is little
scope of heavy air pollution say in rural neighbourhoods away from a
heavy traffic highway. Comparison of the health outcomes among these two
groups of people would provide insights into the association between air
pollution and health effects in the longer term. For example, xxx et.al
(xxxx) conducted a cohort study where they studied \ldots{}. Air
pollutants are associated with a range of health effects. These include:
// insert the table here to show the pollutants and health effects We
think of a number of mechanisms that can explain the health effects of
air pollution. Some health effects are direct irrigation. For example,
immediately when we enter a region of heavy air pollution, we tend to
feel choked, or we feel irritation in the back of the throat or a
burning sensation. This is attribute to direct irritation of the mucous
membranes in our throat and upper respiratory passages as they come in
contact with particulate matters or irritant molecules present in the
air we breathe. Other health effects are more remote: these are caused
by physiological or pathological mechanisms associated with particulate
matters that enter the body through our respiratory systems. Heart and
lungs are the organs that are most affected by air pollution (remote
effects). We can identify three mechanisms proposed for heart and lung
related diseases: altered autonomic nervous system activity with
increased activity for sympathetic nervous system, increased
inflammation both local and general, and increased vasoconstriction and
increased coagulability of blood by a variety of mechanisms - these
include action on endothelial cells, increase coagulation of blood,
oxidative stress, increased production of leucocytes which also gets
into the sub epithelial spaces, generation of ROS (reactive oxidative
stress) Let us summarise the key health effects that are known to be
associated with air pollution:

\begin{itemize}
\tightlist
\item
  Air pollution causes lung diseases, heart diseaes, mortality, infant
  mortality, premature birth, ischaemic stroke
\item
  Mortality most commonly studied outcome.
\item
  Infant mortality is also well studied, particlularly post-neonatal
  mortality, mainly from respiratory illnesses
\item
  Mortality is high for those individuals who have suffered a heart
  attack before
\item
  Increases risk of hospitalisation for asthma and heart disease
\item
  There is no conclusive evidence whether exposure to air pollution
  leads to increase in cardiac arrhythmias
\item
  There is conclusive evidence that it leads to increased or worsening
  of asthma and COPD related symptoms
\item
  Three mechanisms proposed for heart diseases - autonomic dysfunction,
  increased cardiac susceptibility, and metabolic or other substrates of
  heart that get affected by air pollutants, particularly PM10 and
  PM2.5.
\item
  The role of ultrafine particulate matters (UFP) is less clear.
\item
  Three mechanisms proposed for heart and lung related diseases: altered
  autonomic nervous system activity with increased activity for
  sympathetic nervous system, increased inflammation both local and
  gneeral, and increased vasoconstriction and increased coagulability of
  blood by a variety of mechanisms - these include actiion on
  endothelial cells, increase coagulation of blood, oxidative stress,
  increased production of leucocytes which also gets into the
  subepithelial spaces, generation of ROS (reactive oxidative stress)
\item
  The role of individual criteria pollutants versus role of mixed
  pollutants is not clear.
\item
  Individual exposure verus ambient exposure is also not always clear
\item
  Studies are done on the basis of bs (black soot), black carbon,
  concentration and health effects
\item
  Studies are also done on the basis of exposure to traffic and distance
  from the highway.
\item
  For pregnancy outcomes, it is not clear whether selective exposure in
  the firt trimester versus in the last trimester is more dangerous,
  also whether low socioeconomic status is a confounder or effect
  modifier
\item
  exppsure to air pollution leads to inflammation of tissues and
  inflammation of lung interstitial tissues, increased thickening of
  blood vessels, increased likelihood of ischaemic stroke but rupture of
  plaques, and this makes exposure to air pollution for people with
  diabetes paricularly risky as this can increase the risk of
  atherosclerotic plaque formation and rupture due to oxidative stress
\end{itemize}

So what can we do about it and how do we study the effects of our
actions?

\subsection*{Air Pollution and health effects due to climate
change}

Hassan, N. A., Hashim, Z., \& Hashim, J. H. (2016). Impact of Climate
Change on Air Quality and Public Health in Urban Areas. Asia-Pacific
Journal of Public Health / Asia-Pacific Academic Consortium for Public
Health, 28(2 Suppl), 38S--48S. http://doi.org/10.1177/1010539515592951
Notes: A systematic reivew on the association between air quality and
global warming. Global warming caused climate change and this in turn
caused air pollution by increasing the concentratin of criteria
pollutants in the earth's atmosphere. These include CO2, N2O, O3, SOX,
F-gases. CO2 is cuaused both by human activities and ny from emisio
emissions from plants, animals an d and ocean/atmosphere exchanges.
