Indicator 3.9.1: Rata mortalit\u0103\u021bii determinate de poluarea aerului \u00een \u00eenc\u0103peri \u0219i celui ambiant
","SDG_TARGET__GLOBAL":"Target 3.9: P\u00e2n\u0103 \u00een 2030, reducerea substan\u021bial\u0103 a num\u0103rului deceselor \u0219i bolilor provocate de produsele chimice periculoase, de poluare \u0219i de contaminarea aerului, apei \u0219i a solului
","META_LAST_UPDATE__GLOBAL":"","SDG_CUSTODIAN_AGENCIES__GLOBAL":"World Health Organisation (WHO)","SDG_RELATED_INDICATORS__GLOBAL":"","SDG_INDICATOR_INFO":"","SDG_INDICATOR_INFO__GLOBAL":"","SDG_GOAL":"","target_id":"3.9","SDG_TARGET":"","GOAL_META_LINK":"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-01.pdf","GOAL_META_LINK_TEXT":"United Nations Sustainable Development Goals Metadata (PDF 216 KB)","SDG_INDICATOR":"Indicator 3.9.1: Rata mortalit\u0103\u021bii determinate de poluarea aerului \u00een \u00eenc\u0103peri \u0219i celui ambiant la 100.000 popula\u021bie
","SDG_SERIES_DESCR":"","META_LAST_UPDATE":"","SDG_RELATED_INDICATORS":"","CONTACT":"","CONTACT_ORGANISATION":"","CONTACT_NAME":"","ORGANISATION_UNIT":"","CONTACT_FUNCT":"","CONTACT_PHONE":"","CONTACT_MAIL":"","CONTACT_EMAIL":"","IND_DEF_CON_CLASS":"","STAT_CONC_DEF":"Poluarea aerului este unul dintre cele mai mari riscuri pentru s\u0103n\u0103tate. Conform datelor OMS efectele combinate ale polu\u0103rii ambientale ( a aerului din \u00eenc\u0103perile casnice \u0219i mediului ambiant exterior) determin\u0103 aproximativ peste 7 milioane de decese premature anual. Majoritatea deceselor apar ca cauz\u0103 a accidentelor vasculare cerebrale, bolilor de inim\u0103, bolilor pulmonare obstructive cronice, cancerului pulmonar \u0219i infec\u021biilor respiratorii acute. Cea mai mare povar\u0103 a sarcinii date este suportat\u0103 de popula\u021biile \u021b\u0103rilor cu venituri mici \u0219i medii.","UNIT_MEASURE":"100 mii popula\u021bie","CLASS_SYSTEM":"","SRC_TYPE_COLL_METHOD":"","SOURCE_TYPE":"1) Date statistice privind mortalitatea popula\u021biei pe cauze de deces - ANSP
2) Date statistice cu privire la popula\u021bie - BNS
","COLL_METHOD":"","FREQ_COLL":"","REL_CAL_POLICY":"","DATA_SOURCE":"1) Agen\u021bia Na\u021bional\u0103 pentru S\u0103n\u0103tate Public\u0103
2) Biroul Na\u021bional de Statistic\u0103
","COMPILING_ORG":"","INST_MANDATE":"Ministerul Muncii \u0219i Protec\u021biei Sociale","OTHER_METHOD":"","RATIONALE":"","REC_USE_LIM":"","DATA_COMP":"Num\u0103rul de decese atribuite efectelor comune ale polu\u0103rii aerului din \u00eenc\u0103perile din gospod\u0103rii \u0219i mediului ambiant \u00eentr-un an raportat la total popula\u021bie *100.000.
Pentru calcularea indicatorului urm\u0103toarele boli sunt luate \u00een considerare: infec\u021biile respiratorii acute (estimate pentru toate v\u00e2rstele); boli cerebrovasculare la adul\u021bi (estimate la persoanele peste 25 de ani); bolile cardiace ischemice la adul\u021bi (estimate la persoanele peste 25 de ani); bolile pulmonare obstructive cronice la adul\u021bi (estimate la persoane peste 25 de ani); \u0219i cancerul pulmonar la adul\u021bi (estimat la persoane peste 25 de ani).
