bluuebunny/crossref_metadata_2025 huggingface dataset:
Seroprevalence and Risk Factors of Sexually Transmitted Blood-Borne Infections among Pregnant Women Attending Antenatal Care in Jirapa, Upper West Region of Ghana
Authors: Kuugbee Eugene D. , Maaldu Gloria , Adamu Aseta , Salia Nafisa , Walana Williams , Kampo Sylvanus , Vicar Ezekiel K. , Ziem Juventus B.
Published: May 2023

Background. Sexually transmitted blood-borne infections (STBBIs) contribute to negative outcomes of pregnancy. Hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), and syphilis infections in pregnancy contribute significantly to maternal and child morbidities and mortalities. This study assessed the prevalence, knowledge, and risk factors of STBBIs (HBV, HCV, HIV, and syphilis) among pregnant women attending antenatal clinics in Jirapa. Methods. A cross-sectional study design involving 246 pregnant women was employed for the study. A structured questionnaire was used to solicit information about the knowledge, prevalence, and risk factors of STBBIs. Results. The overall prevalence of STBBIs was 11.4%; HBV prevalence was 9.8% and 0.8% each for HCV, HIV, and syphilis. About 66% of mothers were aware of mother-to-child transmission of infections during pregnancy. Knowledge of transmission of HIV (93.9%), hepatitis (67.1%), and syphilis (53.7%) in pregnancy was relatively high. Knowledge of risk factors for HIV, hepatitis, and syphilis was 97.6%, 74.4%, and 76.0%, respectively. More than 98% of respondents knew about the prevention of HIV, hepatitis, and syphilis. Significant risk factors associated with and predictive of STBBIs were female genital mutilation (FGM) and gravidity. Conclusion. The occurrence of STBBIs among pregnant women was strongly associated with FGM and gravidity. Public health education should be directed at stopping the practice of FGM and improving reproductive health in the study area.
Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques
Authors: Gaber Tarek , Awotunde Joseph B. , Folorunso Sakinat O. , Ajagbe Sunday A. , Eldesouky Esraa
Published: May 2023

The emergence of the Internet of Things (IoT) has witnessed immense growth globally with the use of various devices found in home, transportation, healthcare, and industry. The deployment and implementation of the IoT paradigm in industrial settings lead to the architectural changes of Industrial Automation and Control Systems (IACS) plus the countless connectivity of industrial systems. This resulted in what is referred to as the Industrial Internet of Things (IIoT), which removes the barrier of connecting IACS to isolated conventional ICT platforms. In recent times, the IoT has started hacking our personal lives and not only our world, thus creating a platform for impending IoT cyberattacks. The widespread use of the IoT has created a rich platform for possible IoT cyberattacks. Machine learning (ML) algorithms have been driven solutions to secure wireless communication in IIoT-based systems, and their use in solving various cybersecurity challenges. Therefore, this paper proposes a novel intrusion detection model based on the Particle Swarm Optimization (PSO) and Bat algorithm (BA) for feature selection, and the Random Forest (RF) classifier for the classification of malicious behaviors in IIoT-based network traffic. An IIoT-based cybersecurity dataset, WUSTL-IIOT-2021 Dataset, was used to evaluate the performance of the proposed model using accuracy, recall, precision, and F1-score. The results of the two feature selection were compared to identify the most promising one. The results were compared with other recent state-of-the-art ML and multiobjective algorithms, and the results showed better performance. The RF along with BA classifier had proved to be the best classifier.
Amharic Language Image Captions Generation Using Hybridized Attention-Based Deep Neural Networks
Authors: Solomon Rodas , Abebe Mesfin
Published: May 2023