Others are all essentially from human ca activiiea activities such as
agricultural activities, and use of motor vehilcles or use of power
plants. If the temperature increases, the criteria pollutants also
increase. The urban heat ila ila islands lead to further increase in the
criteria pollutants. These are associated with increased risk of deaths
from cardiovascular diseases and respiratory illnesses. Several
mitigation actiivities are proposed. These include less reliance on
motor vehicles, and use of less nitrogen fixing frertisi fertilisers in
soil and establishment of carbon banks and carbon exchange programmes.
p.1: The Intergovernmental Panel on Climate Change (IPCC) defines
climate change as ``a change in the state of the climate that can be
identified by changes in the mean and/or the variability of its
properties and that persists for an extended period, typically decades
or longer.'' -- Highlighted 21/07/2017 p.1: attributed directly by
natural process or indirectly through anthropogenic changes in the
composition of the atmosphere. It is estimated that without new policies
aiming to mitigate climate change, the global mean surface temperature
is predicted to increase by 3.7\selectlanguage{ngerman}°C to 4.8°C over the next 100 years. --
Highlighted 21/07/2017 p.1: Climate change and air quality are closely
related -- Highlighted 21/07/2017 p.1: troposphere (lower atmospheric
layer -- Highlighted 11/09/2017 p.3: anthropogenic activities can very
likely explain most of the warming incidences since the mid-20th century
-- Highlighted 16/08/2017 p.8: Global Methane Initiative. -- Highlighted
11/09/2017 A new study has found air pollution could be a direct cause
in some of the thousands of stroke deaths in New Zealand each year. A
global research team, co-led by Auckland University of Technology (AUT)
professor Valery Feigin, analysed data from other studies, reports and
official statistics to create a mathematical model estimating stroke
risk for 188 countries from 1990 to 2013. It found about five per cent
of strokes in New Zealand were caused by smog - with Timaru's air
pollution levels rating the worst in Oceania.~Figures recently released
by the World Health Organisation Christchurch was the worst of New
Zealand's major cities, while Wellington and Auckland's air was found to
be much cleaner. Although the study found that there was a direct link
to air pollution, about 90 per cent of strokes were preventable.~The
study found the link between air pollution and strokes varied in
different parts of the world. In China, air pollution accounted for 40
per cent of strokes, but in Australia it was responsible for around one
per cent.~~ ~ Indoor air pollution from unprocessed solid fuel use and
pneumonia risk in children aged under five years: a systematic review
and meta-analysis Mukesh Dherani a, Daniel Pope a, Maya Mascarenhas b,
Kirk R Smith b, Martin Weber c, Nigel Bruce a Introduction ~ ~With
annual deaths from pneumonia in children under 5 years old exceeding 2
million and scant evidence of a decline in this number in the last 5--10
years, prevention remains a critical component of control strategy.1 In
1995, Kirkwood et al. identified indoor air pollution (IAP) from
household use of solid fuels (wood, animal dung, crop wastes and coal)
as one of several modifiable risk factors requiring evaluation.2 Solid
fuels remain the principal household fuel for around 3 billion people,
and since their use is closely linked to poverty, this is also a
population with generally poor access to health care. ~ ~Several reviews
have examined the available evidence linking IAP with childhood
pneumonia, culminating in the meta-analyses carried out for the 2004 WHO
comparative risk assessment.3,4 A total of 15 studies were reviewed in
detail, and eight included in the meta-analyses. Exclusions were for
specific methodological issues including low-exposure prevalence and
inadequate assessment of exposure and outcome. The authors reported an
overall pooled odds ratio (OR) of 2.3 (95\% confidence interval, CI:
1.9--2.7). Subanalyses examining the effects of different exposure
measures, degree of adjustment for confounding, and children's age were
quite consistent, although limited by small numbers of studies. Since
very few of the studies measured exposure, the review could not relate
risk of pneumonia to actual levels of pollutants. ~ ~Since completion of
the meta-analysis by Smith et al.,3 several new studies have been
published, and the first randomized control trial (RCT) testing the
impact of a chimney wood stove (compared with a 3-stone open fire) in
Guatemala has been completed.5--7 It was therefore important to update
