Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.005
David T. Zhu , Andrew Park
Purpose
To analyze drug overdose mortality trends among Asian American and Native Hawaiian/Pacific Islander (AANHPI) populations.
Methods
We obtained data on drug overdose deaths and population totals from CDC WONDER and the American Community Survey (2018–2022). Crude mortality rates per 100,000 were calculated overall and by sex, U.S. Census Division, and drug type. Disaggregated analyses included six Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese) and three NHPI subgroups (Native Hawaiian, Guamanian, and Samoan).
Results
In 2022, Asian Americans had 1226 drug overdose deaths and NHPI individuals had 154. The mortality rate for NHPI individuals (17.52 [95 % CI: 14.76–20.29] per 100,000) tripled that of Asian Americans (5.85 [95 % CI: 5.52–6.18] per 100,000). Fentanyl was the leading drug-related death among Asian Americans (3.17 [95 % CI: 2.93–3.41] per 100,000), while methamphetamine led for NHPI individuals (11.38 [95 % CI: 9.15–13.61] per 100,000). Disaggregated mortality rates were highest for Korean Americans (9.06 [95 % CI: 8.88–9.24] per 100,000) and Guamanians (43.16 [95 % CI: 39.05–48.24] per 100,000) among the Asian American and NHPI subgroups, respectively.
Conclusions
AANHPI populations experience distinct overdose mortality patterns, with NHPI individuals and specific ethnic subgroups disproportionately affected, warranting targeted public health interventions.
{"title":"National trends in drug overdose mortality among Asian American, Native Hawaiian, and Pacific Islander populations","authors":"David T. Zhu , Andrew Park","doi":"10.1016/j.annepidem.2024.12.005","DOIUrl":"10.1016/j.annepidem.2024.12.005","url":null,"abstract":"<div><h3>Purpose</h3><div>To analyze drug overdose mortality trends among Asian American and Native Hawaiian/Pacific Islander (AANHPI) populations.</div></div><div><h3>Methods</h3><div>We obtained data on drug overdose deaths and population totals from CDC WONDER and the American Community Survey (2018–2022). Crude mortality rates per 100,000 were calculated overall and by sex, U.S. Census Division, and drug type. Disaggregated analyses included six Asian American subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese) and three NHPI subgroups (Native Hawaiian, Guamanian, and Samoan).</div></div><div><h3>Results</h3><div>In 2022, Asian Americans had 1226 drug overdose deaths and NHPI individuals had 154. The mortality rate for NHPI individuals (17.52 [95 % CI: 14.76–20.29] per 100,000) tripled that of Asian Americans (5.85 [95 % CI: 5.52–6.18] per 100,000). Fentanyl was the leading drug-related death among Asian Americans (3.17 [95 % CI: 2.93–3.41] per 100,000), while methamphetamine led for NHPI individuals (11.38 [95 % CI: 9.15–13.61] per 100,000). Disaggregated mortality rates were highest for Korean Americans (9.06 [95 % CI: 8.88–9.24] per 100,000) and Guamanians (43.16 [95 % CI: 39.05–48.24] per 100,000) among the Asian American and NHPI subgroups, respectively.</div></div><div><h3>Conclusions</h3><div>AANHPI populations experience distinct overdose mortality patterns, with NHPI individuals and specific ethnic subgroups disproportionately affected, warranting targeted public health interventions.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 36-41"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.001
Adriano Hyeda , Élide Sbardellotto Mariano da Costa , Sérgio Cândido Kowalski
Background
There is a lack of research on whether COVID-19 disruptions in breast cancer screening, diagnosis, and treatment affected mortality rates over time.
Method
This ecological time series study, covering the period between 2013 and 2023, utilizes the inflection point regression model and calculates the Annual Percentage Change (APC). The study used open-access data from the Brazilian Mortality Information System. The dependent variables measured were mortality rates due to breast cancer as an underlying cause and contributing cause in women aged 20 and over. The double exponential smoothing method was applied to predict mortality rates for 2020–2023.
