The burden of disease from indoor air pollution - Environmental ...

Int. J. Hyg. Environ. Health 206, 279 ± 289 (2003)¹ Urban &Fischer Verlaghttp:// www.urbanfischer.de/journals/intjhygInternational Journalof Hygiene andEnvironmental HealthThe burden of disease from indoor air pollution in developingcountries: comparison of estimatesKirk R. Smith, Sumi MehtaEnvironmental Health Sciences, School of Public Health, University of California Berkeley, California, USAReceived December 16, 2002 ¥ Revision received January 10, 2003 ¥ Accepted January 13, 2003AbstractFour different methods have been applied to estimate the burden of disease due to indoor airpollution from household solid fuel use in developing countries (LDCs). The largest numberof estimates involves applying exposure-response information from urban ambient airpollution studies to estimate indoor exposure concentrations of particulate air pollution.Another approach is to construct child survival curves using the results of large-scalehousehold surveys, as has been done for India. A third approach involves cross-nationalanalyses of child survival and household fuel use. The fourth method, referred to as the`fuel ± based' approach, which is explored in more depth here, involves applying relative riskestimates from epidemiological studies that use exposure surrogates, such as fuel type, toestimates of household solid fuel use to determine population attributable fractions bydisease and age group. With this method and conservative assumptions about relative risks,4 ± 5 percent of the global LDC totals for both deaths and DALYs (disability adjusted lifeyears) from acute respiratory infections, chronic obstructive pulmonary disease, tuberculosis,asthma, lung cancer, ischaemic heart disease, and blindness can be attributed to solid fuel usein developing countries. Acute respiratory infections in children under five years of age arethe largest single category of deaths (64%) and DALYs (81%) from indoor air pollution,apparently being responsible globally for about 1.2 million premature deaths annually in theearly 1990s.Key words: Indoor air pollution ± developing countries ± household solid fuel use ± riskassessment ± global burden of diseaseIntroductionAir pollution has been consistently linked withsubstantial burdens of ill-health in developed anddeveloping countries (Schwartz 1994; WHO 1999;Bruce et al., 2000; Smith et al., 2000). Most recently,as part of the Comparative Risk Assessment(CRA) project of the World Health Organization(WHO), Geneva, the global and regional burdens ofdiseases (death, illness, injury, lost life years, andyears lost to disability) by age, sex, and 14 worldregions were calculated for a range of risk factors,including indoor and outdoor air pollution (WHO2002). It is the intention here to discuss the historyCorresponding author: Professor Kirk R. Smith, Environmental Health Sciences, School of Public Health, Universityof California Berkeley, CA 94720-7360, USA. Phone: ‡‡510643 0793, Fax: ‡‡510642 5815, E-mail:krksmith@uclink.berkeley.edu1438-4639/03/206-04-05-279 $ 15.00/0

280 K. R. Smith, S. MehtaTable 1. Indoor pollution sources by major pollutant types.ParticlesCombustion byproducts(CO, NOx)Volatile organics Biologicals Pesticides RadonSolid fuel andtobaccocombustion,cleaningFuel and tobaccocombustionFurnishings, householdproducts, solid fuel andtobacco combustionFurnishings, ventilationducts, moist areasHousehold products,dust from outsideGround underbuilding, ventilationcharacteristicsand briefly compare the results of earlier estimates ofthe health effects of indoor air pollution to the latestresults in the WHO CRA.The bulk of air pollution research has focused onurban outdoor (ambient) air pollution. With therapid increase in vehicular and other pollutionsources in urban areas of developing countries, andburgeoning numbers of epidemiological studies indeveloped countries showing effects at what used tobe considered low levels (WHO 1999), outdoorsources have remained the center of most airpollution research worldwide. Not surprisingly,then, the first estimate of the global burden ofdisease from air pollution only addressed outdoorair pollution ((Hong, 1995), summarized in (Murrayand Lopez 1996)). This endeavor focused on thehealth effects of two ambient air pollutants, totalsuspended particulates and sulfur dioxide, to estimatethat some 500 000 deaths from pneumonia,COPD, cardiovascular diseases, and all causescombined could be attributable to outdoor airpollution each year. It estimated regional urbanexposures by reference to the WHO/UNEP GEMSurban air pollution database and applied availableexposure-response information to determine impacts.Because few exposure-response studies hadbeen done in developing countries, the results ofthose done in China were applied to the rest of thedeveloping world. The counterfactual levels chosenwere the WHO air quality