Loehrer Andrew P, Carlos Heather A, Weiss Julia E, Leversedge Chelsea V, Katter Julia E, Phillips Joseph D, Ferrari-Light Dana, Onega Tracy, Shi Xun
Dartmouth-Hitchcock Medical Center.
Geisel School of Medicine at Dartmouth.
Res Sq. 2025 Aug 22:rs.3.rs-7330140. doi: 10.21203/rs.3.rs-7330140/v1.
Cancer control relies on the identification of populations at risk (hotspots) of new or late-stage cancer diagnoses. However, the extent to which hotspots differ between cancer sites or between outcome measures has been poorly characterized. We sought to determine the geospatial heterogeneity of hotspots of breast, colorectal, and lung cancer incidence and late-stage diagnoses.
We identified adult patients diagnosed with female breast, colorectal, and lung cancer between 2010 and 2019 in Indiana. To identify hotspots for incidence and late-stage diagnoses, we disaggregated the patient residential location information from the Census block group level to the approximated individual point level. Statistically significant hotspots were identified with kernel ratio estimation. Total areas of hotspots and overlap between hotspots were compared.
117,305 patients diagnosed with breast (n=51,623), colorectal (n=25,160), and lung (n=37,522) cancer were included. Geospatial visualization demonstrated marked spatial deviation, with little overlapping area between incidence and late-stage hotspots for all three cancer sites (32km - 165km). However, there was greater overlap in late-stage hotspots between the different cancer sites, with total overlapping hotspot areas ranging from 408km - 1046km.
Our results demonstrate considerable geospatial heterogeneity of hotspots between different outcome measures and different sites of cancer. However, there are greater overlapping areas of late-stage hotspots, especially for breast and lung cancer. The use of disaggregated spatial data enables more granular, precise comparison of cancer hotspots. Greater overlap between late-stage breast and lung cancer suggests similar spatial drivers and the potential for coordinated cancer control interventions.
癌症控制依赖于识别新诊断或晚期癌症的高危人群（热点地区）。然而，不同癌症部位或不同结局指标之间热点地区的差异程度尚未得到充分描述。我们试图确定乳腺癌、结直肠癌和肺癌发病率及晚期诊断热点地区的地理空间异质性。
我们识别了2010年至2019年期间在印第安纳州被诊断为女性乳腺癌、结直肠癌和肺癌的成年患者。为了识别发病率和晚期诊断的热点地区，我们将患者居住位置信息从人口普查街区组层面细化到近似的个人点层面。通过核比率估计确定具有统计学意义的热点地区。比较热点地区的总面积和热点地区之间的重叠情况。
纳入了117305例被诊断为乳腺癌（n = 51623）、结直肠癌（n = 25160）和肺癌（n = 37522）的患者。地理空间可视化显示出明显的空间偏差，所有三个癌症部位的发病率和晚期热点地区之间几乎没有重叠区域（32公里 - 165公里）。然而，不同癌症部位的晚期热点地区之间重叠更大，总重叠热点地区面积在408公里 - 1046公里之间。
我们的结果表明，不同结局指标和不同癌症部位之间的热点地区存在相当大的地理空间异质性。然而，晚期热点地区的重叠区域更大，尤其是乳腺癌和肺癌。使用细化的空间数据能够对癌症热点地区进行更细致、精确的比较。晚期乳腺癌和肺癌之间更大的重叠表明存在相似的空间驱动因素以及实施协调癌症控制干预措施的潜力。