Results
During the study period, the mortality rate due to breast cancer as a contributing cause increased approximately tenfold compared to mortality as an underlying cause (APC 6.9 % vs. 0.7 %). On average, 12 % of breast cancer-related deaths were attributed to the disease as a contributing cause. Breast cancer deaths as an underlying cause declined in 2020 and 2021, remaining below the 95 % predicted interval (95 % PI), but showed recovery until 2023. Mortality due to breast cancer as a contributing cause increased early in the pandemic, with deaths related to COVID-19 as an underlying cause comprising 39.6 % of cases in 2021. Breast cancer-related deaths, both as an underlying and contributing cause, showed an upward trend until 2021 and remained within the 95 % PI until 2023.
Conclusion
During the pandemic, deaths due to breast cancer as an underlying cause decreased while contributing deaths increased, with total mortality remaining within the predicted range.
{"title":"The impact of COVID-19 on breast cancer mortality trends in Brazil: A time-series study","authors":"Adriano Hyeda , Élide Sbardellotto Mariano da Costa , Sérgio Cândido Kowalski","doi":"10.1016/j.annepidem.2024.12.001","DOIUrl":"10.1016/j.annepidem.2024.12.001","url":null,"abstract":"<div><h3>Background</h3><div>There is a lack of research on whether COVID-19 disruptions in breast cancer screening, diagnosis, and treatment affected mortality rates over time.</div></div><div><h3>Method</h3><div>This ecological time series study, covering the period between 2013 and 2023, utilizes the inflection point regression model and calculates the Annual Percentage Change (APC). The study used open-access data from the Brazilian Mortality Information System. The dependent variables measured were mortality rates due to breast cancer as an underlying cause and contributing cause in women aged 20 and over. The double exponential smoothing method was applied to predict mortality rates for 2020–2023.</div></div><div><h3>Results</h3><div>During the study period, the mortality rate due to breast cancer as a contributing cause increased approximately tenfold compared to mortality as an underlying cause (APC 6.9 % vs. 0.7 %). On average, 12 % of breast cancer-related deaths were attributed to the disease as a contributing cause. Breast cancer deaths as an underlying cause declined in 2020 and 2021, remaining below the 95 % predicted interval (95 % PI), but showed recovery until 2023. Mortality due to breast cancer as a contributing cause increased early in the pandemic, with deaths related to COVID-19 as an underlying cause comprising 39.6 % of cases in 2021. Breast cancer-related deaths, both as an underlying and contributing cause, showed an upward trend until 2021 and remained within the 95 % PI until 2023.</div></div><div><h3>Conclusion</h3><div>During the pandemic, deaths due to breast cancer as an underlying cause decreased while contributing deaths increased, with total mortality remaining within the predicted range.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 7-13"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.002
Travis R. Moore , Erin Hennessy , Yuilyn Chang Chusan , Laura Ellen Ashcraft , Christina D. Economos
Effective chronic disease prevention requires a systems approach to the design, implementation, and refinement of interventions that account for the complexity and interdependence of factors influencing health outcomes. This paper proposes the Participatory Implementation Systems Mapping (PISM) process, which combines participatory systems modeling with implementation strategy development to enhance intervention design and implementation planning. PISM leverages the collaborative efforts of researchers and community partners to analyze complex health systems, identify key determinants, and develop tailored interventions and strategies that are both adaptive and contextually relevant. The phases of the PISM process include strategize, innovate, operationalize, and assess. We describe and demonstrate how each phase contributes to the overall goal of effective and sustainable intervention implementation. We also address the challenges of data availability, model complexity, and resource constraints. We offer solutions such as innovative data collection methods and participatory model development to enhance the robustness and applicability of systems models. Through a case study on the development of a chronic disease prevention intervention, the paper illustrates the practical application of PISM and highlights its potential to guide epidemiologists and implementation scientists in developing interventions that are responsive to the complexities of real-world health systems. The conclusion calls for further research to refine participatory systems modeling techniques, overcome existing challenges in data availability, and expand the use of PISM in diverse public health contexts.