guidelines (WHO 1979).The 2002 WHO CRA used measured and modeledurban particle pollution as its sole indicator of healthrisk from outdoor air pollution (Cohen et al., 2003).In reality, however, indoor sources of air pollutionalso pose substantial risks and, for some pollutants,probably dominate global human exposure. This isso even though pollutant emissions are dominatedby outdoor sources. Exposures are a function of thedegree of pollution in places were people spend timeand, globally, people spend the majority of their timeindoors. As a result, a gram of pollution releasedindoors is likely to cause many hundreds of timesmore exposure than a gram released outdoors.Similarly, even outdoors, a cookfire near the housewill produce much more exposure per unit emissionsthan a vehicle or factory some distance away fromplaces where the population spends most time. Thisconcept, originally referred to as, inter alia, exposurefactor∫ (Smith 1988) or exposure/doseeffectiveness∫ (Smith 1993) is now termed intakefraction∫(Bennett et al., 2002).Unfortunately, there are also important indooremission sources throughout the world and consequentsignificant exposures. As with outdoor airpollution, however, the bulk of indoor air pollution(IAP) research and control has focused on sources ofconcern in developed countries. Table 1 provides asimple categorization of indoor sources according topollutant category worldwide.The important non-occupational indoor environmentsthat might be included in a complete IAP CRAwould be households, schools, and passenger compartmentsin vehicles. Unfortunately, however, thereare too few exposure and exposure-response studiesto derive reliable global risk estimates for the lattertwo microenvironments.Because they contain the largest fraction of timespent by nearly all populations worldwide, householdsources of pollution can dominate exposures.(We are focusing here on indoor sources, not indoorexposures. The latter is influenced by outdoorsources too, of course, since outdoor pollutionpenetrates indoors. Indeed, overall, the major impactof outdoor pollution is probably through theindoor exposures it causes, since such a largefraction of the population's time is spent indoors.)Indeed, based on available measurements, it seemsthat bulk of global IAP exposures seem to be due tojust two categories: the combustion of solid fuels forcooking/heating and environmental tobacco smoke(ETS). In fact, these sources probably produce moreexposure for several important pollutants than alloutdoor sources. Here we focus solely on solid fueluse. In terms of available epidemiology and otherrisk estimates, however, it is also possible tocalculate the impact of ETS and radon on diseasefor any population where sufficient exposure dataare available. Insufficient exposure data were availableat the time of the WHO CRA to do so globally,however.

Indoor air pollution 281Table 2. Summary of risk assessment methods applied to solid fuel use in developing countries.Approach Methodology utilized Type of data utilized Likely bias ofmethod as utilizedPollutant-based Exposure-response extrapolation Estimated exposure concentrations for indicator pollutants(usually particulates)Exposure-response relationships fromurban outdoor studies, usually in developed countriesCurrent rates of morbidity and mortalityChild survival Survival analysis Survival curves for different risk factors based on householdsurveys Done only for India to dateCross-national Regression Cross-country comparisons of national-level data on healthFuel-basedRemainder-basedDisease by disease summationfrom binary exposure classificationbased on published epidemiologyliteratureEstimate fraction of burden ofmajor categories of IAP-relatedill-health that can be attributedto non-IAP factors, the remainderis applied to IAPand energy conditionsEstimated distribution of exposure surrogates, usually fueltype Relative risk primarily from developing-country householdstudies of specific diseases in specific populationgroups experiencing exposure surrogate Current rates ofmorbidity and mortality for each diseaseIf there are no unaccounted major risk factors, uncertaintiesin the known ones, and no interactions among risk factors,the remainder can be considered a lower bound estimate forthe fraction attributable to IAP Unfortunately, however,these conditions are not likely to be met*OverestimateOverestimateOverestimateUnderestimateUnknown* See estimates by Florig (1997) for China in Table 3. This method is not discussed further in this paper.About half of the world continues to cook withsolid fuels, such as dung, wood, agricultural residues,and coal. In simple household stoves, thesefuels emit substantial amounts of a number ofimportant pollutants, including respirable particles,carbon monoxide, toxic organic compounds such asbenzene, formaldehyde, and 1,3-butadiene, andpolyaromatic compounds, such as