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Journal ArticleDOI: 10.1111/INA.12790

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

02 Mar 2021-Indoor Air (Wiley)-Vol. 31, Iss: 5, pp 1441-1457
Abstract: This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM2.5 , black carbon, CO); household air pollution (PM2.5 , CO); personal exposure (PM2.5 , CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM2.5 (R2 = 0.45) and CO (R2 = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R2 = 0.76) and a root mean-squared error of 85 µg/m3 , and the survey-only-based regression model was able to predict PM2.5 exposures with an R2 of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R2 of 0.57 and RMSE of 98 µg/m3 .

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Topics: Regression analysis (51%)
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5 results found



Journal ArticleDOI: 10.1016/J.ENPOL.2021.112263
Eric Hsu1, Noah Forougi1, Meixi Gan, Elizabeth Muchiri  +2 moreInstitutions (2)
01 Jul 2021-Energy Policy
Abstract: Liquidity constraints are a key barrier to acquisition and sustained use of clean household energy in resource-poor settings. This study evaluates a pilot microfinance initiative in Kenya to help low-income rural households access liquefied petroleum gas (LPG) for cooking. Program beneficiaries received a six-month loan that covered all equipment costs and was to be repaid in monthly installments. We present results from surveys of beneficiaries (n = 69) after they began using LPG, as well as 332 non-beneficiaries from the same community (to understand how beneficiaries and non-beneficiaries differ in cooking patterns and socioeconomic outcomes). 94% of beneficiaries had repaid their loan in full and on time at the time of data collection. Meanwhile, beneficiaries were more likely than non-beneficiaries to use LPG as their primary cooking fuel (76.8% of beneficiaries versus 38.8% of non-beneficiaries). While 81.1% of beneficiaries who used LPG as their primary cooking fuel reported multiple fuel use, we find beneficiaries increased LPG use by 5.9 h per week with a corresponding decrease of 4.8 h in weekly use of biomass fuel. Our findings suggest that promoting LPG usage through microloans for equipment is likely to be both commercially viable and beneficial to health through decreased use of polluting biomass fuel.

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4 Citations


Open accessPosted ContentDOI: 10.1101/2021.11.04.21265938
05 Nov 2021-medRxiv
Abstract: Background: Exposure to PM2.5 arising from solid fuel combustion is estimated to result in approximately 2.3 million premature deaths and 90 million lost disability-adjusted life years annually. 9Clean9 cooking interventions attempting to mitigate this burden have had limited success in reducing exposures to levels that may yield improved health outcomes. Objectives: This paper reports exposure reductions achieved by a liquified petroleum gas (LPG) stove and fuel intervention for pregnant mothers in the Household Air Pollution Intervention Network (HAPIN) randomized controlled trial. Methods: The HAPIN trial included 3195 households primarily using biomass for cooking in Guatemala, India, Peru, and Rwanda. 24-hour exposures to PM2.5, carbon monoxide (CO), and black carbon (BC) were measured for pregnant women once before randomization into control (n=1605) and LPG arms (n=1590) and twice thereafter (aligned with trimester). Changes in exposure were estimated by directly comparing exposures between intervention and control arms and by using linear mixed-effect models to estimate the impact of the intervention on exposure levels. Results: Median exposures of PM2.5, BC, and CO post-randomization in the intervention arm were lower by 66% (70.7 versus 24.0 µg/m3), 71% (9.6 versus 2.8 µg/m3), and 83% (1.2 versus 0.2 ppm), respectively, compared to the control arm. Exposure reductions were similar across research locations. Post-intervention PM2.5 exposures in the intervention arm were at the lower end of what has been reported for LPG and other clean fuel interventions, with 69% of PM2.5 samples falling below the WHO Annual Interim Target 1 of 35 µg/m3. Discussion: This study indicates that an LPG intervention with high displacement of traditional cooking can reduce exposures to levels thought to be associated with health benefits. Success in reducing exposures was likely due to strong performance of, and high adherence to the intervention. Keywords (5-8): Liquefied petroleum gas, clean cooking, intervention, exposure assessment, PM2.5

