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Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey
Household-level information on central (cenAC) and room (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information fr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483423/ https://www.ncbi.nlm.nih.gov/pubmed/32203058 http://dx.doi.org/10.1038/s41370-020-0220-8 |
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author | Gronlund, Carina J. Berrocal, Veronica J. |
author_facet | Gronlund, Carina J. Berrocal, Veronica J. |
author_sort | Gronlund, Carina J. |
collection | PubMed |
description | Household-level information on central (cenAC) and room (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003–2013 and 140 U.S. core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or “home was uncomfortably cold for 24 hours or more” (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015–2017 were compared to corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC. |
format | Online Article Text |
id | pubmed-7483423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-74834232020-09-13 Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey Gronlund, Carina J. Berrocal, Veronica J. J Expo Sci Environ Epidemiol Article Household-level information on central (cenAC) and room (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003–2013 and 140 U.S. core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or “home was uncomfortably cold for 24 hours or more” (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015–2017 were compared to corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC. 2020-03-13 2020-09 /pmc/articles/PMC7483423/ /pubmed/32203058 http://dx.doi.org/10.1038/s41370-020-0220-8 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Gronlund, Carina J. Berrocal, Veronica J. Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title | Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title_full | Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title_fullStr | Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title_full_unstemmed | Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title_short | Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey |
title_sort | modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the american housing survey |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483423/ https://www.ncbi.nlm.nih.gov/pubmed/32203058 http://dx.doi.org/10.1038/s41370-020-0220-8 |
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