{"title":"Considerations for using participatory systems modeling as a tool for implementation mapping in chronic disease prevention","authors":"Travis R. Moore , Erin Hennessy , Yuilyn Chang Chusan , Laura Ellen Ashcraft , Christina D. Economos","doi":"10.1016/j.annepidem.2024.12.002","DOIUrl":"10.1016/j.annepidem.2024.12.002","url":null,"abstract":"<div><div>Effective chronic disease prevention requires a systems approach to the design, implementation, and refinement of interventions that account for the complexity and interdependence of factors influencing health outcomes. This paper proposes the Participatory Implementation Systems Mapping (PISM) process, which combines participatory systems modeling with implementation strategy development to enhance intervention design and implementation planning. PISM leverages the collaborative efforts of researchers and community partners to analyze complex health systems, identify key determinants, and develop tailored interventions and strategies that are both adaptive and contextually relevant. The phases of the PISM process include strategize, innovate, operationalize, and assess. We describe and demonstrate how each phase contributes to the overall goal of effective and sustainable intervention implementation. We also address the challenges of data availability, model complexity, and resource constraints. We offer solutions such as innovative data collection methods and participatory model development to enhance the robustness and applicability of systems models. Through a case study on the development of a chronic disease prevention intervention, the paper illustrates the practical application of PISM and highlights its potential to guide epidemiologists and implementation scientists in developing interventions that are responsive to the complexities of real-world health systems. The conclusion calls for further research to refine participatory systems modeling techniques, overcome existing challenges in data availability, and expand the use of PISM in diverse public health contexts.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 42-51"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11728936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.004
Julie M. Petersen , Jaimie L. Gradus , Martha M. Werler , Samantha E. Parker
Purpose
Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests.
Methods
Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.
Results
The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index < 30 kg/m2 (aOR 3.3, 95 % CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education.
Conclusions
Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.
目的：尽管有大量的研究，腹壁缺损胃裂的病因仍不清楚。已知最强的危险因素是年轻的产妇年龄。我们的目的是利用随机森林对胃裂的病因进行假设生成分析。方法：数据来自Slone出生缺陷研究（病例对照，美国和加拿大，1998-2015年）。病例为腹裂妊娠（n=273）；对照组为活产婴儿，与中心频率匹配（n=2591）。通过标准化访谈确定潜在风险因素数据。我们使用目标最大似然估计计算调整优势比（aOR）和95%置信区间（ci）。结果：观察到最强的相关性与年轻的母亲年龄（aOR 3.4, 95% CI 2.9, 4.0）和孕前体重指数2 （aOR 3.3, 95% CI 2.4, 4.5）。在没有关系的父母、非黑人母亲种族、父亲年龄小、使用大麻、吸烟、饮酒、低胎次、口服避孕药使用、非甾体抗炎药使用、每日快餐/加工食品摄入、低多不饱和脂肪或单不饱和脂肪、高总脂肪和父母受教育程度较低的情况下，观察到更适度的增加几率。结论：我们的研究为已确定的风险因素和建议的新因素（如饮食的某些方面）提供了支持，这些因素值得进一步调查。
{"title":"An exploration of potential risk factors for gastroschisis using decision tree learning","authors":"Julie M. Petersen , Jaimie L. Gradus , Martha M. Werler , Samantha E. Parker","doi":"10.1016/j.annepidem.2024.12.004","DOIUrl":"10.1016/j.annepidem.2024.12.004","url":null,"abstract":"<div><h3>Purpose</h3><div>Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests.</div></div><div><h3>Methods</h3><div>Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.</div></div><div><h3>Results</h3><div>The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index < 30 kg/m<sup>2</sup> (aOR 3.3, 95 % CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education.</div></div><div><h3>Conclusions</h3><div>Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 19-26"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.003
Xingjian Xiao , Xiaohan Yi , Nyi Nyi Soe , Phyu Mon Latt , Luotao Lin , Xuefen Chen , Hualing Song , Bo Sun , Hailei Zhao , Xianglong Xu
Background
From a global perspective, China is one of the countries with higher incidence and mortality rates for cancer.
Objective
Our objective is to create an online cancer risk prediction tool for middle-aged and elderly Chinese adults by leveraging machine learning algorithms and self-reported data.