This study aims to develop a hybridized deep learning model for generating semantically meaningful image captions in Amharic Language. Image captioning is a task that combines both computer vision and natural language processing (NLP) domains. However, existing studies in the English language primarily focus on visual features to generate captions, resulting in a gap between visual and textual features and inadequate semantic representation. To address this challenge, this study proposes a hybridized attention-based deep neural network (DNN) model. The model consists of an Inception-v3 convolutional neural network (CNN) encoder to extract image features, a visual attention mechanism to capture significant features, and a bidirectional gated recurrent unit (Bi-GRU) with attention decoder to generate the image captions. The model was trained on the Flickr8k and BNATURE datasets with English captions, which were translated into Amharic Language with the help of Google Translator and Amharic Language experts. The evaluation of the model showed improvement in its performance, with a 1G-BLEU score of 60.6, a 2G-BLEU score of 50.1, a 3G-BLEU score of 43.7, and a 4G-BLEU score of 38.8. Generally, this study highlights the effectiveness of the hybrid approach in generating Amharic Language image captions with better semantic meaning.
Supplementary Table S1 from Comparative Analysis of the Humoral Immune Response to the EBV Proteome across EBV-Related Malignancies
Authors: Argirion Ilona , Pfeiffer Ruth M. , Proietti Carla , Coghill Anna E. , Yu Kelly J. , Middeldorp Jaap M. , Sarathkumara Yomani D. , Hsu Wan-Lun , Chien Yin-Chu , Lou Pei-Jen , Wang Cheng-Ping , Rothman Nathaniel , Lan Qing , Chen Chien-Jen , Mbulaiteye Sam M. , Jarrett Ruth F. , Glimelius Ingrid , Smedby Karin E. , Hjalgrim Henrik , Hildesheim Allan , Doolan Denise L. , Liu Zhiwei
Published: May 2023

Supplementary Table S1 shows marker pathway allocation and corresponding citations
Supplementary Table S2 from Rural, Large Town, and Urban Differences in Optimal Subspecialty Follow-up and Survivorship Care Plan Documentation among Childhood Cancer Survivors
Authors: Noyd David H. , Janitz Amanda E. , Baker Ashley A. , Beasley William H. , Etzold Nancy C. , Kendrick David C. , Oeffinger Kevin C.
Published: May 2023

Modeling for the association between rurality, with subanalysis for non-Oklahoma City urban areas, and the risk of suboptimal subspecialty follow-up care five to seven years after initial diagnosis using Modified Poisson Regression with robust error variance. (n=449)
Table S3 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

Progression free survival covariates and added predictive values.
Table S2 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

Hazard ratios of high vs low risk of progression free survival.
Supplementary Figure S1 from Rural, Large Town, and Urban Differences in Optimal Subspecialty Follow-up and Survivorship Care Plan Documentation among Childhood Cancer Survivors
Authors: Noyd David H. , Janitz Amanda E. , Baker Ashley A. , Beasley William H. , Etzold Nancy C. , Kendrick David C. , Oeffinger Kevin C.
Published: May 2023

Directed acyclic graph of the potential associations between rurality at time of diagnosis and optimal subspecialty follow-up among childhood cancer survivors.
Supplemental Methods from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

Expanded description of the SPECTRA approach.
Supplemental Figure S7 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

CoMMpass spectra Hallmark pathway GSEA summary. Gene Set Enrichment Analysis (GSEA) results for all spectra using the Hallmark pathway set from MSigDB v7.4. Only pathways with a significant enrichment (p<0.000025) with at least one spectrum shown. The significance level (intensity) and direction of each enrichment are indicated: green negative normalized enrichment score (NES), purple positive NES. The Hallmark Process Category (Liberzon et al., 2015) for each gene set is shown far left.
Supplemental Figure S4 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

Kaplan-Meier curves of overall survival by spectra and UAMS risk-groups in the CoMMpass data. CoMMpass patients were assigned to high and low risk separately using the OS Spectra score and the UAMS score (See Methods, Comparison to the UAMS risk score). Blue and orange lines: both methods agree on patient's risk category. Purple line: Spectra correctly identified high-risk patients (poorer survival) that are misclassified as low risk by UAMS. Green line: Spectra correctly identified low-risk patients (better survival) that are misclassified as high risk by UAMS.
Supplementary Table S3 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

