Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study
Bibliographic record
Abstract
Background: Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies. Objective: This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective. Methods: The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients. Results: In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95% CI 0.65-1.58) to 24.53 (95% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95% CI 2.67-3.91) to 43.70 (95% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of -1.43 (95% CI -1.58 to -1.27) and -1.76 (95% CI -2.08 to -1.43) in males, and -1.93 (95% CI -2.11 to -1.75) and -1.79 (95% CI -2.13 to -1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98). Conclusions: Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".