this systematic review and evaluate exposure-response data available
from two recent studies.7,8 Additional objectives were to assess whether
risk of acute lower respiratory infection (ALRI) differed by: (i)
etiological agent (viral versus bacterial), and (ii) severity, as both
issues have implications for the fraction of ALRI disease burden
attributable to solid fuels. Methods ~ ~Inclusion criteria relating to
exposure, outcome and population were used to identify observational
(cross-sectional, case--control, cohort) and intervention studies
investigating risk of childhood (\textless{} 5 years) ALRI with
household use of solid fuels (Box 1). Two reviewers (Mukesh Dherani and
Maya Mascarenhas) independently interrogated the main published and
unpublished literature databases (Table 1) to identify relevant studies
using the search terms listed in Table 2, with an additional reviewer
conducting searches of Chinese language databases (Table 1 and Table 2,
available at:
http://www.who.int/bulletin/volumes/86/5/07-044529/en/index.html). Box
1. Inclusion criteria for the systematic review ~ ~Criteria for outcome
of child ALRI * Pneumonia assessed by recall (by caregiver) of key
symptoms and signs within a specified time period (recall of up to two
weeks) * Pneumonia assessed by report and/or recall (by caregiver) of
key symptoms with direct observation of signs by staff trained under WHO
guidelines * Assessment by a physician, leading to a diagnosis of
pneumonia or other lower respiratory infection * In addition to any of
the above, chest X-ray * In addition to any of the above, blood culture
and/or culture of bronchioalveolar lavage * RSV disease (which may also
be described as bronchiolitis) ~ ~Criteria for child exposure to
household IAP * Fuel use: unprocessed solid fuels compared to clean(er)
fuels such as liquefied petroleum gas, kerosene and electricity (fuels
for comparison need to be specified) * Behavioural: time child spends
near the (solid fuel) stove or other relevant behaviours * Behavioural:
child carried while cooking * Structural: improved stove compared to
traditional stove; cooking or heating inside compared to outside *
Availability of actual measurements of IAP and/or exposure that
demonstrate substantive exposure differential ~ ~ALRI, acute lower
respiratory infection; IAP, indoor air pollution; RSV, respiratory
syncytial virus. ~ ~Pre-defined forms were used to extract information
from selected studies, and methodological quality was assessed using
design-specific bespoke instruments. Almost all studies were
observational; as a consequence, particular care was required to
identify bias and confounding so as to avoid arriving at erroneous but
precise risk estimates from meta-analysis.9 All studies meeting criteria
for review are summarized in Appendix A (available at:
http://pcwww.liv.ac.uk/\textasciitilde{}ngb/) with a further explanation
of quality assurance procedures. ~ ~The approach to meta-analysis was
first to pool all eligible studies and then to carry out sensitivity
analyses to assess the impact of methodological concerns. Eligible
studies allowed distinction between upper and lower respiratory
infection, and provided a risk estimate for ALRI with 95\% CI (or data
allowing calculation). The criterion for using random-effects
meta-analysis was significant heterogeneity on Cochran's Q (P
\textless{}\selectlanguage{english} 0.1) and/or an I2 statistic value \textgreater{} 50\%.
Sensitivity analyses were carried out for bias in control selection;
exposure prevalence; exposure assessment; outcome assessment; control
for confounding; age group. The information used to select studies for
each sensitivity analysis is presented in Appendix A. ~ ~Publication
bias was checked by funnel plot asymmetry and use of Begg's and Egger's
tests.9 Statistical analyses were conducted using RevMan 4.2.10
(Cochrane Collaboration's Information Management System, available at:
http://www.cc-ims.net/RevMan) and Stata, version 9.1, software (Stata
Corp., College Station, TX, United States of America). The impact of
publication bias was assessed by (i) manual stepwise trimming removing
studies with lowest precision and highest ORs until Egger's test was
non-significant, and (ii) using ``metatrim'' (Stata) which uses the
Duval and Tweedie trim and fill procedure.10 Due to uncertainty about
adjustment methods for publication bias in the presence of between-study
heterogeneity (metatrim may over adjust), it is recommended that the
resulting adjusted ORs be viewed as sensitivity analysis.11 * Table 1.
Electronic databases used for the systematic review html, 5kb * Table 2.
