Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS - a cross-sectional study in Iran

Sep 21, 2026

Uploaded by Aurora

Study evaluates how socioeconomic and environmental indicators relate to age-adjusted incidence rate (AAIR) of neoplasms across Iran, using county-level data from 2010 to 2020. Spatiotemporal variations are assessed across five time periods, then HotSpot analyses and Anselin Local Moran’s I identify high- and low-risk clusters. Multi-scale geographically weighted regression (MGWR) quantifies spatially varying associations for each explanatory variable. Random forests (RF) further model relationships between environmental factors (e.g., UV index, PM2.5) and socioeconomic factors (e.g., Gini coefficient, literacy rate), achieving high R2 on training and testing sets.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Study aims and design

Spatiotemporal variation and clustering

MGWR and machine learning modeling

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross‑sectional study in Iran Mohammad Rafiee1,2, Mahsa Jahangiri‑rad3,4*, Anoushiravan Mohseni‑Bandpei1,2 & Elham Razmi 5 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 theAAIR 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 theAAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors andAAIR 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 theAAIR 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. Keywords Incidence rate of neoplasms, Multiscale geographical weighted regression, Random forest, Risk factors, Spatiotemporal analysis In 2019, neoplasms—a type of aberrant and excessive tissue growth1—were projected to have disability adjusted life years (DALYs) of 4,239 per 100,000 people globally2. Neoplasia is the term used to describe the process by which tumor forms or is produced3,4. 1Air Quality and Climate Change Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 3Department of Environmental Health Engineering, School of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran. 4Water Purification Research Center, Islamic Azad University, Tehran, Iran. 5Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.*[email: m.jahangiri@iautmu.ac.ir](email: m.jahangiri@iautmu.ac.ir) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) Neoplasms are categorized by ICD-10 (a medical classification list by the World Health Organization [WHO]) into four primary groups, namely benign, malignant, in situ, and neoplasms with unclear or unknown activity4. Cancers, usually referred to as malignant neoplasms, are the main focus o

FAQs

What are the study’s main objectives?

The study analyzes spatiotemporal changes in AAIR of neoplasms across five periods, examines relationships between environmental and socioeconomic indicators and AAIR nationwide, and evaluates how the relative importance of factors changes geographically.

How were high- and low-risk clusters identified?

HotSpot analyses and Anselin Local Moran’s I indices were used to locate AAIR of neoplasms high- and low-risk clusters.

Which factors were most strongly associated with AAIR of neoplasms?

MGWR indicates the UV index is the only factor that significantly varies spatially and is associated with AAIR of neoplasms in Iran. Random forests also rank UV index and the Gini coefficient highly for prediction.

Sep 21, 2026

Uploaded by Aurora