Kanda Satoru, Watanabe Kaname, Nakamura Sho, Narimatsu Hiroto
Department of Clinical Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan.
Cancer Prevention and Control Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa, Japan.
PLoS One. 2025 Jul 9;20(7):e0326895. doi: 10.1371/journal.pone.0326895. eCollection 2025.
Information on the association between socioeconomic status (SES) and cancer is useful for policy-based cancer control. However, few studies have investigated the association between each community SES indicator and cancer. Therefore, here, we investigated the relationship between community land price, neighborhood income, education level, employment rate, and morbidity and mortality rates for lung, stomach, colorectal, liver, and breast cancers. We obtained cancer patient data from the Kanagawa Cancer Registry and SES indicator data from public databases from 2000 to 2016. We classified the data according to the year, sex, and community. Poisson regression analyses were conducted for each SES indicator, using one SES indicator as the explanatory variable and the morbidity or mortality of cancer as the response variable. The largest inverse regression coefficient for the community SES indicator was -0.91 (95% CI -1.11, -0.70) found in a model where liver-cancer mortality was the response variable and employment rate was the explanatory variable for women. Community neighborhood income and employment rate demonstrated significant inverse associations across many models. Areas with low community neighborhood income or employment rates may have more individuals at a higher risk of cancer; these SES data could help to identify locations where cancer control should be focused.
社会经济地位(SES)与癌症之间关联的信息对基于政策的癌症防控很有用。然而,很少有研究调查每个社区SES指标与癌症之间的关联。因此,在此我们研究了社区土地价格、邻里收入、教育水平、就业率与肺癌、胃癌、结直肠癌、肝癌和乳腺癌的发病率及死亡率之间的关系。我们从神奈川癌症登记处获取了癌症患者数据,并从2000年至2016年的公共数据库中获取了SES指标数据。我们根据年份、性别和社区对数据进行了分类。对每个SES指标进行了泊松回归分析,将一个SES指标作为解释变量,将癌症的发病率或死亡率作为响应变量。在以肝癌死亡率为响应变量、就业率为女性解释变量的模型中,发现社区SES指标的最大反向回归系数为-0.91(95%置信区间-1.11,-0.70)。社区邻里收入和就业率在许多模型中都显示出显著的反向关联。社区邻里收入或就业率较低的地区可能有更多患癌风险较高的个体;这些SES数据有助于确定癌症防控应重点关注的地点。