Search terms html, 4kb Results Systematic review ~ ~Fig. 1, (available
at: http://www.who.int/bulletin/volumes/86/5/07-044529/en/index.html),
summarizes the search and selection process. A total of 5317 studies
from the main electronic databases were identified. In addition, 588
studies were found from Chinese language databases, China National
Knowledge Infrastructures (CNKI) and Chinese Scientific Journal Database
(VIP), and 307 African and Latin American studies were found from
African Index Medicus (AIM) and Scientific Electronic Library Online
(SciELO) respectively (Table 1). However, since they could not be
electronically merged to identify duplicates, they are enumerated
separately (Fig. 1). From all studies, 255 were selected for review, 43
for full data extraction and quality appraisal. Of these, 25 met
criteria for the review. Fig. 1. Flowchart for study selection AIM,
African Index Medicus; ALRI, acute lower respiratory infection; RSV,
respiratory syncytial virus; SciELO, Scientific Electronic Library
Online.a Results of searches by WHO Library (SciELO; EasternMed; LILACS;
Western Pacific) and of Chinese databases could not be merged
electronically, so the number of duplicates could not easily be
identified. None of the located Chinese-language studies met the
criteria for data extraction. ~ ~These 25 studies are summarized by
study design (Appendix A) and comprise 3 cross-sectional, 16
case--control, 5 cohort, and 1 RCT. Apart from two studies among Native
Americans,12,13 all were conducted in developing countries or urban
areas of countries in transition, such as Brazil14,15 and Malaysia.16
Three other studies, plus the RCT, included respiratory syncytial virus
(RSV) illness as an outcome, and allowed examination of the impact of
solid fuel use on the incidence and/or severity of RSV disease (Appendix
B; available at: http://pcwww.liv.ac.uk/\textasciitilde{}ngb/).17--19 An
overriding feature of this review is the amount of variation among
studies in terms of settings, design, exposure and outcome assessment,
and factors affecting quality. This information is summarized in
Appendix A and Appendix B, and key issues are discussed below. Study
setting and exposure prevalence ~ ~The selected studies include
populations in all major continents, urban and rural communities, using
most types of household fuel. Elevation varies from sea level to around
3000 m, and climate and seasonal patterns differ widely, with consequent
potential for influence by seasonal epidemics (e.g. RSV illness), and by
diseases such as malaria, which may be confused with pneumonia.20 The
prevalence of exposure to smoke from household solid fuel use varies
from less than 10\% in urban areas14--16,21 to more than 90\% in rural
areas of the United Republic of Tanzania.22 Populations with low
exposure prevalence (for this review defined as \textless{} 15\%) may
not be typical of poor biomass and coal-using communities in general,
and furthermore, in urban areas, solid fuels may be processed (e.g. as
charcoal, which is less polluting than wood) and often used along with
modern fuels (e.g. kerosene). None of the studies examined the possible
impact of HIV, but given the timing and location of each, this is
unlikely to be a major factor for most -- with the possible exception of
a recent study in South Africa.23 Study design ~ ~Case--control studies
were most numerous. We had concerns in a number of these studies about
bias from control selection where these were mildly ill outpatients
(e.g. acute upper respiratory infection) or children attending
immunization clinics at the same hospital that was used to recruit
pneumonia cases (details in Appendix A). Bias in the direction of the
hypothesis (larger risk) could arise since, while children with
pneumonia from poorer biomass-using homes may reach an urban referral
centre, children from such homes are less likely to attend the same
institution with mild illness or for well-child care. This will result
in controls having a non-representative low prevalence of solid fuel
use, and generate a falsely high OR. Some authors recognized this issue,
for example Morris et al.,12 who quoted high immunization rates (90\%)
as possible evidence that controls were representative of the
hospital-attending case population. By contrast, some of the other
case--control studies selected controls that were more likely to be
drawn from the same population from which cases arose, for example,