benzo(a)pyrene(Smith 1987). In households with limited ventilation,as is common in many developing countries,exposures to householders, particularly women andyoung children who spend a large proportion of theirtime indoors, have been measured to be many timeshigher than WHO Guidelines and national standards(Smith 1987).Different approaches used to estimateenvironmental burden of diseaseKnown to us are five different methods that havebeen applied to estimate the burden of disease fromsolid fuel use in developing countries, each withadvantages and disadvantages. Given that theirresults are fairly similar, taken together they providesome credibility, although by no means proof,∫ forthe assertion that the problem is severe. Here webriefly summarize the methods and results fromapplication of three of these methods, as done mostlyby others, and then explain the specific approachused in the recent WHO CRA in more detail. Asummary of the different approaches and theirsources of data is given in Table 2.Pollutant-based approachThis method involves the following steps: 1) Estimatetotal population exposures from indoor sourcesto the indicator pollutant. Estimates of this typehave relied on particulate matter as the indicatorpollutant and mean exposure concentrations in mg/m 3 as the metric. 2) Determine best availableexposure-response factors for this pollutant. 3)Find the current rates of morbidity and mortalityin the population of concern. 4) Estimate theattributable number of deaths and diseases.Table 3 is a summary of the results from suchefforts done globally and for the two largest nations,India and China. Shown for comparison are estimatesfor outdoor air pollution done using the samemethod. That the exposure-response relationshipshave been derived for outdoor air pollution in urbansituations, where the chief source of particulates isfossil fuel burning, however, raises a number ofquestions about their suitability for applicationindoors mainly with rural populations relying onbiomass fuels. In addition, some of the studies relymostly on developed-country studies. These characteristicsraise several important questions (manyof these same problems plague attempts to calculateimpacts of outdoor air pollution in developingcountries from epidemiologic results in developedcountries (Cohen et al. 2003)): 1) Differences inpollutant mix due to different sources, i.e. although

282 K. R. Smith, S. MehtaTable 3. Estimates of annual pre-mature mortality from air pollution for world, India, and China using the pollutant-based method.LocationOutdoor Exposure( 1000 deaths)Indoor Exposure( 1000 deaths)Pollutant Comments ReferenceWorld 570 ± - PM, SO x First draft done for GBD database using local airmonitoring data and local exposure-responsedata where available200 2800 PM Using local air pollution monitoring data andMDC exposure-response information at lowexposures and half risk at higher exposures510 2200 PM Using local air pollution monitoring data andlocal exposure-response data where available799 PM Second iteration of GBD, includes only urban airpollution. Relies on econometric model topredict PM levels in those cities without dataIndia 50 ± 300 850 ± 3300 PM Using urban air quality data and rural exposuresfrom rural microenvironment studies and urbandistribution; MDC exposure-response information;range comes from spread between dailyand annual studies40 ± - PM 36 cities only based on MDC exposure-responsedata86 ± - PM, SO x Uses Chinese exposure-response data since nonein India84 590 PM Using local air pollution monitoring data andChinese exposure-response data since none inIndia200 2000 PM Based on estimates of time and exposures inmajor microenvironments by important populationgroups and MDC exposure-response data52 PM Extrapolation of (Brandon and Hommann 1995)using 1995 air pollution monitoring data107 PM Assumes here that India impacts proportional toits population in the WHR SEAR-D region, i.e.81%China 68 ± - PM, SO x Uses exposure-response data developed in China.(Hong 1995)(WHO 1997),based on(Smith 1994)(WHO 1997),(Schwela 1996)(WHO 2002)(Smith 1994)(Brandon andHommann 1995)(Hong 1995)(WHO 1997),(Schwela 1996)(Saksena andDayal 1997)(Kumar, et al. 1997)(WHO 2002)(Hong 1995)70 370* PM Uses Chinese exposure-response data. (WHO 1997)180 110* PM Using exposure-response data from Chinesecities. Assumes only 13% of rural populationexposed to IAP.(World Bank 1997)298 PM Assumes here that China impacts proportional toits population in the WHR WPR-B region, i.e.84%.17 ± 290 720 ± 1200* PM Based on evaluation of Chinese exposure-responsedata for COPD, lung cancer, coronaryheart disease, and childhood ARI. Combinationof exposure-based and pollutant-based data.* All these estimates use (Sinton et al., 1996).