Hazard ratios, 95% confidence intervals, and p-values of oral cavity and larynx cancer patients in a multivariable analysis.
Supplemental Figure S2 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

Diagram of predictive modeling.
Supplementary Table S2 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

Table summarizing 5-year overall survival rate of laryngeal cancer patients.
Supplemental Figure S1 from Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes
Authors: Griffin Rosalie , Hanson Heidi A. , Avery Brian J. , Madsen Michael J. , Sborov Douglas W. , Camp Nicola J.
Published: May 2023

A color analogy to illustrate the advantages of spectra variables for modeling. a) Individual observations of color. b) Dimension Reduction (additive color theory), all colors can be represented using 3 quantitative RGB variables. c) Standard-use, hierarchical clustering identifies 3 groups. Each sample is categorized into a group (A, B or C). Subsequent modeling uses one categorical variable. d) Multivariable modeling with quantitative spectra variables, multiple spectra integrated directly into a multivariable analysis. Each uncorrelated variable can be assessed separately for its predictive value for an outcome. This implementation retains higher resolution because the variables are quantitative and retain integrity to the initial data. Note, lower-resolution versions of xB and xG can be achieved using hierarchical groups, but the loss of quantification may lose power. xR cannot be captured by any group ordering and associations for this spectrum would be lost using hierarchical groups. Three dimensions shown for visualization purposes; transcriptome data is likely to harbor a higher number of dimensions.
Supplementary Table S1 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

Table summarizing 5-year overall survival rate of oral cavity cancer patients.
Table S7 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S7 Shows Hazard Ratios and 95% Confidence Intervals for Total Mortality in Women Diagnosed with Breast Cancer According to Diet Quality Index Stratified by Race and Ethnicity
Supplementary Figure S2 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

KM curve depicting the 5-year overall survival of larynx cavity cancer patients.
Supplementary Figure S1 from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

KM curve depicting 5-year overall survival of oral cavity cancer patients.
Table S3 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S3 Shows Hazard Ratios and 95% Confidence Intervals for All-Cause Mortality in Women Diagnosed with Breast Cancer (unrelated women)
Table S2 from Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Authors: Haslam Danielle E. , John Esther M. , Knight Julia A. , Li Zhongyu , Buys Saundra S. , Andrulis Irene L. , Daly Mary B. , Genkinger Jeanine M. , Terry Mary Beth , Zhang Fang Fang
Published: May 2023

Table S2 Shows Pearson's Correlation Coefficients (r) for Diet Quality Index Scores
Table S4 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Table S4 shows differences in the prevalence of risk factors comparing foreign-born Asian American cases and foreign-born Asian American controls aged 44-55 years
Table S3 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Table S3 shows differences in the prevalence of risk factors comparing foreign-born Asian American cases and foreign-born Asian American controls
Table S1 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Table S1 shows differences in birthplace distribution of study participants
Figure S2 from Changes in Breast Cancer Risk and Risk Factor Profiles among U.S.-Born and Immigrant Asian American Women Residing in the San Francisco Bay Area
Authors: John Esther M. , Koo Jocelyn , Ingles Sue A. , Kurian Allison W. , Hines Lisa M.
Published: May 2023

