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      Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study

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          Summary

          Background

          Approximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM 2·5] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM 2·5 and black carbon in rural communities with a wide range of cooking environments.

          Methods

          As part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM 2·5 measurements were collected. Light absorbance (10 −5m −1) of the PM 2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period.

          Findings

          Monitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM 2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 μg/m 3 [95% CI 43–48]), electricity (53 μg/m 3 [47–60]), coal (68 μg/m 3 [61–77]), charcoal (92 μg/m 3 [58–146]), agricultural or crop waste (106 μg/m 3 [91–125]), wood (109 μg/m 3 [102–118]), animal dung (224 μg/m 3 [197–254]), and shrubs or grass (276 μg/m 3 [223–342]). Among households cooking primarily with wood, average PM 2·5 concentrations varied ten-fold (range: 40–380 μg/m 3). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM 2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM 2·5 personal exposures between women (67 μg/m 3 [95% CI 62–72]) and men (62 [58–67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM 2·5 (0·79 [95% 0·71–0·88] for men and 0·82 [0·74–0·91] for women) and black carbon (0·64 [0·45–0·92] for men and 0·68 [0·46–1·02] for women).

          Interpretation

          Using clean primary fuels substantially lowers kitchen PM 2·5 concentrations. Importantly, average kitchen and personal PM 2·5 measurements for all primary fuel types exceeded WHO’s Interim Target-1 (35 μg/m 3 annual average), highlighting the need for comprehensive pollution mitigation strategies.

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          Most cited references27

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          Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. Funding Bill & Melinda Gates Foundation.
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            Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution.

            In the Comparative Risk Assessment (CRA) done as part of the Global Burden of Disease project (GBD-2010), the global and regional burdens of household air pollution (HAP) due to the use of solid cookfuels, were estimated along with 60+ other risk factors. This article describes how the HAP CRA was framed; how global HAP exposures were modeled; how diseases were judged to have sufficient evidence for inclusion; and how meta-analyses and exposure-response modeling were done to estimate relative risks. We explore relationships with the other air pollution risk factors: ambient air pollution, smoking, and secondhand smoke. We conclude with sensitivity analyses to illustrate some of the major uncertainties and recommendations for future work. We estimate that in 2010 HAP was responsible for 3.9 million premature deaths and ∼4.8% of lost healthy life years (DALYs), ranking it highest among environmental risk factors examined and one of the major risk factors of any type globally.
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              The Prospective Urban Rural Epidemiology (PURE) study: examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries.

              Marked changes in the prevalence of noncommunicable diseases such as obesity, diabetes, and cardiovascular disease have occurred in developed and developing countries in recent decades. The overarching aim of the study is to examine the relationship of societal influences on human lifestyle behaviors, cardiovascular risk factors, and incidence of chronic noncommunicable diseases. The Prospective Urban Rural Epidemiology (PURE) study is a large-scale epidemiological study that plans to recruit approximately 140,000 individuals residing in >600 communities in 17 low-, middle-, and high-income countries around the world. Individual data collection includes medical history, lifestyle behaviors (physical activity and dietary profile), blood collection and storage for biochemistry and future genetic analysis, electrocardiogram, and anthropometric measures. In addition, detailed information is being collected with respect to 4 environmental domains of interest-the built environment, nutrition and associated food policy, psychosocial/socioeconomic factors, and tobacco environment. A minimum follow-up of 10 years is currently planned. This report describes the design, justification, and methodology of the PURE study. The PURE study has been recruiting since 2002 and has enrolled 139,506 individuals by March 31, 2009. The PURE study builds on the work and experience gained through conduct of the INTERHEART study. Its design and extensive data collection are geared toward addressing major questions on causation and development of the underlying determinants of cardiovascular disease in populations at varying stages of epidemiologic transition.
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                Author and article information

                Contributors
                Journal
                101704339
                46425
                Lancet Planet Health
                Lancet Planet Health
                The Lancet. Planetary health
                2542-5196
                11 October 2020
                October 2020
                27 October 2020
                : 4
                : 10
                : e451-e462
                Affiliations
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; Department of Public Health and Policy, University of Liverpool, Liverpool, UK
                College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
                Access Sensors Technologies, Fort Collins, CO, USA
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
                Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
                Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
                Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
                Universidad de La Frontera, Temuco, Chile
                Universidad de La Frontera, Temuco, Chile
                Universidad de La Frontera, Temuco, Chile
                Universidad de Santander (UDES), Bucaramanga, Colombia
                FOSCAL, Floridablanca, Colombia
                Universidad Militar Nueva Granada, Bogota, Colombia
                Pamoja Tunaweza Research Centre, Moshi, Tanzania; Department of Medicine, Queen’s University, Kingston, ON, Canada
                Pamoja Tunaweza Research Centre, Moshi, Tanzania
                Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
                Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
                Department of Physiology, University of Zimbabwe, Harare, Zimbabwe
                School of Life Sciences, Independent University, Dhaka, Bangladesh
                School of Life Sciences, Independent University, Dhaka, Bangladesh
                Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
                Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
                Medical Research & Biometrics Center, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
                Jockey Club School of Public health and Primary Care, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
                Madras Diabetes Research Foundation, Chennai, India
                Madras Diabetes Research Foundation, Chennai, India
                Eternal Heart Care Centre & Research Institute, Jaipur, India
                Mahatma Gandhi Medical College, Jaipur, India
                St John’s Medical College & Research Institute, Bangalore, India
                St John’s Medical College & Research Institute, Bangalore, India
                Health Action By People, Thiruvananthapuram and Medical College, Trivandrum, India
                Health Action By People, Thiruvananthapuram and Medical College, Trivandrum, India
                Post Graduate Institute of Medical Education and Research, Chandigarh, India
                Post Graduate Institute of Medical Education and Research, Chandigarh, India
                Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
                Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
                Department of Community Health Science, Aga Khan University Hospital, Karachi, Pakistan
                Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
                School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
                Author notes
                Correspondence to: Matthew Shupler, School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada, mshupler@ 123456mail.ubc.ca

                Contributors

                MS assisted with protocol development, led survey design, managed, cleaned, analysed, and interpreted all data, and wrote the first and final drafts of the Article. PH and MB designed and supervised the conduct of the PURE-Air study, supervised the data analysis and interpretation of the data, and reviewed and commented on all drafts and the final Article. AB led protocol development and assisted with study logistics. DM-L assisted with the monitoring equipment, data quality control and provided input on the final Article. MJ was in charge of laboratory analysis of the data. REA assisted with study design and reviewed and commented on the final Article. YLC assisted with data management and study logistics. SY designed and supervised the conduct of the PURE study and reviewed and commented on the final Article. MM and LH assisted with data management and study logistics. SR coordinated the worldwide study and reviewed and commented on the final Article. All other authors coordinated the study in their respective countries and commented on the final Article.

                [*]

                Members of the PURE-AIR study are listed in the appendix

                Article
                NIHMS1635947
                10.1016/S2542-5196(20)30197-2
                7591267
                33038319
                f8b10c2f-4718-4299-876a-e27abb0f77fc

                This is an Open Access article under the CC BY 4.0 license. http://creativecommons.org/licenses/by/4.0/

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