** Remainder-based approachCOPD ˆ Chronic Obstructive Pulmonary Disease; ARI ˆ Acute Respiratory InfectionsLDC ˆ less-developed country; MDC ˆ more-developed country, PM ˆ particulate matter(WHO 2002)(Florig 1997)**particulates can be used as indicator of hazard inboth cases, biomass fuels as commonly used in LDChouseholds produce relatively more organic compounds(e.g., benzene, formaldehyde, 1,3-butadiene,polyaromatic hydrocarbons) and fossil fuelsmore sulfur oxides. Thus risk (exposure-response)estimates derived in the latter situation may notapply to the former. 2) In a similar fashion, thechemical and other characteristics of the particlesproduced by biomass combustion are not the sameas those produced by fossil fuel use, although ofcourse woodsmoke is found seasonally in the outdoorair of many developed-country cities. 3)Differences in exposure patterns, i.e., indoor concentrationstend to vary much more during the day,because of household cooking and heating schedules,than do outdoor urban levels. 4) Differentexposure levels, i.e., the average exposure levels of

Indoor air pollution 285Table 5. Association of demographic indicators and biomass use (Bloom et al., 2002).Dependent variable Constant* Percent Log Inverse Log R 2 ***Traditional GNP GNPbiomass use per capita per capitaFemale life expectancy 231.647** 0.102** 6.234 889.108** 0.86Life expectancy 213.215** 0.088** 5.453 816.024** 0.86Male life expectancy 195.649** 0.076** 4.708 746.366** 0.84Infant mortality rate 795.305** 0.247** 37.945** 4203.384** 0.83Under 5 mortality rate 1377.804** 0.494** 67.613** 7066.313** 0.82Total fertility rate 0.011 0.025** 0.213 37.326 0.78Crude birth rate 66.412 0.176** 5.336 6.581 0.77Crude death rate 227.919** 0.007 13.247** 1044.726** 0.54Population growth rate 3.031 0.021** 0.184 3.546 0.43Life expectancy gap (F ± M) 35.998 0.026** 1.526 142.741 0.35* point where regression line crosses the axis** indicates statistical significance at the 5% level*** square of the regression coefficient indication fraction of variation in the health parameter explained by the variation in biomass fuel useData from 1993 and surrounding years. Traditional fuel includes fuelwood, bagasse, charcoal, animal wastes, vegetable wastes, and other wastes. Traditional fuel use isexpressed as a percentage of total fuel use.Source: Traditional fuel use data from United Nations Energy Statistics Yearbook 1993.Demographic data from World Development Indicators 1998, World Bank CD ROM.based approach, the fuel-based approach utilizesrelative risk estimates for health outcomes that havebeen associated with exposures to indoor air pollutionfrom solid fuel use. In contrast to the pollutantbasedapproach, which focuses on specific indicatorpollutants that occur as a result of combustion, thefuel-based approach takes advantage of the largenumber of available LDC epidemiological studiesthat have been conducted that treat exposure toindoor air pollution from solid fuel use as a binaryvariable, e.g., using dirty or clear fuel. A descriptionof the methodology used in the fuel-based approachis provided below and in more detail by Smith et al.(2003). Using data from the International EnergyAgency, UN statistical office, World Bank, FAO,national censuses and specific fuel-use surveys indeveloping countries, the sizes of the exposed andnon-exposed populations, which are defined simplyas those using solid fuels and those not, aredetermined by region using a model incorporatingenergy, income, and demographic parameters toextrapolate to nations not covered. Using the resultsof epidemiological studies in biomass-burninghouseholds in South Asia, Latin America, sub-Saharan Africa, and elsewhere, and coal-burninghouseholds in China, appropriate risk factors (relativerisks) for specific diseases in specific agegroups are determined using formal meta-analysistechniques to combine the results from separatestudies. Such studies are available in sufficientquantity and quality only for three disease endpoints(acute lower respiratory infections, chronic obstructivepulmonary disease, and lung cancer from coal)in two age/sex groups: adult women and childrenunder 5, who have the highest exposures to stoveemissions. Using the regional population and burdenof disease (death and disability) database from theGBD, the current patterns of these diseases in thesepopulation groups are determined. Using the standardprocedure for determining the populationattributable fraction, the total disease burden attributableto use of household fuels is determined byregion. Using the known mortality-morbidity relationshipsfor specific diseases for each age group ineach region, an estimate of the total lost life yearsand total sick days attributable to indoor airpollution is estimated. By combining the variancein the fuel-use model and the 95% confidenceinterval from the meta-analyses, objective uncertaintybounds are calculated.This approach, although not without weaknesses,substantially reduces all the problems noted abovefor the pollutant-based approach (numbered asbefore): 1±4) Being based solely on studies done inbiomass-using households, the differences in pollutantmix, particle composition, exposure patterns,and exposure levels should be substantially reducedif not eliminated. 5) The studies were all done inpoor, mostly rural, developing-country populationspresumably much more similar to the exposed LDCpopulations than urban developed-country populations.6) The studies address directly the specifichealth endpoints over time periods appropriate tothe each and thus do not reflect the possibleharvesting∫ that may be seen in time-series studies.7) Confining the assessment to women, who have

