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Within cities, environmental characteristics can modify heat exposure. Using accessible indicators such as tree canopy coverage (TCC) and building fraction (BF) may improve understanding of how residential environments shape heat-related mortality and support equitable climate adaptation. In this study, we examined associations between residential neighborhood TCC, BF, and heat-related mortality among adults aged\u2009\u2265\u200965\u00a0years using daily maximum air temperature as the exposure. This was done for Sweden\u2019s three largest urban areas, Stockholm, Gothenburg, and Malm\u00f6, from the years 2006 to 2019. Daily all-cause mortality was analyzed using distributed lag non-linear models (DLNMs), with a lag of 0\u20134\u00a0days. Analyses were aggregated by levels of high and low TCC and BF. Heat-related mortality was calculated both at the 99th percentile (28.1\u201329.0\u00a0\u00b0C), and at 30\u00a0\u00b0C, and compared with the minimum mortality temperature (MMT) to estimate relative risk (RR). Compared with the MMT, overall mortality increased by 15\u201317% at the 99th percentile and by 24\u201332% at 30\u00a0\u00b0C. The results also suggested a trend towards higher heat-related mortality in neighborhoods with low TCC and high BF, compared with areas with high TCC and low BF, with relative risk differences of 4\u201321% for TCC and 0\u201322% for BF. These findings highlight the importance of residential urban environment for protecting older adults during heat extremes and support the use of simple indicators to inform equitable urban health and climate adaptation strategies.<\/jats:p>","DOI":"10.1007\/s11524-026-01146-3","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:43:45Z","timestamp":1790865825000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neighborhood Tree Canopy Coverage and Building Fraction as Modifiers for Heat-related Mortality among Older Adults in Sweden's Largest Urban Areas"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2547-3208","authenticated-orcid":false,"given":"Jessika","family":"L\u00f6nn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christofer","family":"\u00c5str\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fredrik","family":"Lindberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia","family":"Thorsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"1146_CR1","doi-asserted-by":"publisher","unstructured":"Seneviratne SI, Zhang X, Adnan M, et al. 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