Fonseca et al.15 and Weber et al.18 Exposure assessment ~ ~A wide range
of methods were used for assessing exposure with few directly measuring
IAP (Box 2). Because of complexity in (i) the way different fuels and
devices are used for cooking, heating and lighting, and (ii) behaviours
that determine child exposure, there is likely to be misclassification
of exposure in most studies. This will tend to bias risk estimates
towards no effect. Despite this, substantive differences between group
average exposures should have been captured by most studies comparing
solid and modern fuels, as exposure studies comparing homes that use
mainly biomass for cooking with those that use clean fuels such as
liquefied petroleum gas or electricity have demonstrated substantially
lower levels with the latter.24 Several studies have also shown that
improved solid fuel stoves can deliver important reductions in kitchen
levels25,26 and child exposure27 but, since other studies have shown
minimal or no reduction even in kitchen air pollution levels,28 it is
important not to assume that a stove described as ``improved'' will
actually reduce child exposure unless so demonstrated. Box 2. Exposure
assessment methods * Questions on fuel type(s) mainly used for cooking
and, in some studies, also for heating30 * Behavioural measures, most
commonly whether ``child is carried by the mother'' while she is
cooking,18,31,37,41 also ``time spent near fire'',32 and vaguer
descriptions including ``child stays in smoke''33 * Questions on
location of the child relative to cooking place, e.g. ``cooking done in
same room as where child sleeps''38 * Type of solid fuel stove used,
e.g. comparing traditional open fire stoves with improved chimney
stoves,42 which in the case of the trial in Guatemala was the basis of
the intervention * Measurement of house pollution and/or child exposure,
in all subjects5,8,13 or a subgroup43 ALRI case ascertainment ~ ~There
is similarly a wide range of methods used for ascertaining ALRI cases
(Box 3). All of these definitions were included in the selection
criteria as each should have some validity for ALRI, although maternal
recall as used in the Demographic and Health Surveys (DHS) can be
expected to have low specificity and possibly poor validity.23,29,30
Physician and radiological diagnosis should have higher specificity and
microbiological investigations can indicate predominant viral or
bacterial etiology. Pneumonia deaths indicate severe disease (as well as
reflecting access to effective care) but validity depends on the method
used to determine cause of death: the accuracy of verbal autopsy may
(for example) be poorer in areas of endemic malaria.20,22 Box 3. ALRI
definitions and case ascertainment * Recall by parent/carer of symptoms
and signs (predominantly ``respiratory illness with fast breathing''),
usually over the previous 2 weeks * Fieldworker surveillance at weekly
home visits to identify illness episodes with cough and/or difficulty
breathing, and signs defined by WHO for recognition of ALRI44 *
Physician diagnosis, although very few studies reported standardized
protocols and/or training6 * Radiological pneumonia, varying from
``positive findings'' to detailed description of pneumonic infiltrate,
lobar consolidation and pleural effusion. Few report standardized
protocols or independent reading6 * Investigations including oxygen
saturation (pulse oximetry), respiratory viruses (mainly RSV)6,17--19
and pneumococcal disease37 * Deaths among hospitalized cases38 and among
population samples using verbal autopsy20,22 ~ ~ALRI, acute lower
respiratory infection; RSV, respiratory syncytial virus. Dealing with
confounding ~ ~In assessing how fully confounding was addressed,
evidence was sought that the following ALRI risk factors had been
matched and/or examined and adjusted for: socioeconomic status, parental
education, breastfeeding, nutritional status, environmental tobacco
smoke, crowding and vaccination status. The adequacy of control of
and/or adjustment for confounding varied considerably, and is described
in Appendix A and Appendix B. The sole RCT achieved effective balance of
confounders through randomization.5 Meta-analysis ~ ~All studies in
Appendix A were included in the meta-analysis, except Mtango et al. as
insufficient data were provided for pneumonia deaths.22 Some 27
estimates from 24 studies are included, as that by Armstrong \& Campbell
has separate results for males and females,31 that by Pandey et al. has