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Journal ArticleDOI: 10.1016/J.ESD.2021.05.004
Natalia Ortega, Ariadna Curto1, Asya Dimitrova1, Jovito Nunes  +3 moreInstitutions (2)
Abstract: Background Lighting sources have been overlooked in previous estimates of the health burden attributable to household air pollution (HAP). However, lighting sources can be an important predictor of personal exposure to HAP in countries with limited access to electricity. We modeled the health and environmental impacts of hypothetical intervention scenarios that replace kerosene-based lighting with renewable electricity in East Africa. Methods We used comparative risk assessment methods to quantify the ischemic heart disease-, stroke-, lung cancer-, chronic obstructive pulmonary disease- and lower respiratory infection-related morbidity and mortality attributable to personal fine particulate matter (PM2.5) exposure due to kerosene-based lighting for residents of East Africa in 2015. We used health and demographic data from the Global Burden of Disease; PM2.5 exposure estimates from a previous study replacing kerosene-based lighting with solar lighting; and exposure-response functions from the literature to estimate the number of deaths and DALYs that could be avoided with increased scaling-up of the lighting source intervention. We estimated avoided black carbon emissions using emission factors from published literature. Results We estimated that 6218, 10,092, 12,723 deaths and 409, 654, 814 hundred thousand DALYs could be avoided if 33%, 66%, and 100% respectively of households using kerosene-based lighting replaced it with electricity in East Africa in 2015. Including lighting fuel in estimates of deaths due to PM2.5 from household air pollution in the region would add a further 9% in addition to those from cooking with solid fuel. Full replacement of kerosene-based lighting with renewable electricity would have reduced black carbon emissions by 4.4 Gg/year or 3957 CO2eq Gg in 2015, with the largest emission reductions in Uganda and Kenya. Conclusion Kerosene-based lighting is a non-negligible source of HAP in the region and should be included in assessments of the health burden attributable to HAP.

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Journal ArticleDOI: 10.1016/J.BUILDENV.2021.108325
Darpan Das1, Emma Moynihan1, Mark Nicas2, Eric D. McCollum1  +7 moreInstitutions (2)
Abstract: Household air pollution due to solid fuel (biomass) combustion is widely prevalent in rural households in the developing world. Providing adequate ventilation can be a potential method to reduce exposures to residents. Previous cookstove studies in rural areas around the world have estimated the ventilation air changes per hour (ACH) values to be of the order of >20. These studies use a one-compartment model to estimate the ACH from the decay of the pollutant released very near to the cookstove. While the one-box compartmental model is appropriate for estimating exposures farther away from emission sources, a multi-compartment (e.g., a 2-box model) may be more appropriate for distinguishing between exposures of the cook versus other occupants in the house, as well as estimating ventilation rates in the house. In the present study, we use a two-compartment model to estimate the ACH. Field based particulate matter measurements were carried out in 40 Bangladesh rural households in kitchen and living room. The overall Geometric Mean (Geometric Standard Deviation) of ACH across households were found to be 0.43 (4.3) in the kitchen and 0.32 (2.7) in the living room. Obtaining the air changes per hour or ventilation rates from the decay curve of concentrations in the near vicinity of the pollutant source will lead to significant over-estimates. Our findings indicate that there is currently a substantial over-estimate based on using an incorrect model to derive the ACH values.

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46 results found


Open accessJournal ArticleDOI: 10.1093/HEAPOL/CZL029
Seema Vyas1, Lilani KumaranayakeInstitutions (1)
Abstract: Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

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Topics: Population (51%), Socioeconomic status (50%)

2,414 Citations


Open accessJournal ArticleDOI: 10.1016/S0140-6736(18)32225-6
Jeffrey D. Stanaway1, Ashkan Afshin1, Emmanuela Gakidou1, Stephen S Lim1  +1050 moreInstitutions (346)
10 Nov 2018-The Lancet
Abstract: 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 and Bloomberg Philanthropies.

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Topics: Environmental exposure (60%), Disease burden (60%), Risk assessment (55%) ... show more

1,790 Citations


Journal ArticleDOI: 10.4103/2229-5186.79345
Abstract: The quality of an analytical method developed is always appraised in terms of suitability for its intended purpose, recovery, requirement for standardization, sensitivity, analyte stability, ease of analysis, skill subset required, time and cost in that order. It is highly imperative to establish through a systematic process that the analytical method under question is acceptable for its intended purpose. Limit of detection (LOD) and limit of quantification (LOQ) are two important performance characteristics in method validation. LOD and LOQ are terms used to describe the smallest concentration of an analyte that can be reliably measured by an analytical procedure. There has often been a lack of agreement within the clinical laboratory field as to the terminology best suited to describe this parameter. Likewise, there have been various methods for estimating it. The presented review provides information relating to the calculation of the limit of detection and limit of quantitation. Brief information about differences in various regulatory agencies about these parameters is also presented here.

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Topics: Detection limit (53%), Analyte (51%)

1,687 Citations


Open accessJournal ArticleDOI: 10.1289/EHP.1205987
Abstract: Background: Exposure to household air pollution from cooking with solid fuels in simple stoves is a major health risk. Modeling reliable estimates of solid fuel use is needed for monitoring trends ...

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Topics: Solid fuel (55%), Environmental exposure (55%), Stove (53%)

602 Citations


Open accessJournal ArticleDOI: 10.1146/ANNUREV-PUBLHEALTH-032013-182356
Abstract: 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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Topics: Risk assessment (56%)

497 Citations