Method
Drawing from a cohort of 19,798 participants aged 45 and above from the China Health and Retirement Longitudinal Study (2011 - 2018), we employed nine machine learning algorithms (LR: Logistic Regression, Adaboost: Adaptive Boosting, SVM: Support Vector Machine, RF: Random Forest, GNB: Gaussian Naive Bayes, GBM: Gradient Boosting Machine, LGBM: Light Gradient Boosting Machine, XGBoost: eXtreme Gradient Boosting, KNN: K - Nearest Neighbors), which are mainly used for classification and regression tasks, to construct predictive models for various cancers. Utilizing non-invasive self-reported predictors encompassing demographic, educational, marital, lifestyle, health history, and other factors, we focused on predicting "Cancer or Malignant Tumour" outcomes. The types of cancers that can be predicted mainly include lung cancer, breast cancer, cervical cancer, colorectal cancer, gastric cancer, esophageal cancer, and other rare cancers.
Results
The developed tool, MyCancerRisk, demonstrated significant performance, with the Random Forest algorithm achieving an AUC of 0.75 and ACC of 0.99 using self-reported variables. Key predictors identified include age, self-rated health, sleep patterns, household heating sources, childhood health status, living conditions, and smoking habits.
Conclusion
MyCancerRisk aims to serve as a preventative screening tool, encouraging individuals to undergo testing and adopt healthier behaviours to mitigate the public health impact of cancer. Our study also sheds light on unconventional predictors, such as housing conditions, offering valuable insights for refining cancer prediction models.
{"title":"A web-based tool for cancer risk prediction for middle-aged and elderly adults using machine learning algorithms and self-reported questions","authors":"Xingjian Xiao , Xiaohan Yi , Nyi Nyi Soe , Phyu Mon Latt , Luotao Lin , Xuefen Chen , Hualing Song , Bo Sun , Hailei Zhao , Xianglong Xu","doi":"10.1016/j.annepidem.2024.12.003","DOIUrl":"10.1016/j.annepidem.2024.12.003","url":null,"abstract":"<div><h3>Background</h3><div>From a global perspective, China is one of the countries with higher incidence and mortality rates for cancer.</div></div><div><h3>Objective</h3><div>Our objective is to create an online cancer risk prediction tool for middle-aged and elderly Chinese adults by leveraging machine learning algorithms and self-reported data.</div></div><div><h3>Method</h3><div>Drawing from a cohort of 19,798 participants aged 45 and above from the China Health and Retirement Longitudinal Study (2011 - 2018), we employed nine machine learning algorithms (LR: Logistic Regression, Adaboost: Adaptive Boosting, SVM: Support Vector Machine, RF: Random Forest, GNB: Gaussian Naive Bayes, GBM: Gradient Boosting Machine, LGBM: Light Gradient Boosting Machine, XGBoost: eXtreme Gradient Boosting, KNN: K - Nearest Neighbors), which are mainly used for classification and regression tasks, to construct predictive models for various cancers. Utilizing non-invasive self-reported predictors encompassing demographic, educational, marital, lifestyle, health history, and other factors, we focused on predicting \"Cancer or Malignant Tumour\" outcomes. The types of cancers that can be predicted mainly include lung cancer, breast cancer, cervical cancer, colorectal cancer, gastric cancer, esophageal cancer, and other rare cancers.</div></div><div><h3>Results</h3><div>The developed tool, MyCancerRisk, demonstrated significant performance, with the Random Forest algorithm achieving an AUC of 0.75 and ACC of 0.99 using self-reported variables. Key predictors identified include age, self-rated health, sleep patterns, household heating sources, childhood health status, living conditions, and smoking habits.</div></div><div><h3>Conclusion</h3><div><em>MyCancerRisk</em> aims to serve as a preventative screening tool, encouraging individuals to undergo testing and adopt healthier behaviours to mitigate the public health impact of cancer. Our study also sheds light on unconventional predictors, such as housing conditions, offering valuable insights for refining cancer prediction models.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 27-35"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.009
Baoting He PhD , Sheng Xu PhD , C. Mary Schooling PhD , Gabriel M. Leung MD , Joshua W.K. Ho PhD , Shiu Lun Au Yeung PhD
Purpose
Although the gut microbiome is important in human health, its relation to adolescent obesity remains unclear. Here we assessed the associations of the gut microbiome with adolescent obesity in a case-control study.