Figure S2 shows differences in the prevalence of risk factors between controls from the younger birth cohort when comparing foreign-born Asian American, U.S.-born Asian American, and U.S.-born non-Hispanic White women.
Supplementary Table 3 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Table 3. Patients' malnutrition status stratified by BMI groups.
Table S6D from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 6D presents findings of the prospective cohort study
Supplementary Figure 7 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 7 Kaplan-Meier curves for overall survival in patients who received target therapy and immune checkpoint blockade.
Table S6C from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 6C presents findings of individual non-randomised interventions
Table S6B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 6B presents findings of individual randomised cross-over studies
Supplementary Figure 5 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 5. Time-dependent ROC curves of malnutrition scores.
Supplementary Figure 4 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 4 The Sankey plot showing the relationship between different malnutrition categories.
Supplementary Figure 3 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 3 The correlation analysis for three malnutrition scores and BMI. Scores and BMI were treated as continuous variables. For CONUT, higher scores are associated with worse malnutrition status. For NRI and PNI, lower scores are associated with worse malnutrition status.
Supplementary Figure 2 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 2 The flow chart of patient selection.
Supplementary Figure 1 from Prevalence and Prognostic Significance of Malnutrition in Patients with Brain Metastasis
Authors: Liu Zheran , Zhang Yu , Pei Yiyan , He Yan , Yu Jiayi , Zhang Renjie , Wang Jingjing , Chong Weelic , Hai Yang , Peng Xingchen , Fang Fang
Published: May 2023

Supplementary Figure 1 The statistical and graphical diagnostics test based on Schoenfeld residuals for proportional hazards assumption.
Table S2D from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 2D presents the study characteristics of the prospective cohort study
Table S3 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 3 presents the risk of bias assessment, using the ROBINS-E
Table S1 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Table 1 presents the search terminology for the systematic review
Table 1 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

GRADE appraisal for physical activity–inflammatory biomarkers pathways.
Table 1 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

GRADE appraisal for inflammatory biomarkers – breast cancer pathways.
Figure S5D from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 5D presents the funnel plots from the meta-analysis of adiponectin and breast cancer risk
Figure S3A from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 3A presents forest plots for physical activity and IL-6, by menopausal status
Figure S2B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 2B presents forest plots for physical activity and TNF-alpha, by exercise type
Figure S2A from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 2A present forest plots for physical activity and TNF-alpha, by menopausal status
Figure S1B from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1B presents forest plots for physical activity and CRP, by exercise type
Figure S4C from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 4C presents dose-response curves for leptin and breast cancer risk, by menopausal status
Figure 3 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Forest plots for effects of physical activity interventions compared with usual activity in RCTs. A forest plot for (A) adiponection and (B) leptin.
Figure S4A from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 4A presents forest plots for leptin and breast cancer risk, by menopausal status
Figure S3B from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 3B presents the funnel plot from the meta-analysis of IL-6 and breast cancer risk
Figure 1 from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

PRISMA flow diagram. This figure incorporates literature search, screening, and study selection. Reasons for full-text inclusion included duplicate study, publication type (e.g., review), study design (e.g., cross-sectional design), population (e.g., male only), intervention/exposure (e.g., physical activity and diet), comparator (e.g., diet or medication), and outcomes (e.g., no inflammation measures).
Figure S3A from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 3A presents forest plots for IL-6 and breast cancer risk, by menopausal status
Figure S2C from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 2C presents the funnel plot from the meta-analysis of TNF-alpha and breast cancer risk
Figure S2A from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 2A presents forest plots for TNF-alpha and breast cancer risk, by menopausal status
Figure S1E from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1E presents the funnel plot from the meta-analysis of CRP and breast cancer risk
Figure S1D from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1D presents dose-response curves for CRP and breast cancer risk, by menopausal status
Figure S1C from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1C present forest plots for CRP and breast cancer risk, excluding studies that had a moderate risk of bias
Figure S1A from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Supplementary Figure 1A presents forest plots for CRP and breast cancer risk, by menopausal status
Figure 4 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Dose–response curves for effects of inflammatory biomarkers on breast cancer risk. Dose–response curves for (A) CRP, (B) leptin, and (C) adiponectin.
Figure 1 from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