","DATA_VALIDATION":"","DOC_METHOD":"","QUALITY_MGMNT":"","QUALITY_ASSURE":"","QUALITY_ASSMNT":"","COVERAGE":"","COMPARABILITY":"","OTHER_DOC":"","national_data_updated_date":"","national_metadata_updated_date":"","un_designated_tier":"1","source_active_1":true,"source_organisation_1":"Agen\u021bia Na\u021bional\u0103 pentru S\u0103n\u0103tate Public\u0103","source_periodicity_1":"anual","source_url_1":"","source_url_text_1":"Link la sursa de date","source_contact_1":"","source_other_info_1":"","SDG_GOAL__GLOBAL":"Goal 3: Ensure healthy lives and promote well-being for all at all ages
","SDG_SERIES_DESCR__GLOBAL":"SH_AAP_ASMORT - Age-standardized mortality rate attributed to ambient air pollution [3.9.1]
\nSH_HAP_ASMORT - Age-standardized mortality rate attributed to household air pollution [3.9.1]
\nSH_STA_ASAIRP - Age-standardized mortality rate attributed to household and ambient air pollution [3.9.1]
","CONTACT_ORGANISATION__GLOBAL":"World Health Organization (WHO)
","STAT_CONC_DEF__GLOBAL":"Definition:
\nThe mortality rate attributable to the joint effects of household and ambient air pollution can be expressed as: crude death rate or age-standardized death rate. Crude rates are calculated by dividing the brut number of deaths by the total population (or indicated if a different population group is used, e.g. children under 5 years), while the age-standardized rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population.
\nEvidence from epidemiological studies have shown that exposure to air pollution is linked, among others, to the important underlying causes of death taken into account in this estimate:
\n- Acute lower respiratory infections (estimated in all age groups; ICD-10: J09-J22, P23, U04 );
\n- Cerebrovascular diseases (stroke) in adults (estimated above 25 years; ICD-10: I60-I69);
\n- Ischaemic heart diseases (IHD) in adults (estimated above 25 years; ICD-10: I20-I25);
\n- Chronic obstructive pulmonary disease (COPD) in adults (estimated above 25 years; ICD-10: J40-J44); and
\n- Lung cancer in adults (estimated above 25 years; ICD-10: C33-C34).
\nConcepts:
\nThe mortality resulting from the exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuels used for cooking and/or heating was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood kerosene, wood, coal, animal dung, charcoal, and crop wastes.
","UNIT_MEASURE__GLOBAL":"Deaths per 100,000 population
","CLASS_SYSTEM__GLOBAL":"Not applicable
","SOURCE_TYPE__GLOBAL":"A. Exposure:
\nB. Exposure-response function:
\nThe integrated exposure-response functions (IER) developed for the Global Burden of Disease (GBD) project 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality, for the five outcomes of interest.
\nThe exposure-response function captures the magnitude of the death risks due to the exposure to air pollution by integrating epidemiological evidence from four sources of PM: ambient air pollution, household air pollution, active smoking, and second-hand smoking; and excluding the possible effects of other risk factors on the outcomes of interest. Due that, it is possible to assess the attributable burden due to household and ambient air pollution using the same IERs.
\nThe IER has recently been included and is available for download in the AirQ+ software tool for health risk assessment of air pollution, version 2.2 (released in March the 14th, 2023).
\nC. Background health burden: The total number of deaths by country, disease, sex and age group have been developed by the World Health Organization’s (WHO 2019b) Global Health Estimates (GHE).
","COLL_METHOD__GLOBAL":"A. Exposure:
\nB. Exposure-response function:
\nModelled by the WHO Air Quality and Health Unit with input from epidemiological studies on particulate matter and mortality, collected through a systematic search.
\nC. Background health burden: collected from the WHO Global Health Estimates (GHE).
","FREQ_COLL__GLOBAL":"Not applicable
","REL_CAL_POLICY__GLOBAL":"Not applicable
","DATA_SOURCE__GLOBAL":"WHO Global Health Estimates
\nGlobal Burden of Disease project
\nWHO as a custodial agency of the SDG 11.6.2
\nWHO as a custodial agency of the SDG 7.1.2
","COMPILING_ORG__GLOBAL":"World Health Organization (WHO)
","INST_MANDATE__GLOBAL":"Not applicable
","RATIONALE__GLOBAL":"As part of a broader project to assess major risk factors to health, the mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuel use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, as well as kerosene.
\nAir pollution is the biggest environmental risk to health. The majority of the burden is borne by the populations in low and middle-income countries.
","REC_USE_LIM__GLOBAL":"An approximation of the combined effects of risk factors (i.e., ambient and household air pollution) is possible if independence and little correlation between risk factors with impacts on the same diseases can be assumed (Ezzati et al, 2003). In the case of air pollution, however, there are some limitations to estimate the joint effects: limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions (Lim et al, 2012; Smith et al, 2014). In several regions, however, household air pollution remains mainly a rural issue, while ambient air pollution is predominantly an urban problem. Also, in some continents, many countries are relatively unaffected by household air pollution, while ambient air pollution is a major concern. If assuming independence and little correlation, a rough estimate of the total impact can be calculated, which is less than the sum of the impact of the two risk factors.