Methods
In the “Children of 1997” birth cohort, participants with and without obesity at ∼17.4 years were 1:1 matched on sex, physical activity, parental education and occupation (n = 312). Fecal gut microbiome composition and pathways were assessed via shotgun metagenomic sequencing. The association of microbiota species with obesity was evaluated using conditional logistic regression. We explored the association of the obesity-relevant species with adolescent metabolomics using multivariable linear regression, and causal relationships with type 2 diabetes using Mendelian randomization analysis.
Results
Gut microbiota in the adolescents with obesity exhibited lower richness (p = 0.031) and evenness (p = 0.014) compared to controls. Beta diversity revealed differences in the microbiome composition in two groups (p = 0.034). Lower relative abundance of Clostridium spiroforme, Clostridium phoceensis and Bacteroides uniformis were associated with higher obesity risk (q<0.15). Lower Bacteroides uniformis was associated with higher branched-chain amino acid, potentially contributing to higher type 2 diabetes risk.
Conclusion
Adolescents with obesity had a distinct gut microbiota profile compared to the controls, possibly linked to metabolic pertubation and related diseases.
{"title":"Gut microbiome and obesity in late adolescence: A case-control study in “Children of 1997” birth cohort","authors":"Baoting He PhD , Sheng Xu PhD , C. Mary Schooling PhD , Gabriel M. Leung MD , Joshua W.K. Ho PhD , Shiu Lun Au Yeung PhD","doi":"10.1016/j.annepidem.2024.12.009","DOIUrl":"10.1016/j.annepidem.2024.12.009","url":null,"abstract":"<div><h3>Purpose</h3><div>Although the gut microbiome is important in human health, its relation to adolescent obesity remains unclear. Here we assessed the associations of the gut microbiome with adolescent obesity in a case-control study.</div></div><div><h3>Methods</h3><div>In the “Children of 1997” birth cohort, participants with and without obesity at ∼17.4 years were 1:1 matched on sex, physical activity, parental education and occupation (n = 312). Fecal gut microbiome composition and pathways were assessed via shotgun metagenomic sequencing. The association of microbiota species with obesity was evaluated using conditional logistic regression. We explored the association of the obesity-relevant species with adolescent metabolomics using multivariable linear regression, and causal relationships with type 2 diabetes using Mendelian randomization analysis.</div></div><div><h3>Results</h3><div>Gut microbiota in the adolescents with obesity exhibited lower richness (p = 0.031) and evenness (p = 0.014) compared to controls. Beta diversity revealed differences in the microbiome composition in two groups (p = 0.034). Lower relative abundance of <em>Clostridium spiroforme, Clostridium phoceensis and Bacteroides uniformis</em> were associated with higher obesity risk (q<0.15). Lower <em>Bacteroides uniformis</em> was associated with higher branched-chain amino acid, potentially contributing to higher type 2 diabetes risk.</div></div><div><h3>Conclusion</h3><div>Adolescents with obesity had a distinct gut microbiota profile compared to the controls, possibly linked to metabolic pertubation and related diseases.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 58-66"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.006
Fardowsa L.A. Yusuf MSc , Mohammad Ehsanul Karim PhD , Paul Gustafson PhD , Jason M. Sutherland PhD , Feng Zhu MSc , Yinshan Zhao PhD , Ruth Ann Marrie MD, PhD , Helen Tremlett PhD
Background
Studies suggest that depression/anxiety form part of the multiple sclerosis (MS) prodrome. However, several biases have not been addressed. We re-examined this association after correcting for: (i) misclassification of individuals not seeking healthcare, (ii) differential surveillance of depression/anxiety in the health system, and (iii) misclassified person-time from using the date of the first MS-related diagnostic claim (i.e., a demyelinating event) as a proxy for MS onset.
Methods
In this cohort study, we applied a validated algorithm to health administrative (‘claims’) data in British Columbia, Canada (1991–2020) to identify MS cases, and matched to general population controls. The neurologist-recorded date of MS symptom onset was available for a subset of the MS cases. We identified depression/anxiety in the 5-years preceding the first demyelinating claim using a validated algorithm. We compared the prevalence of depression/anxiety using modified Poisson regression. To account for misclassification and differential surveillance, we applied probabilistic bias analyses; for misclassified person-time, we applied time-distribution matching to the MS symptom onset date.
