Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM_2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R² greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

肿瘤是一个总称术语，用于描述良性或恶性疾病。大量研究已经证明了社会经济和环境因素与新发肿瘤发生之间的相关性。然而，很少有研究专门讨论关系的性质、风险因素的重要性以及它们的地理差异，特别是在低收入和中等收入社区。因此，本研究旨在 (1) 分析伊朗肿瘤年龄调整发病率 (AAIR) 在五个时期内的时空变化，(2) 调查一系列环境和社会经济指标与伊朗肿瘤年龄调整发病率 (AAIR) 之间的关系。全国各地的肿瘤，以及（3）评估其相对重要性的地理变化。我们的横断面研究设计基于 2010 年至 2020 年的县级数据。肿瘤数据的 AAIR 是从健康指标与评估研究所 (IHME) 获得的。采用热点分析和 Anselin Local Moran's I 指数来精确识别肿瘤高风险和低风险集群的 AAIR。进行多尺度地理权重回归（MGWR）分析来评估每个解释变量与肿瘤 AAIR 之间的关联。利用随机森林（RF），我们还研究了环境（例如，紫外线指数和PM _2.5浓度）和社会经济（例如，基尼系数和识字率）因素与肿瘤的AAIR之间的关系。在研究期间，肿瘤的 AAIR 显示出显着增加的趋势。根据 MGWR 的数据，唯一在空间上存在显着差异且与伊朗肿瘤 AAIR 相关的因素是紫外线指数。训练和测试数据均证实了良好精度的 RF 模型，相关系数R ²分别大于 0.91 和 0.92。根据各变量的相对影响力，UV指数和基尼系数在肿瘤AAIR预测中排名最高的变量。需要利用机器学习方法进行更多研究，以充分考虑所有可能的决定因素，以评估健康策略的结果并正确制定政策规划。