\nOn the other hand, as the IER function integrates epidemiological evidence from four sources of PM (i.e., ambient air pollution, household air pollution, active smoking and second-hand smoking), some assumptions are assumed. Specifically, the relative risk at any concentration is independent of the source of PM2.5, and only dependent on the magnitude of the total exposure from all sources together (Burnett et al, 2020).
","DATA_COMP__GLOBAL":"Attributable mortality is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed, proportion of population relying primarily on polluting fuels for cooking).
\nThis allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in that case of both the annual mean concentration of particulate matter and exposure to polluting fuels for cooking).
\nApplying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths), gives the total number of deaths that results from exposure to that particular risk factor (in the example given above, to ambient and household air pollution).
\nTo estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).
\nThe mortality associated with household and ambient air pollution was estimated based on the calculation of the joint population attributable fractions assuming independently distributed exposures and independent hazards as described in (Ezzati et al, 2003).
\nThe joint population attributable fraction (PAF) were calculated using the following formula:
\n\nWhere PAFi is PAF of individual risk factors.
\nThe PAF for ambient air pollution and the PAF for household air pollution were assessed separately, based on the Comparative Risk Assessment (Ezzati et al, 2002) and expert groups for the Global Burden of Disease (GBD) 2010 study (Lim et al, 2012; Smith et al, 2014).
\nFor exposure to ambient air pollution, annual mean estimates of particulate matter of a diameter of less than 2.5 um (PM25) were modelled as described in (Shaddick et al, 2018; Shaddick et al, 2021)), or for Indicator 11.6.2.
\nFor exposure to household air pollution, the proportion of population with primary reliance on polluting fuels use for cooking was modelled (see Indicator 7.1.2 [polluting fuels use=1-clean fuels use]). Details on the model are published in (Bonjour et al, 2013).
\nThe integrated exposure-response functions (IER) developed for the GBD 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality for the five outcomes of interest.
\nThe percentage of the population exposed to a specific risk factor (here ambient air pollution, i.e. PM2.5) was provided by country and by increment of 1 µg/m3; relative risks were calculated for each PM2.5 increment, based on the IER. The counterfactual concentration was selected to be between 2.4 and 5.9 µg/m3, as described elsewhere (Cohen et al, 2017). The country population attributable fraction for ALRI, COPD, IHD, stroke and lung cancer were calculated using the following formula:
\n\nWhere i is the level of PM2.5 in ug/m3, and Pi is the percentage of the population exposed to that level of air pollution, and RR is the relative risk.
\nThe calculations for household air pollution are similar and are explained in detail elsewhere (WHO 2014a).
","DATA_VALIDATION__GLOBAL":"Not applicable
","ADJUSTMENT__GLOBAL":"Not applicable
","IMPUTATION__GLOBAL":"Countries with no data are reported as blank.
\nCountries with no data are not considered to estimate the regional and global averages.
","REG_AGG__GLOBAL":"Number of deaths by country are summed and divided by the population of countries included in the region (regional aggregates) or by the total population (global aggregates).
","DOC_METHOD__GLOBAL":"Not applicable
","QUALITY_MGMNT__GLOBAL":"Not applicable
","QUALITY_ASSURE__GLOBAL":"Not applicable
","QUALITY_ASSMNT__GLOBAL":"Not applicable
","COVERAGE__GLOBAL":"Data availability:
\nData is available by country, sex, disease and age.
\nDisaggregation:
\nThe data is available by country, by sex, by disease, and by age.
","COMPARABILITY__GLOBAL":"Sources of discrepancies:
\nUnderlying differences between country produced and internationally estimated data may due to :
\n- Different exposure data (annual mean concentration of particulate matter of less than 2.5 µm of diameter, proportion of population using clean fuels and technology for cooking)
\n- Different exposure-risk estimates
\n- Different underlying mortality data
","OTHER_DOC__GLOBAL":"URL:
\nhttps://www.who.int/data/gho/data/themes/air-pollution
\nReferences:
\nBonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Prüss-Ustün A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. (2013). Solid fuel use for household cooking: country and regional estimates for 1980-2010. Environ Health Perspect. 121(7):784-90. doi: 10.1289/ehp.1205987.