two groups,32 and the Guatemala trial provides distinct intention to
treat and exposure-response analyses.5,7 The funnel plot shows asymmetry
(Fig. 2), with significant Begg's (P = 0.027) and Egger's tests (P =
0.005). Exclusion (as an extreme outlier) of the high OR from Group II
in Pandey et al.'s study,32 does not eliminate the asymmetry {[}Begg's
test (P = 0.098); Egger's test (P = 0.016){]}. With low exposure
prevalence (\textless{} 15\%) studies also excluded,14--16,21 Begg's
test is non-significant (P = 0.13) but Egger's remains significant (P =
0.009). Fig. 2. Funnel plot for all studies included in meta-analysis ~
~Fig. 3, (available at:
http://www.who.int/bulletin/volumes/86/5/07-044529/en/index.html), shows
the forest plot for all 27 estimates (24 studies) grouped by study
design. The exposure comparisons made and outcome definitions used for
each OR are presented in Table 3. There was substantial heterogeneity
with I2 = 74.4\% (P \textless{} 0.0001). The pooled OR was 1.78 (95\%
CI: 1.45--2.18). Following exclusion of the high outlier,32 the OR
reduced to 1.67 (95\% CI: 1.39--2.01; Table 3), and with additional
exclusion of low exposure prevalence studies, the OR is 1.79 (95\% CI:
1.46--2.21). The pooled ORs for individual study designs did not differ
greatly (Fig. 3 and Table 3) , although the one RCT provided the lowest
estimate. To assess the impact of publication bias, in addition to
removal of the low exposure prevalence studies and the high outlier, we
trimmed three studies with the lowest precision/highest ORs to obtain
non-significant Begg's (P = 0.81) and Egger's tests (P = 0.068), and an
OR of 1.64 (95\% CI: 1.34--2.01).12,20,33 Adjustment with metatrim
involved five studies and yielded an OR estimate of 1.54 (95\% CI:
1.25--1.89). Fig. 3. Forest plot for all studies included in
meta-analysis: comparison of higher versus lower exposure a Values in
parentheses are 95\% confidence intervals. * Table 3. Pooled odds ratios
from meta-analysis of all studies and sensitivity analyses html, 8kb
Sensitivity analysis ~ ~Classification of key study characteristics used
to determine exclusions in the following sensitivity analyses are
summarized (in bold) in Appendix A. The resulting meta-analyses are
presented in Table 3. Control selection ~ ~We previously identified the
possibility of bias from control selection in some case--control
studies. The pooled OR for nine studies with more appropriate control
selection was 1.50 (95\% CI: 1.05--2.14), this lower estimate implying
that bias may have occurred. There are, however, other substantive
methodological limitations across this group of studies: thus,
additional exclusion of low exposure prevalence studies14--16,21 left
five estimates with an OR of 2.17 (95\% CI: 1.07--4.41). Control of
confounding ~ ~Fifteen study estimates, of all designs, were judged to
have adequate/good control of confounding. These had a pooled OR of 1.80
(95\% CI: 1.43--2.25) after exclusion of low exposure prevalence studies
(Table 3). The fact that the exclusion of studies with limited or no
adjustment makes so little difference may be considered surprising. One
possible reason is offered by some of the authors, namely that, in some
settings, exposure contrasts may be observed with little heterogeneity
of socioeconomic and related factors,8,32 but this may not be the full
explanation. Exposure prevalence and assessment ~ ~This analysis
retained studies comparing clean versus solid fuel, or solid fuel stove
types with evidence of substantial measured exposure differences.
Exposure defined by ``carriage on mother's back while cooking'' or by
``more time spent by fire'' was excluded as we are not aware of any
studies demonstrating higher exposure among these children. The pooled
OR with exclusion of low exposure prevalence studies was 1.73 (95\% CI:
1.35--2.20; Table 3). When restricted to studies comparing clean versus
solid fuel, the OR was 1.76 (95\% CI: 1.32--2.36). Outcome assessment ~
~To determine the impact of variation in outcome assessment, we
initially excluded studies based on the DHS surveys.23,29,30 The
resulting OR, with additional exclusion of low exposure prevalence
studies and the outlier, was 1.89 (95\% CI: 1.44--2.48). For studies
using the most specific outcomes, that is physician diagnosis, chest
X-ray, laboratory confirmation of pneumococcal disease,34 and death
(with cause determined by verbal autopsy),20 the pooled OR was 1.83
(95\% CI: 1.31--2.55) with exclusion of low exposure prevalence studies.