PRISMA flow diagram. This figure incorporates literature search, screening and study selection.
Supplementary Table 1 from Genetic Factors Associated with Absolute and Relative Plasma Concentrations of Calcitriol
Authors: Wilson Robin Taylor , Safford Susan E. , Ostrom Quinn T. , Wang Ming , McDonald Alicia C. , Salzberg Anna C. , Barnholtz-Sloan Jill S. , Richie John P.
Published: May 2023

Supplemental Table 1. Distribution of Vitamin D Biomarkers By Race/Ethnicity, Sex and Oral Contraceptive Use.
Supplemental Table 3 from Genetic Factors Associated with Absolute and Relative Plasma Concentrations of Calcitriol
Authors: Wilson Robin Taylor , Safford Susan E. , Ostrom Quinn T. , Wang Ming , McDonald Alicia C. , Salzberg Anna C. , Barnholtz-Sloan Jill S. , Richie John P.
Published: May 2023

Supplemental Table 3. Pairwise D’ and r2 Linkage Disequilibrium Values between Single Nucleotide Polymorphisms and Spearman's Correlation with West African Genetic Ancestry Proportion, African American (N=188) matched with European American (N=188) participants, Central Pennsylvania*
Supplemental Table 2 from Genetic Factors Associated with Absolute and Relative Plasma Concentrations of Calcitriol
Authors: Wilson Robin Taylor , Safford Susan E. , Ostrom Quinn T. , Wang Ming , McDonald Alicia C. , Salzberg Anna C. , Barnholtz-Sloan Jill S. , Richie John P.
Published: May 2023

Supplemental Table 2. Allele Frequencies and Hardy-Weinberg Equilibrium (HWE) among African American (AA) and European American (EA) Participants (Central Pennsylvania) compared with 1000 Genomes African (AFR) and European (EUR) Populations
Supplementary Table S8 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table contains the 2030 projections of metastatic prostate cancer prevalence when we assume better recurrent survival compared to de novo.
Supplementary Table S3 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table compares the prevalence by duration in 2018 by race.
Supplementary Table S2 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table contains prevalence projections for 2030 under the assumption of improving survival from 2019 onwards.
Supplementary Table S11 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table compares projections of metastatic prostate cancer prevalence in 2030 between White and Black men.
Supplementary Table S10 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table compares estimates of metastatic prostate cancer prevalence in 2018 between White and Black men.
Supplementary Table S1 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This supplemental table shows the relative survival data for de novo metastatic patients used to calculate prevalence estimates
Supplementary Material and Methods from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This document contains a description of the methods used in calculating projections with improved survival and the methods for the sensitivity analysis.
Supplementary Material and Methods from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

This zip file contains the data, MIAMOD screenshots, and modeling results for the major results reported in the main text. It also contains the MIAMOD user manual which provides more details on how to format data to run MIAMOD.
Supplementary Figure S5 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

Supplementary Figure S5: Population distributions by race and age group for years 2000, 2010, 2020, and 2030.Projections for 2020 and 2030 are taken from US Census population projections based on the 2010 census.
Supplementary Figure S4 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

Supplementary Figure S4: Population projections by race and age group (40+) between 1990 and 2030.Projections from 2019 onward are taken from US Census population projections based on the 2010 census.
Supplementary Figure S2 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

Supplementary Figure S2: Prevalence over time for all races, assuming better recurrent survival. Solid black linerepresents estimated number of new metastatic prostate cancer (MPC) cases; dashed blue line representsobserved number of prostate cancer (PC) deaths; dotted red line indicates projections after 2018.
Supplementary Figure S1 from Estimating the Number of Men Living with Metastatic Prostate Cancer in the United States
Authors: Devasia Theresa P. , Mariotto Angela B. , Nyame Yaw A. , Etzioni Ruth
Published: May 2023