\nBurnett RT, Pope CA 3rd, Ezzati M, Olives C, Lim SS, Mehta S, Shin HH, Singh G, Hubbell B, Brauer M, Anderson HR, Smith KR, Balmes JR, Bruce NG, Kan H, Laden F, Prüss-Ustün A, Turner MC, Gapstur SM, Diver WR, Cohen A. (2014). An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ Health Perspect. 122(4):397-403. doi: 10.1289/ehp.1307049.
\nBurnett R, Cohen A. (2020). Relative Risk Functions for Estimating Excess Mortality Attributable to Outdoor PM2.5 Air Pollution: Evolution and State-of-the-Art. Atmosphere, 11, 589. https://doi.org/10.3390/atmos11060589
\nCohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope CA 3rd, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray CJL, Forouzanfar MH. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 389(10082):1907-1918. doi: 10.1016/S0140-6736(17)30505-6.
\nEzzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ. (2003). Comparative Risk Assessment Collaborating Group. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 362(9380):271-80. doi: 10.1016/s0140-6736(03)13968-2.
\nForouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, Delwiche K, Estep K, Frostad JJ, Astha KC, Kyu HH, Moradi-Lakeh M, Ng M, Slepak EL, Thomas BA, Wagner J, Aasvang GM, Abbafati C, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham B, Abraham JP, Abubakar I, Abu-Rmeileh NM, Aburto TC, Achoki T, Adelekan A, Adofo K, Adou AK, Adsuar JC, Afshin A, Agardh EE, Al Khabouri MJ, Al Lami FH, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Aleman AV, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Ali MK, Alla F, Allebeck P, Allen PJ, Alsharif U, Alvarez E, Alvis-Guzman N, Amankwaa AA, Amare AT, Ameh EA, Ameli O, Amini H, Ammar W, Anderson BO, Antonio CA, Anwari P, Argeseanu Cunningham S, Arnlöv J, Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Atkinson C, Avila MA, Awuah B, Badawi A, Bahit MC, Bakfalouni T, Balakrishnan K, Balalla S, Balu RK, Banerjee A, Barber RM, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Barrientos-Gutierrez T, Basto-Abreu AC, Basu A, Basu S, Basulaiman MO, Batis Ruvalcaba C, Beardsley J, Bedi N, Bekele T, Bell ML, Benjet C, Bennett DA, Benzian H, Bernabé E, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bikbov B, Bin Abdulhak AA, Blore JD, Blyth FM, Bohensky MA, Bora Başara B, Borges G, Bornstein NM, Bose D, Boufous S, Bourne RR, Brainin M, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Broday DM, Brooks PM, Bruce NG, Brugha TS, Brunekreef B, Buchbinder R, Bui LN, Bukhman G, Bulloch AG, Burch M, Burney PG, Campos-Nonato IR, Campuzano JC, Cantoral AJ, Caravanos J, Cárdenas R, Cardis E, Carpenter DO, Caso V, Castañeda-Orjuela CA, Castro RE, Catalá-López F, Cavalleri F, Çavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chen Z, Chiang PP, Chimed-Ochir O, Chowdhury R, Christophi CA, Chuang TW, Chugh SS, Cirillo M, Claßen TK, Colistro V, Colomar M, Colquhoun SM, Contreras AG, Cooper C, Cooperrider K, Cooper LT, Coresh J, Courville KJ, Criqui MH, Cuevas-Nasu L, Damsere-Derry J, Danawi H, Dandona L, Dandona R, Dargan PI, Davis A, Davitoiu DV, Dayama A, de Castro EF, De la Cruz-Góngora V, De Leo D, de Lima G, Degenhardt L, del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, deVeber GA, Devries KM, Dharmaratne SD, Dherani MK, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Durrani AM, Ebel BE, Ellenbogen RG, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Fahimi S, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Feigl AB, Fereshtehnejad SM, Ferrari AJ, Ferri CP, Flaxman AD, Fleming TD, Foigt N, Foreman KJ, Paleo UF, Franklin RC, Gabbe B, Gaffikin L, Gakidou E, Gamkrelidze A, Gankpé FG, Gansevoort RT, García-Guerra FA, Gasana E, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goginashvili K, Gomez Dantes H, Gona P, Gonzalez de Cosio T, González-Castell D, Gotay CC, Goto A, Gouda HN, Guerrant RL, Gugnani HC, Guillemin F, Gunnell D, Gupta