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:

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:

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. ... 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 .... 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:

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

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°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 (< 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/~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 < 0.1) and/or an I² statistic value > 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/~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 < 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 (< 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 < 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”) = 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) = 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. ■ 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 * Rudan I, Tomaskovic L, Boschi-Pinto C, Campbell H. Global estimate of the incidence of clinical pneumonia among children under five years of age. Bull World Health Organ 2004; 82: 895-903 pmid: 15654403. * Kirkwood BR, Gove S, Rogers S, Lob-Levyt J, Arthur P, Campbell H. Potential interventions for the prevention of childhood pneumonia in developing countries: a systematic review. Bull World Health Organ 1995; 73: 793-8 pmid: 8907773. * Smith KR, Mehta S, Feuz M. Indoor air pollution from household use of solid fuels. In: Ezzati M, ed. Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. Geneva: WHO; 2004. * Reducing risks, promoting healthy life: the world health report. Geneva: WHO; 2002. * Smith KR, Bruce NG, Weber M, Hubbard A, Jenny A, Dherani M, et al., et al. Impact of a chimney wood stove on risk of pneumonia in children aged less than 18 months in rural Guatemala: results from a randomized, controlled trial. Epidemiology 2007; 17: S45- doi: 10.2188/jea.17.45. * Bruce N, Diaz A, Arana B, Jenny A, Thompson L, Weber M, et al., et al. Pneumonia case-finding in the Guatemala indoor air pollution trial (RESPIRE): standardizing methods for resource poor settings. Bull World Health Organ 2007; 85: 535-44 pmid: 17768502. * McCracken JP, Schwartz J, Mittleman M, Ryan L, Diaz A, Smith KR, et al., et al. Biomass smoke exposure and acute lower respiratory infections among Guatemalan children. Epidemiology 2007; 18: S185- doi: 10.1097/01.ede.0000254653.69858.88. * Ezzati M, Kammen DM. Quantifying the effects of exposure to indoor air pollution from biomass combustion on acute respiratory infections in developing countries. 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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, Jakarta, Indonesia.                Share                    Print     You are here: * Bulletin of the World Health Organization* Past issues* Volume 86: 2008* Volume 86, Number 5, May 2008, 321-416 Quick Links Sitemap *           Home         *           Health topics         *           Data and statistics         *           Media centre         *           Publications         *           Countries         *           Programmes and projects         *           About                     WHO         Help and Services *           Contacts         *           FAQs         *           Employment         *           Feedback         *           Privacy         *           E-mail scams         WHO Regional Offices *                     WHO African Region         *                     WHO Region of the Americas         *                     WHO South-East Asia Region         *                     WHO European Region         *                     WHO Eastern Mediterranean Region         *                     WHO Western Pacific Region         Connect with WHO *           RSS Feeds         *                               WHO YouTube channel         *           Follow                     WHO on Twitter         *                               WHO Facebook page         *                               WHO Google+ page         ©         WHO 2013

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.