With exclusion of one further study35 that used physician diagnosis
obtained from record cards over an 18 month period (the authors claim
these records should be complete, but no validation is provided), the OR
is 1.97 (95\% CI: 1.44--2.70). Age group ~ ~Pooled ORs were slightly
higher for the younger two age groups, even when low exposure prevalence
studies and the high outlier were excluded. Risk of RSV
disease/bronchiolitis ~ ~Appendix B summarizes four studies providing
information on risk of RSV illness, one of which also studied human
metapneumovirus,19 and results are conflicting. Weber et al. found that
more frequent cooking (higher exposure) was protective for severe RSV
with an adjusted OR of 0.31 (95\% CI: 0.14--0.70).18 This was somewhat
consistent with the Guatemala trial,5 which found no increase in risk of
severe (hypoxaemic) RSV positive cases, OR (open fire versus
intervention stove or ``plancha'')\selectlanguage{english} =\selectlanguage{english} 0.95 (95\% CI: 0.54--1.67), but an
increased risk in the open-fire group for hypoxaemic RSV negative cases,
OR (open fire versus plancha)\selectlanguage{english} =\selectlanguage{english} 1.64 (95\% CI: 0.96--2.78). In contrast,
Al-Sonboli et al. found an adjusted OR of 10.3 (95\% CI: 2.2--48.0) for
risk of severe RSV illness with exposure.19 Similarly, Jeena et al.'s
data yields an unadjusted OR of 2.42 (95\% CI: 0.84--6.83) for exposure
to pollution (adjusted estimate not given but stated as
non-significant).17 Although bias from control group selection is likely
in both of the latter studies,19 this conflicting evidence requires
further investigation. Impact on severe outcomes ~ ~Severe pneumonia,
best predicted by hypoxaemia, has higher case fatality than less
severe.36 Bacterial pneumonia also has higher case fatality than viral,
although hypoxaemia is common in severe RSV illness. Risk estimates for
severe and non-viral pneumonia are therefore important in assessing the
fraction of ALRI disease burden preventable through exposure reduction.
Five studies (all included in Appendix A) provide data to examine risk
by severity with outcomes defined by one or more of (i) hypoxaemia, (ii)
pneumococcal infection, (iii) RSV negative with hypoxaemia, and (iv)
deaths from pneumonia. Hypoxaemic and bacterial pneumonia ~ ~O'Dempsey
et al. reported an adjusted OR of 2.55 (95\% CI: 0.98--6.65) for
pneumococcal disease (pneumonia, meningitis and septicaemia; 79\%
pneumonia) in under 5 year olds.37 Preliminary intention to treat
analysis of the Guatemala trial found an OR for open fire versus plancha
of 1.85 (95\% CI: 1.04--3.23) for severe pneumonia as assessed by
fieldworkers under WHO guidelines while for all physician-diagnosed
hypoxaemic cases the OR for open fire versus plancha was 1.35 (95\% CI:
0.92--2.00) and 1.64 (95\% CI: 0.96--2.78) for RSV negative and
hypoxaemic cases. Exposure-response analysis of the trial found similar,
statistically significant reductions (adjusted) in risk for
fieldworker-assessed severe pneumonia and physician-diagnosed hypoxaemic
pneumonia.7 Deaths from pneumonia ~ ~Using verbal autopsies, de
Francisco et al. reported adjusted ORs of 1.47 (95\% CI: 0.54--4.02) for
pneumonia deaths with ``sometimes carrying the child while cooking'' and
5.23 (95\% CI: 1.72--15.92) for ``always carrying the child while
cooking''.20 Also using verbal autopsies, Mtango et al. reported an
adjusted OR for all deaths when children slept in the room used for
cooking of 2.78 (95\% CI: 1.79--4.33).22 For pneumonia deaths (25\% of
deaths from all causes), the adjusted risk was 4.29 (95\% CI not
provided). Among 103 pneumonia cases, Johnson et al. reported mortality
among cases of 31\% for firewood users, compared with 3.6\% for
petroleum product users, an unadjusted OR of 12.3 (95\% CI:
2.57--58.60), but an adjusted estimate was not reported.38 Discussion
and conclusion ~ ~This review found considerable variation in design and
quality, and substantial statistical heterogeneity. This has been
addressed by taking an initially inclusive approach then using
sensitivity analysis to identify factors that might have contributed to
bias in the overall estimate. There was also evidence of publication
bias among the 24 studies selected for meta-analysis, not eliminated by
exclusion of one outlying high estimate.32 The overall pooled OR for all
studies was 1.78 (95\% CI: 1.45--2.18) which increased slightly on
exclusion of the outlier and four studies (all case--control) with very
low exposure prevalence. This estimate, and those from the sensitivity
analyses, are lower than the overall pooled result from the previous
meta-analysis (OR 2.3; 95\% CI: 1.9--2.7),3 reflecting some differences
in inclusion criteria, the larger number of studies included in this new
review and findings from the additional studies now available. ~
~Sensitivity analyses did not identify any substantial effects resulting
from differences in exposure and outcome assessment, or other aspects of
study design. Exclusion of studies with exposure prevalence less than
15\% increased risk estimates. However, the thoroughness with which
confounding was controlled appeared to make little difference, possibly
due to relatively low levels of heterogeneity in other pneumonia risk
factors in some of the studies with less complete adjustment. Quality of
exposure assessment, and restriction to studies comparing solid versus
clean fuels also made little difference. Exclusion of the DHS-based
studies, and restriction to physician or more specific outcome
definition, did result in higher risk estimates of around 1.9 to 2.0 but
this may in part reflect the consequently greater influence of the
case--control studies. Risk was higher in younger children and, although
the differences were small, we would expect this due to their
vulnerability and proximity to pollution sources. ~ ~Due to wide
variation in study designs, methods and quality, it was not possible to
obtain a pooled estimate for studies which satisfied all desirable
quality criteria, as few would be retained. Consequently, and taking
into account the lack of any strong effects from sensitivity analyses,
we conclude that the most appropriate single estimate is that for all
studies, excluding those with low exposure prevalence, and the high
outlier from Pandey et al., that is 1.79 (95\% CI: 1.46--2.21).