Indoor air pollution 287Fig. 2.Comparison of methods discussed in text.Table 6. Deaths from household solid fuel use estimated by different methods (uncertainty bounds not shown).Method Region Infant Deaths Under 5 deaths Total deaths ReferenceChild survival India 395 000 570 000 ± (Hughes and Dunleavy 2000)Cross national India 173 000 ± ± (Bloom and Zaidi 2000 ± rev. 2002)Fuel-based India ± 287 000 424 000 (WHO 2002)Fuel-based China ± 52000 423 000 (WHO 2002)Fuel-based World ± 910 000 1 600 000 (WHO 2002)10 th in burden (DALYs) and 11 th in prematuredeaths. It is the second most important environmentalrisk factor examined (after poor water/sanitation/hygiene), with about twice the deathsand five times the DALYs of urban outdoor airpollution. We have added two other major riskfactors not found in (WHO 2002) ± road trafficaccidents and the child cluster of vaccine-treatablediseases (measles, tetanus, polio, pertussis, anddiphtheria) ± to provide a more complete list of therisk factors amenable to public action. Data for theselatter two risk factors are taken from (WHO 2001).If these two were left out, indoor air pollution wouldrank 8 th globally.ConclusionFigure 2 summarizes the approaches used for thethree most viable methods discussed above andTable 6shows their results for India, which is theonly country in which all three were examined. Alsoshown for comparison are the results for China andthe World from the fuel-based method so as tocompare with previous estimates in Table 3.As utilized, most methods except for the fuelbasedapproach are likely to result in an overestimateof disease burden. In the pollutant-based approach,exposure response relationships from ambient airpollution studies in developed countries with differentepidemiological profiles have been linearlyextrapolated to concentrations that are often ordersof magnitude higher. The child-survival approachused a remarkably high baseline estimate of mortality(over 5% mortality in children under five). Inaddition, the child survival approach, as well as thecross- national approach, both estimate diseaseburdens based on all cause mortality. As notedbefore, this results in higher values, because it picksup indirect effects. In addition, it is extremelydifficult to separate out the effects of other socioeconomicrisk factors, such as access to water andsanitation, as well as poverty as a risk factor in and ofitself.The results of the fuel-based approach, i.e. theestimates in the WHO CRA for the world, India, andChina, are less, sometimes much less, than thosederived in pollutant-based approaches summarizedin Table 3. As noted in the text, the CRA approachwould seem to avoid many problems inherent in thepollutant-based methods applied to date. At thesame time, the inability to address potentially

288 K. R. Smith, S. Mehtaimportant health outcomes, including adverse pregnancyoutcomes and cardiovascular disease, is likelyto result in a substantial underestimate of diseaseburden.Note that China and India have quite differentmixtures of adult deaths, which are mostly fromCOPD, and children, which are from ALRI. This isat least partly due to better access to health care inChina such that ALRI kills many fewer children. Incontrast to India, however, China has a much higherCOPD rate in non-smokers. This may have to dowith the differential impacts in China of coal smoke,which is much less prevalent in India.The different exposure methods employed, andthe different health outcomes addressed make adirect comparison of the approaches difficult.Agreement between the results would not necessarilyguarantee the accuracy of the estimates, but doeslend some credibility that the burden is significant.With the most conservative estimate suggesting thataround 1.6million deaths can be associated withindoor air pollution each year, solid fuel use clearlyremains one of the major risk factors facing humanitytoday.This paper is an updated and shortened version of theconference paper of the same name and authors given atthe WHO/USAID Global Technical Consultation HealthImpacts of Indoor Air Pollution in Developing Countries,May 3 ± 4, 2000.ReferencesBennett, D., McKone, T., Evans, J., Nazaroff, W.,Margni, M., Jolliet O. and Smith K. (2002). 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