Results
Our cohort included 9929 MS cases and 49,574 controls. The prevalence ratio for depression/anxiety was 1.74 (95 %CI: 1.66–1.81). Following correction for misclassification, differential surveillance using a detection ratio of 1.11, and misclassified person-time, the prevalence ratio increased to 3.25 (95 %CI: 1.98–40.54). When the same correction was conducted, but a detection ratio of 1.16 was applied, the prevalence ratio increased to 3.13 (95 %CI: 1.97–33.52).
Conclusions
Previous conventional analyses were biased towards the null, leading to an under-estimation of the association between depression/anxiety and MS in the prodromal period. This first application of probabilistic quantitative bias analysis within MS research demonstrates both its feasibility and utility.
{"title":"The misclassification of depression and anxiety disorders in the multiple sclerosis prodrome: A probabilistic bias analysis","authors":"Fardowsa L.A. Yusuf MSc , Mohammad Ehsanul Karim PhD , Paul Gustafson PhD , Jason M. Sutherland PhD , Feng Zhu MSc , Yinshan Zhao PhD , Ruth Ann Marrie MD, PhD , Helen Tremlett PhD","doi":"10.1016/j.annepidem.2024.12.006","DOIUrl":"10.1016/j.annepidem.2024.12.006","url":null,"abstract":"<div><h3>Background</h3><div>Studies suggest that depression/anxiety form part of the multiple sclerosis (MS) prodrome. However, several biases have not been addressed. We re-examined this association after correcting for: (i) misclassification of individuals not seeking healthcare, (ii) differential surveillance of depression/anxiety in the health system, and (iii) misclassified person-time from using the date of the first MS-related diagnostic claim (i.e., a demyelinating event) as a proxy for MS onset.</div></div><div><h3>Methods</h3><div>In this cohort study, we applied a validated algorithm to health administrative (‘claims’) data in British Columbia, Canada (1991–2020) to identify MS cases, and matched to general population controls. The neurologist-recorded date of MS symptom onset was available for a subset of the MS cases. We identified depression/anxiety in the 5-years preceding the first demyelinating claim using a validated algorithm. We compared the prevalence of depression/anxiety using modified Poisson regression. To account for misclassification and differential surveillance, we applied probabilistic bias analyses; for misclassified person-time, we applied time-distribution matching to the MS symptom onset date.</div></div><div><h3>Results</h3><div>Our cohort included 9929 MS cases and 49,574 controls. The prevalence ratio for depression/anxiety was 1.74 (95 %CI: 1.66–1.81). Following correction for misclassification, differential surveillance using a detection ratio of 1.11, and misclassified person-time, the prevalence ratio increased to 3.25 (95 %CI: 1.98–40.54). When the same correction was conducted, but a detection ratio of 1.16 was applied, the prevalence ratio increased to 3.13 (95 %CI: 1.97–33.52).</div></div><div><h3>Conclusions</h3><div>Previous conventional analyses were biased towards the null, leading to an under-estimation of the association between depression/anxiety and MS in the prodromal period. This first application of probabilistic quantitative bias analysis within MS research demonstrates both its feasibility and utility.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 67-73"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.11.002
SuJung Jung , Ji Young Choi , Pradeep Tiwari , Itai M. Magodoro , Shivani A. Patel , Ahlam Jadalla , Daesung Choi
Purpose
Using a US nationally representative survey of adults, we aimed to evaluate the association between prevalent diabetes and the uptake of COVID-19 testing, rate of positive testing and symptom severity.
Methods
Data were sourced from the 2020–2021 National Health Interview Survey. COVID-19 outcomes were defined as: (1) test uptake (2) test positivity (3) diagnosis of COVID-19 and (4) severe disease symptoms with a positive COVID-19 test result. We compared the prevalence of COVID-19 outcomes by diabetes status and examined their associations using multivariate adjusted logistic and ordered logistic regression models.