R, Gupta R, Gutiérrez RA, Hafezi-Nejad N, Hagan H, Hagstromer M, Halasa YA, Hamadeh RR, Hammami M, Hankey GJ, Hao Y, Harb HL, Haregu TN, Haro JM, Havmoeller R, Hay SI, Hedayati MT, Heredia-Pi IB, Hernandez L, Heuton KR, Heydarpour P, Hijar M, Hoek HW, Hoffman HJ, Hornberger JC, Hosgood HD, Hoy DG, Hsairi M, Hu G, Hu H, Huang C, Huang JJ, Hubbell BJ, Huiart L, Husseini A, Iannarone ML, Iburg KM, Idrisov BT, Ikeda N, Innos K, Inoue M, Islami F, Ismayilova S, Jacobsen KH, Jansen HA, Jarvis DL, Jassal SK, Jauregui A, Jayaraman S, Jeemon P, Jensen PN, Jha V, Jiang F, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Kany Roseline SS, Karam NE, Karch A, Karema CK, Karthikeyan G, Kaul A, Kawakami N, Kazi DS, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khang YH, Khatibzadeh S, Khonelidze I, Kieling C, Kim D, Kim S, Kim Y, Kimokoti RW, Kinfu Y, Kinge JM, Kissela BM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kose MR, Kosen S, Kraemer A, Kravchenko M, Krishnaswami S, Kromhout H, Ku T, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kwan GF, Lai T, Lakshmana Balaji A, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Lawrynowicz AE, Leasher JL, Lee JT, Leigh J, Leung R, Levi M, Li Y, Li Y, Liang J, Liang X, Lim SS, Lindsay MP, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Logroscino G, London SJ, Lopez N, Lortet-Tieulent J, Lotufo PA, Lozano R, Lunevicius R, Ma J, Ma S, Machado VM, MacIntyre MF, Magis-Rodriguez C, Mahdi AA, Majdan M, Malekzadeh R, Mangalam S, Mapoma CC, Marape M, Marcenes W, Margolis DJ, Margono C, Marks GB, Martin RV, Marzan MB, Mashal MT, Masiye F, Mason-Jones AJ, Matsushita K, Matzopoulos R, Mayosi BM, Mazorodze TT, McKay AC, McKee M, McLain A, Meaney PA, Medina C, Mehndiratta MM, Mejia-Rodriguez F, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mendoza W, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Misganaw A, Mishra S, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montañez Hernandez JC, Montico M, Moore AR, Morawska L, Mori R, Moschandreas J, Moturi WN, Mozaffarian D, Mueller UO, Mukaigawara M, Mullany EC, Murthy KS, Naghavi M, Nahas Z, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nash D, Neal B, Nejjari C, Neupane SP, Newton CR, Ngalesoni FN, Ngirabega Jde D, Nguyen G, Nguyen NT, Nieuwenhuijsen MJ, Nisar MI, Nogueira JR, Nolla JM, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Olusanya BO, Omer SB, Opio JN, Orozco R, Pagcatipunan RS Jr, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Parry CD, Paternina Caicedo AJ, Patten SB, Paul VK, Pavlin BI, Pearce N, Pedraza LS, Pedroza A, Pejin Stokic L, Pekericli A, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Perry SA, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phua HP, Plass D, Poenaru D, Polanczyk GV, Polinder S, Pond CD, Pope CA, Pope D, Popova S, Pourmalek F, Powles J, Prabhakaran D, Prasad NM, Qato DM, Quezada AD, Quistberg DA, Racapé L, Rafay A, Rahimi K, Rahimi-Movaghar V, Rahman SU, Raju M, Rakovac I, Rana SM, Rao M, Razavi H, Reddy KS, Refaat AH, Rehm J, Remuzzi G, Ribeiro AL, Riccio PM, Richardson L, Riederer A, Robinson M, Roca A, Rodriguez A, Rojas-Rueda D, Romieu I, Ronfani L, Room R, Roy N, Ruhago GM, Rushton L, Sabin N, Sacco RL, Saha S, Sahathevan R, Sahraian MA, Salomon JA, Salvo D, Sampson UK, Sanabria JR, Sanchez LM, Sánchez-Pimienta TG, Sanchez-Riera L, Sandar L, Santos IS, Sapkota A, Satpathy M, Saunders JE, Sawhney M, Saylan MI, Scarborough P, Schmidt JC, Schneider IJ, Schöttker B, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serdar B, Servan-Mori EE, Shaddick G, Shahraz S, Levy TS, Shangguan S, She J, Sheikhbahaei S, Shibuya K, Shin HH, Shinohara Y, Shiri R, Shishani K, Shiue I, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh GM, Singh JA, Skirbekk V, Sliwa K, Soljak M, Soneji S, Søreide K, Soshnikov S, Sposato 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