Evidence map›Paper›PMID 42226955›Full record

ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2024

Geospatial Analysis of Socioeconomic Equity and Environmental Factors Influencing Lung Cancer Prevalence in US.

Mridula Mavuri, Subhajit Chakrabarty

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mridula MavuriComputer Science, Louisiana State University Shreveport, Louisiana, USA.
Subhajit ChakrabartyComputer Science, Louisiana State University Shreveport, Louisiana, USA.

Funding

AIM-AHEAD Coordinating Center - All Four CoresOT2OD032581 · OD · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Paul Avillach, Bettina M. Beech · 2021 to 2026
$168.7M
NIH HHS OT2 OD032581
6 · The paper itself

Abstract

This study investigates the complex interplay of socioeconomic and environmental factors contributing to lung cancer rates across US counties and parishes. The primary objectives are to predict lung cancer rates based on selected socioeconomic and environmental factors and to identify spatial clusters that reflect variations in these rates. Prior studies have explored how socioeconomic variables-such as income, insurance coverage, and smoking rates-along with environmental pollution (PM 2.5), influence lung cancer rates across different regions. This study employs a multi-stage methodology using geospatial data from various US parishes. We apply Multiscale Geographically Weighted Regression (MGWR), Spatial Autoregressive Models (SAR), and Kriging techniques to predict lung cancer rates, followed by K-means clustering based on predicted values. The results are visualized using Folium, and variable significance is assessed through P-values. Our analysis reveals several key findings. Smoking levels show a strong positive correlation with lung cancer rates, particularly in regions with higher smoking rates. Exposure to PM 2.5 significantly contributes to lung cancer risk, especially in areas with higher pollution levels. Poverty has a mixed effect, with some regions showing a negative association with lung cancer rates while others show minimal impact. Insurance coverage is linked with higher lung cancer rates in certain clusters, reflecting better access to healthcare or more comprehensive cancer reporting. Income does not show a strong or consistent effect across regions. This research provides crucial insights into lung cancer's socioeconomic equity and environmental determinants, suggesting targeted public health interventions to mitigate risks in vulnerable communities. By highlighting the significance of smoking and air quality, this study informs policymakers and health organizations on crafting effective strategies for lung cancer prevention tailored to specific regional contexts. The methodologies and findings from this study can be applied to other health-related issues, enabling further exploration of the socioeconomic equity impacts on various diseases and facilitating improved public health planning.

Indexed as

Air QualityEnvironmental FactorsGeospatial AnalysisHealthcare AccessHealth DisparitiesInteractive MappingK-means ClusteringLung Cancer RateMultiscale Geographically Weighted Regression (MGWR)PM 2.5Predictive ModelingPublic HealthSocioeconomic Equity FactorsSpatial Autoregressive Models (SAR)Universal Kriging

Identifiers

PMID42226955
PMCPMC13222682

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.