Supplementary Figure S1: Prevalence over time for all races, assuming worse recurrent survival. Solid black linerepresents estimated number of new metastatic prostate cancer (MPC) cases; dashed blue line representsobserved number of prostate cancer (PC) deaths; dotted red line indicates projections after 2018.
Figure S6 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Random forest model and GO enrichment. a, Schematic plot of random forest; b, GO terms enriched by the matched proteins of differential peptide probes.
Figure S4 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Figure S4. Autoantibodies differentially expressed in plasma samples from patients with Early-LUAD versus BLD. Each boxplot represents an individual peptide signature in the two sample sets.
Figure S3 from High-Throughput Peptide Arrays Identify Potential Diagnostic Autoantibody Signatures in Early-Stage Lung Adenocarcinoma
Authors: Luo Rongrong , Zhong Pei , Li Xiying , Cai Juan , Tao Yimin , Xiong Bangzhu , Zheng Hancheng , Zhang Zhishang , Tang Le , Yao Jiarui , Li Yingrui , Shi Yuankai , Han Xiaohong
Published: May 2023

Figure S3. Autoantibodies differentially expressed in plasma samples from patients with Early-LUAD versus NHC. Each boxplot represents an individual peptide signature in the two sample sets.
Data from Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk
Authors: Lou Makayla W.C. , Drummond Ann E. , Swain Christopher T.V. , Milne Roger L. , English Dallas R. , Brown Kristy A. , van Roekel Eline H. , Skinner Tina L. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Abstract
This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose–response of these associations. Risk of bias was evaluated using the ROBINS-E tool and the quality of evidence was appraised with Grading of Recommendations Assessment, Development, and Evaluation. Thirty-four observational studies and three Mendelian randomization studies were included. Meta-analysis suggested that women with the highest levels of C-reactive protein (CRP) had a higher risk of developing breast cancer [risk ratio (RR) = 1.13; 95% confidence interval (CI), 1.01–1.26] compared with women with the lowest levels. Women with highest levels of adipokines, particularly adiponectin (RR = 0.76; 95% CI, 0.61–0.91) had a reduced breast cancer risk, although this finding was not supported by Mendelian randomization analysis. There was little evidence of an effect of cytokines, including TNFα and IL6, on breast cancer risk. The quality of evidence for each biomarker ranged from very low to moderate. Beyond CRP, the published data do not clearly support the role of inflammation in the development of breast cancer.
Data from Neighborhood Socioeconomic Status and Racial and Ethnic Survival Disparities in Oral Cavity and Laryngeal Cancer
Authors: Liu Yupeng , Zhong Lydia , Puram Sidharth V. , Mazul Angela L.
Published: May 2023

AbstractBackground:
Oral cavity cancer (OCC) and laryngeal cancer are among the most common cancers worldwide. This study investigated survival in non-Hispanic (NH) Black, NH White, Asian, and Hispanic patients with OCC and laryngeal cancer of low, intermediate, and high neighborhood socioeconomic status (nSES).
Methods:
We used data from the SEER 18 Census Tract-level SES and Rurality Database of the National Cancer Institute to create cohorts of OCC and laryngeal cancer patients from 2013 to 2018. Univariate survival analysis was performed with Kaplan–Meier curves and log-rank P values by nSES and then the cross-classification of race, ethnicity, and nSES. We used Cox proportional hazards regression model for multivariable analysis.
Results:
Higher nSES was associated with better OCC survival for NH White, NH Black, and Asian patients, and better laryngeal cancer survival for NH White, NH Black, Hispanic, and Asian patients. In the multivariable analyses of both OCC and laryngeal cancer survival, NH Black patients had worse survival than NH White patients in the high nSES tertile. NH Black patients with OCC were at higher risk of death than NH White patients at all nSES levels. Conversely, Asian patients with laryngeal cancer demonstrated better survival than other races within the high nSES.
Conclusions:
Overall survival differs between racial and ethnic groups of similar nSESs. These health disparities in patients with OCC and laryngeal cancer reflect broader inequities in the cancer control continuum.
Impact:
The cross-classification of race, ethnicity, and nSES revealed disparities in the 5-year overall survival of patients with OCC and laryngeal cancer and highlights the importance of intersectionality in the discussion of health equity.
Data from Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation
Authors: Swain Christopher T.V. , Drummond Ann E. , Milne Roger L. , English Dallas R. , Brown Kristy A. , Lou Makayla W.C. , Boing Leonessa , Bageley Amy , Skinner Tina L. , van Roekel Eline H. , Moore Melissa M. , Gaunt Tom R. , Martin Richard M. , Lewis Sarah J. , Lynch Brigid M.
Published: May 2023