Results
The prevalence of test uptake and test positivity were 50.7 % and 9.4 % in the US population, respectively. 10.3 % were diagnosed with COVID-19 infection by health professionals. There were no statistically significant differences in the outcomes by diabetes status. However, individuals with diabetes were more likely to have severe symptoms. In adjusted regression model, we found no significant associations of diagnosed diabetes with all outcomes.
Conclusions
Our findings contrast with prior evidence derived from hospitalized patients. Researchers and policy makers are encouraged to review the properties of data sources and their impact on public health recommendations, particularly in response to future pandemics.
{"title":"Reevaluating diabetes and COVID-19 outcomes using national-level data","authors":"SuJung Jung , Ji Young Choi , Pradeep Tiwari , Itai M. Magodoro , Shivani A. Patel , Ahlam Jadalla , Daesung Choi","doi":"10.1016/j.annepidem.2024.11.002","DOIUrl":"10.1016/j.annepidem.2024.11.002","url":null,"abstract":"<div><h3>Purpose</h3><div>Using a US nationally representative survey of adults, we aimed to evaluate the association between prevalent diabetes and the uptake of COVID-19 testing, rate of positive testing and symptom severity.</div></div><div><h3>Methods</h3><div>Data were sourced from the 2020–2021 National Health Interview Survey. COVID-19 outcomes were defined as: (1) test uptake (2) test positivity (3) diagnosis of COVID-19 and (4) severe disease symptoms with a positive COVID-19 test result. We compared the prevalence of COVID-19 outcomes by diabetes status and examined their associations using multivariate adjusted logistic and ordered logistic regression models.</div></div><div><h3>Results</h3><div>The prevalence of test uptake and test positivity were 50.7 % and 9.4 % in the US population, respectively. 10.3 % were diagnosed with COVID-19 infection by health professionals. There were no statistically significant differences in the outcomes by diabetes status. However, individuals with diabetes were more likely to have severe symptoms. In adjusted regression model, we found no significant associations of diagnosed diabetes with all outcomes.</div></div><div><h3>Conclusions</h3><div>Our findings contrast with prior evidence derived from hospitalized patients. Researchers and policy makers are encouraged to review the properties of data sources and their impact on public health recommendations, particularly in response to future pandemics.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 14-18"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142695968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.annepidem.2024.12.011
Katherine B. Owen , Lucy Corbett , Ding Ding , Rochelle Eime , Adrian Bauman
Objective
There is a lack of understanding of the specific types and intensities of physical activity driving the gender gap in overall levels of physical activity, and how these activities are changing over time. We examined the gender gap in specific types and intensities of physical activities in European adults from 2005 to 2022.
Study design and methods
This repeated cross-sectional study included data from adults from the Eurobarometer (2005–2022) from 28 European countries. Gender differences in meeting physical activity guidelines, sport, walking, moderate, and vigorous activity were examined using prevalence ratios (PR, relative inequalities) and mean differences (MD, absolute differences).
Results
Among 123,809 participants, there was no change in the gender gap in meeting physical activity guidelines from 2005 to 2022 (PR = 1.10; 95 % CIs 1.07, 1.14, PR = 1.04; 95 % CIs 1.01, 1.08, respectively). The gender gap in vigorous intensity activity decreased from 2005 to 2022 (MD = 589; 95 % CIs 545.7, 631.5, MD = 399; 95 % CIs 354.5, 444.3, respectively). The gender gap in moderate activity increased from 2005 to 2022 (MD = 10.9; 95 % CIs − 14.2, 35.9, MD = 104; 95 % CIs 77.8, 130.1, respectively). The gender gap in sport and exercise increased from 2009 to 2022 (PR = 1.14; 95 % CIs 1.10, 1.19; PR = 1.22; 95 % CIs 1.17, 1.27, respectively). There was no gender gap in walking between 2005 and 2022 (MD = -1.4; 95 % CIs − 21.2, 18.4, MD = 12.5; 95 % CIs − 4.9, 29.9, respectively).
Conclusions
Sport remains an underutilized contributor to overall physical activity levels and could be promoted among women to reduce the overall gender gap in physical activity.