Publication bias is potentially important: the two adjustment methods
yielded ORs of 1.54 and 1.65, and it is recommended that these be
considered for sensitivity analysis in assessment of disease impact and
economic analysis. ~ ~The few studies with data on RSV risk are not in
agreement and further studies are required to elucidate this
relationship. The findings from all five studies with information on
severe and fatal pneumonia are consistently in the direction of
increased risk, the odds ratios are substantial and, where available,
within-study comparisons show a larger effect on the more severe
outcomes. One study found an exposure (by category)-response
association.20 It is concluded that risk for severe pneumonia is similar
to that for all pneumonia at least, and quite possibly greater. ~ ~Since
only one trial is available, evaluation of the impact on ALRI of various
types of intervention in different settings will need to draw on other
sources as well, including risk of exposure (this review), data on
exposure differentials observed between various fuel and stove
combinations,24,39,40 and evidence on IAP and exposure reductions
achieved with stoves and other interventions.25 Importantly, the two
studies providing evidence on the exposure--response relationship report
that risk falls progressively from higher to lower exposure.5,7,8 ~ ~We
conclude that reduction of household IAP from solid fuel use through
switching to other fuels, improving combustion and ventilation, and
possibly other measures, would make an important contribution to
prevention of pneumonia morbidity and mortality. Additional intervention
studies are desirable, where possible these should include randomized
trials, but other designs should also be considered in the context of
intervention programmes. Future studies should ensure careful
description of exposure (and measure exposure directly in a subgroup at
least) and adopt pneumonia case-ascertainment protocols that offer good
specificity. However, despite the variations in methods and quality
among the studies reviewed, there is sufficient evidence now available
to justify much greater exposure-reduction efforts in the hundreds of
millions of households using solid fuels worldwide. \selectlanguage{english}# Acknowledgements ~
~We thank Alisa Jenny and Ray Liu (University of California, Berkeley)
for assistance with the review and for searching the Chinese databases;
and Shamin Qazi (Department of Child and Adolescent Health and
Development, WHO). Funding support was provided by the United Nations
Children's Fund (UNICEF) and Ray Liu was supported by the CC Chen
Fellowship Fund. ~ ~Competing interests: None declared. References *
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10.1038/sj.ejcn.1601845 pmid: 15042122. Affiliations * Division of
Public Health, University of Liverpool, Liverpool, England. *
Environmental Health Sciences, School of Public Health, University of
California, Berkeley, CA, United States of America. * Department of
Child and Adolescent Health and Development, WHO Indonesia Office,
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WHO 2013

\section*{Points Raised in the god 2013 air pollution
paper}

Air Pollution and GBD 1 Need~to~look up:~Brauer et al. 2012 and Lim et
al. 2012 Need to search for this paper Look up 10.1289/ehp.1408646 What
is AOD? van Donkelaar et al. (2014) ?what was the paper? What is
regression calibration approach to combine the mean of the satellite
based estimates and tm5-fasst simulations with the measurements? For the
regression calibration, we initially evaluated a simple regression with
measured log(pm2.5) = B0 + B1 * log(fused) where fused is the mean of
the satellite-derived and TM5 estimates a single global calibration
function was chosen Evaluation of model residuals indicated no
association with population density and addition of population density
to the model only minimally improved fit. We then evaluated the impact
of including available information on the measurements and measurement
sites including whether the exact coordinates were known, whether
PM2.5was directly measured or estimated and whether the monitoring site
classification was known or unspecified. Inclusion of these variables
slightly improved the model R2and slightly reduced the residual standard
error.

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