Abstract
The protective effect of physical activity on breast cancer incidence may partially be mediated by inflammation. Systematic searches of Medline, EMBASE, and SPORTDiscus were performed to identify intervention studies, Mendelian randomization studies, and prospective cohort studies that examined the effects of physical activity on circulating inflammatory biomarkers in adult women. Meta-analyses were performed to generate effect estimates. Risk of bias was assessed, and the Grading of Recommendations Assessment, Development, and Evaluation system was used to determine the overall quality of the evidence. Thirty-five intervention studies and one observational study met the criteria for inclusion. Meta-analyses of randomized controlled trials (RCT) indicated that, compared with control groups, exercise interventions reduced levels of C-reactive protein (CRP) [standardized mean difference (SMD) = −0.27, 95% confidence interval (CI) = −0.62 to 0.08), tumor necrosis factor alpha (TNFα, SMD = −0.63, 95% CI = −1.04 to −0.22), interleukin-6 (IL6, SMD = −0.55, 95% CI = −0.97 to −0.13) and leptin (SMD = −0.50, 95% CI = −1.10 to 0.09). Owing to heterogeneity in effect estimates and imprecision, evidence strength was graded as low (CRP, leptin) or moderate (TNFα and IL6). High-quality evidence indicated that exercise did not change adiponectin levels (SMD = 0.01, 95% CI = −0.14 to 0.17). These findings provide support for the biological plausibility of the first part of the physical activity—inflammation—breast cancer pathway.
Data from Risk and Protective Factors for Cancer Mortality among United States Service Members and Veterans (2001–2018)
Authors: Sharifian Neika , Carey Felicia R. , Seay Julia S. , Castañeda Sheila F. , Boyko Edward J. , Rull Rudolph P.
Published: May 2023

AbstractBackground:
Prior research linking military factors with cancer-specific mortality has shown inconsistent findings, with few studies examining these associations among U.S. service members and veterans who served in Operation Iraqi Freedom/Operation Enduring Freedom conflicts.
Methods:
Cancer mortality between 2001 and 2018 was ascertained from the Department of Defense Medical Mortality Registry and National Death Index for 194,689 Millennium Cohort Study participants. Cause-specific Cox proportional hazard models were used to examine links between military characteristics and cancer mortality [overall, early (<45 years), and lung].
Results:
Compared with individuals who deployed with no combat experiences, non-deployers had a greater risk of overall [HR = 1.34; 95% confidence interval (CI) = 1.01–1.77] and early cancer mortality (HR = 1.80; 95% CI = 1.06–3.04). Enlisted individuals had a greater risk of lung cancer mortality compared with officers (HR = 2.65; 95% CI = 1.27–5.53). No associations by service component, branch, or military occupation and cancer mortality were observed. Higher education was associated with reduced overall, early and lung cancer mortality risk and smoking and life stressors were associated with elevated overall and lung cancer mortality risk.
Conclusions:
These findings are consistent with the healthy deployer effect in which military personnel who were deployed tend to be healthier than those who did not deploy. Further, these findings highlight the importance of considering socioeconomic factors, such as military rank, that may have long-term implications for health.
Impact:
These findings highlight military occupational factors that may predict long-term health outcomes. Additional work is necessary to investigate more nuanced environmental and occupational military exposures and cancer mortality.