目的：人们对身体活动的具体类型和强度导致整体身体活动水平的性别差距以及这些活动如何随时间变化缺乏了解。我们研究了2005年至2022年欧洲成年人在特定类型和强度的体育活动方面的性别差距。研究设计和方法：这项重复的横断面研究包括来自28个欧洲国家的欧洲晴雨表（2005-2022）的成年人数据。使用患病率比（PR，相对不平等）和平均差异（MD，绝对差异）检查在满足身体活动指南、运动、步行、中度和剧烈活动方面的性别差异。结果：在123,809名参与者中，从2005年到2022年，在满足体育活动指南方面的性别差距没有变化(PR=1.10；95% ci = 1.07, 1.14， PR=1.04；95% ci分别为1.01、1.08)。从2005年到2022年，高强度运动的性别差距有所缩小(MD=589；95% ci 545.7, 631.5， MD=399；95% ci分别为354.5和444.3)。从2005年到2022年，适度运动的性别差距有所扩大(MD=10.9；95% ci -14.2, 35.9， MD=104；95% ci分别为77.8、130.1)。从2009年到2022年，体育和锻炼方面的性别差距有所扩大(PR=1.14；95% ci 1.10, 1.19；公关= 1.22;95% ci分别为1.17、1.27)。从2005年到2022年，走路没有性别差异(MD=-1.4；95% ci -21.2, 18.4， MD=12.5；95% ci分别为-4.9和29.9)。结论：体育运动对整体身体活动水平的贡献尚未得到充分利用，可以在女性中推广，以缩小身体活动的总体性别差距。
{"title":"Gender differences in physical activity and sport participation in adults across 28 European countries between 2005 and 2022","authors":"Katherine B. Owen , Lucy Corbett , Ding Ding , Rochelle Eime , Adrian Bauman","doi":"10.1016/j.annepidem.2024.12.011","DOIUrl":"10.1016/j.annepidem.2024.12.011","url":null,"abstract":"<div><h3>Objective</h3><div>There is a lack of understanding of the specific types and intensities of physical activity driving the gender gap in overall levels of physical activity, and how these activities are changing over time. We examined the gender gap in specific types and intensities of physical activities in European adults from 2005 to 2022.</div></div><div><h3>Study design and methods</h3><div>This repeated cross-sectional study included data from adults from the Eurobarometer (2005–2022) from 28 European countries. Gender differences in meeting physical activity guidelines, sport, walking, moderate, and vigorous activity were examined using prevalence ratios (PR, relative inequalities) and mean differences (MD, absolute differences).</div></div><div><h3>Results</h3><div>Among 123,809 participants, there was no change in the gender gap in meeting physical activity guidelines from 2005 to 2022 (PR = 1.10; 95 % CIs 1.07, 1.14, PR = 1.04; 95 % CIs 1.01, 1.08, respectively). The gender gap in vigorous intensity activity decreased from 2005 to 2022 (MD = 589; 95 % CIs 545.7, 631.5, MD = 399; 95 % CIs 354.5, 444.3, respectively). The gender gap in moderate activity increased from 2005 to 2022 (MD = 10.9; 95 % CIs − 14.2, 35.9, MD = 104; 95 % CIs 77.8, 130.1, respectively). The gender gap in sport and exercise increased from 2009 to 2022 (PR = 1.14; 95 % CIs 1.10, 1.19; PR = 1.22; 95 % CIs 1.17, 1.27, respectively). There was no gender gap in walking between 2005 and 2022 (MD = -1.4; 95 % CIs − 21.2, 18.4, MD = 12.5; 95 % CIs − 4.9, 29.9, respectively).</div></div><div><h3>Conclusions</h3><div>Sport remains an underutilized contributor to overall physical activity levels and could be promoted among women to reduce the overall gender gap in physical activity.</div></div>","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"101 ","pages":"Pages 52-57"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.annepidem.2024.10.009
{"title":"Winners of the American College of Epidemiology Annals of Epidemiology Awards, 2024","authors":"","doi":"10.1016/j.annepidem.2024.10.009","DOIUrl":"10.1016/j.annepidem.2024.10.009","url":null,"abstract":"","PeriodicalId":50767,"journal":{"name":"Annals of Epidemiology","volume":"100 ","pages":"Pages 60-61"},"PeriodicalIF":3.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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