Impacts of residential indoor air quality and environmental risk factors on adult asthma-related health outcomes in Chicago, IL

Kang, In-sung; McCreery, Anna C.; Azimi, Parham; Gramigna, Amanda; Baca, Griselda; Hayes, Whitney; Crowder, Timothy M.; Scheu, Rachel; Evens, Anne; Stephens, Brent

doi:10.1038/s41370-022-00503-z

Cited by 12 publications

(8 citation statements)

References 58 publications

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“…Similar findings were found in a recent cross-sectional study reporting a higher prevalence of asthma in the children of an Indian village, where both biomass and liquefied petroleum gas fuels were used for cooking [19]. Furthermore, a relationship between PM levels and asthma cases, especially in the dry season [20], as well as with acute asthma exacerbations [21] was also highlighted.…”

Section: Pmsupporting

confidence: 85%

“…Similarly, a study conducted on adult asthmatic patients showed worse disease control, but not a major risk of developing asthma, in those exposed to higher concentrations of NO 2 in a home environment [21]. A study conducted among Nigerian children found no significant association between indoor exposure to NO 2 and SO 2 and increased prevalence of asthma, in contrast to what was reported for higher concentrations of household CO [20]. On the contrary, Taiwanese children living next to a petrochemical complex and exposed to high levels of SO 2 had significantly higher incidences of asthma, bronchitis, and AR [48].…”

Section: Chemical Gasesmentioning

confidence: 93%

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Indoor air pollution and atopic diseases: a comprehensive framework

Ridolo,

Pederzani,

Barone

et al. 2024

Explor Asthma Allergy

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Indoor air pollution (IAP) is an important cause of concern for human health, leading to millions of deaths worldwide each year. Since people spend most of their time indoor the quality of the air inhaled during routine activities is of primary importance. IAP include particulate matter (PM), volatile organic compounds (VOCs), chemical gases, heavy metals, and biological contaminants. Unfortunately, their sources are various and widespread all over the household and other indoor environments, causing relevant health consequences. This narrative review aims to provide a comprehensive framework of the indoor pollutants effects on subjects affected by asthma, allergic rhinitis, and atopic dermatitis. As pivotal barriers against pollutants, in fact, respiratory and cutaneous districts can be particularly affected by IAP, especially in case of atopic diseases. On the other hand, the application of targeted adjustments, such as the avoidance of cigarette smoking, the use of hoods while cooking, the choice of adequate ventilation systems, and the use of low-emitting building materials and furniture may result in the improvement of indoor quality.

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Section: Pmsupporting

confidence: 85%

Section: Chemical Gasesmentioning

confidence: 93%

Indoor air pollution and atopic diseases: a comprehensive framework

Ridolo,

Pederzani,

Barone

et al. 2024

Explor Asthma Allergy

View full text Add to dashboard Cite

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“…Of course, these housing quality indicators are only proxy estimates for each individual patient's actual housing conditions and exposures, such as indoor air quality, first, second, or third- hand smoke exposure, and mold or pest exposure [47][48][49][50]. Given a subset of patients' actual housing and indoor air quality, we could work to identify which, if any, SDC datasets could serve as the best proxy [51].…”

Section: Analysis Discussionmentioning

confidence: 99%

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health

Tilmon,

Nyenhuis,

Solomonides

et al. 2023

J. Clin. Trans. Sci.

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Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

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“…Then, the data is further enhanced with a second LSTM layer with 16 units for feature extraction. Finally, the model utilizes a TCN network to extract temporal features, where the TCN network has 64 filters, a kernel size of 2, a dilation rate of [1,2,4], layer normalization, and a dropout rate of 0.2. The processed data passes through a global max-pooling layer and a fully connected layer to obtain the target gas concentration prediction.…”

Section: Proposed Lstm-tcn Modelmentioning

confidence: 99%

“…As people become more aware of environmental protection, there is an increasing demand for indoor environmental quality. However, there may be harmful gases in the air that have negative effects on people's health [1][2] . Prediction of mixed gas concentrations is one of the most important applications in environmental monitoring and air quality control [3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

Fusion Neural Network for Gas Concentration Prediction in Mixed Gas Environments

Li,

et al. 2023

J. Phys.: Conf. Ser.

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Due to the inherent complexity and nonlinearity of mixed gas data, existing pattern recognition algorithms utilized in electronic noses often encounter difficulties in accurately predicting gas concentrations. Addressing this issue, we propose a fusion neural network that merges Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), which we denote as the LSTM-TCN fusion model. The LSTM module effectively captures long-term dependencies in time-series data, while the TCN targets local correlations, thereby enhancing the prediction accuracy for complex gas concentrations. Experimental validation was conducted using a mixed gas dataset comprising ethylene and carbon monoxide. When compared with traditional models, including LSTM, TCN, and GRU, the proposed LSTM-TCN model demonstrated superior performance, achieving an R2 value as high as 0.9922. This research holds considerable practical significance and shows promising application prospects, contributing novel insights and methods to the study and application of electronic nose technology.

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Impacts of residential indoor air quality and environmental risk factors on adult asthma-related health outcomes in Chicago, IL

Cited by 12 publications

References 58 publications

Indoor air pollution and atopic diseases: a comprehensive framework

Indoor air pollution and atopic diseases: a comprehensive framework

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health

Fusion Neural Network for Gas Concentration Prediction in Mixed Gas Environments

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