AJE advances : research in epidemiology最新文献

{"title":"Risk Factors Measured Around Age 60 as Predictors of Dementia Around Age 80 in the U.S.","authors":"Péter Hudomiet, Michael D Hurd, Susann Rohwedder","doi":"10.1093/ajeadv/uuag036","DOIUrl":"10.1093/ajeadv/uuag036","url":null,"abstract":"<p><p>The number of older adults living with dementia in the United States and worldwide continues to rise. Identifying elevated dementia risk decades before potential onset can help individuals prepare and enable healthcare providers and policymakers to target resources that may delay onset or mitigate consequences. Using nationally representative data from the U.S. Health and Retirement Study (1992-2016), we estimated the probability of dementia at around age 80 as a function of 110 risk factors measured at around age 60, using a recently developed and validated probabilistic dementia measure and longitudinal regression models. Poor physical health, low cognitive ability, fewer than 12 years of education, and greater functional limitations at around age 60 were the strongest predictors of dementia at age 80. Additional predictors included being born in the Southeastern U.S., lacking private health insurance, non-veteran status, having poor vision or hearing, limited work history, disability, diabetes, body mass index ≥ 35, prior stroke, and both alcohol abstention and heavy drinking. Several predictors, such as geographic origin, veteran status, and work history, have received limited attention in prior prediction research. Future research should examine whether these candidate predictors represent causal risk factors rather than simply associations.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Place-based disorder, social cohesion, and physical and mental health outcomes of LGBTQIA+ adults in the United States.","authors":"Nguyen K Tran, David H Rehkopf, Charleigh Flohr, Juno Obedin-Maliver, Annesa Flentje, Mitchell R Lunn","doi":"10.1093/ajeadv/uuag032","DOIUrl":"10.1093/ajeadv/uuag032","url":null,"abstract":"<p><p>Place-based disorder and social cohesion may influence LGBTQIA+ adults' physical and mental health in meaningful ways given their heightened exposure to minority stress and discrimination. However, few studies have examined these associations. Using a sample of 3790 LGBTQIA+ adults in 786 counties from The Population Research in Identity and Disparities for Equality Study, we assessed associations of place-based disorder and social cohesion with depressive symptoms, perceived stress, and physical health using linear mixed models. We tested effect modification by gender modality and ethnoracial group. Residing in counties with higher social cohesion by 1-unit was associated with 1.06 lower points (95% CI -1.56, -0.56) for depressive symptoms, 1.60 lower points (95% CI -2.26, -0.94) for perceived stress, and 1.17 higher points (95% CI 0.43, 1.90) for physical health. Residence in counties with higher place-based disorder by 1-unit was only associated with 1.17 higher points (95% CI 0.32, 2.01) for perceived stress; no association was observed for depressive symptoms or physical health. We did not detect evidence of effect modification by ethnoracial groups, though association between place-based disorder and depressive symptoms appeared to differ by gender modality. Findings indicate that physical and social environments are important to the health of LGBTQIA+ individuals.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"2 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13599614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis estimating aspergillosis and histoplasmosis encounters among inpatients in the United States using multi-regional electronic health record and National Discharge Data, 2014-2020.","authors":"Brittany L Morgan Bustamante, Juliana G E Bartels, Aidan Lee, Natalie J Kane, Rose M Reynolds, Theodore C White, Mark Hoffman, Jennifer Head, Justin V Remais","doi":"10.1093/ajeadv/uuag012","DOIUrl":"10.1093/ajeadv/uuag012","url":null,"abstract":"<p><p>Fungal diseases are an emerging public health threat, yet their epidemiology remains poorly understood due to limited surveillance. Electronic health record (EHR) datasets offer opportunities for studying fungal diseases, but their national representativeness is unclear. We compared inpatient encounters for aspergillosis and histoplasmosis between Oracle EHR Real World Data (OERWD) and the Healthcare Cost and Utilization Project National Inpatient Sample (HCUP NIS), a nationally representative discharge-derived database. We analyzed inpatient encounters from both databases and applied calibration weights to OERWD to improve alignment with national benchmarks. Relative differences (RDs) in encounter counts were compared using Wald tests. Survey-weighted quasibinomial models estimated prevalence and 95% CIs; temporal trends were evaluated with Mann-Kendall tests. Dataset differences were assessed using interaction terms between a dataset indicator and demographic or geographic subgroups. OERWD had fewer total and disease-specific encounters than HCUP NIS. Weighting improved precision, and prevalence estimates did not differ significantly for most strata (31 of 42), indicating broad agreement in demographic and geographic distributions. Both datasets showed higher aspergillosis prevalence in the Pacific division and higher histoplasmosis prevalence in the East South Central division, with males and older adults consistently exhibiting higher prevalence. These findings indicate that, while HCUP NIS is optimal for nationally representative prevalence estimates, weighting EHR-derived data can reproduce key demographic and geographic prevalence patterns observed in national benchmarks. Benchmarking EHR-derived estimates against national standards represents an important step toward establishing their representativeness and supporting future studies leveraging EHR data to investigate fungal disease epidemiology in greater clinical detail.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13372256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Enrollment and Representativeness of MACS and WIHS Study Participants in the newly merged MACS/WIHS Combined Cohort Study (MWCCS).","authors":"Gypsyamber D'Souza, Ryan Herrschaft, Phyllis C Tien, Todd T Brown, Seble G Kassaye, Sabina Haberlen, Brad Aouizerat, Catalina Ramirez, Matthew J Mimiaga, Audrey L French, Charles R Rinaldo, Anjali Sharma, Margaret A Fischl, Valentina Stosor, Igho Ofotokun, Jacquett Batson, Susan Holman, Mirjam-Colette Kempf","doi":"10.1093/ajeadv/uuaf025","DOIUrl":"10.1093/ajeadv/uuaf025","url":null,"abstract":"","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12989717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147477886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychological distress, racism, sexism, cissexism, heterosexism, ageism, and sizeism: analyzing patterns of association using explicit and implicit measures of discrimination.","authors":"Nancy Krieger, Nykesha Johnson, Jarvis T Chen, Sari L Reisner, Maddalena Marini, Merrily E LeBlanc, Kenneth H Mayer, Apriani Oendari, Guale Valdez, Tanveer Khan, Donna M Bright","doi":"10.1093/ajeadv/uuag007","DOIUrl":"10.1093/ajeadv/uuag007","url":null,"abstract":"<p><p>Research on discrimination and health has predominantly relied on explicit self-report data for single types of discrimination; use of implicit measures is scant. Our exploratory cross-sectional study investigated associations between psychological distress (Kessler-6 [K6] score) and implicit and explicit measures for 6 types of discrimination: racism, sexism, heterosexism, cissexism, ageism, and sizeism. The 699 participants were recruited (May 28, 2020 to August 4, 2022) from rosters of 3 Boston, MA, community health centers. Covariate-adjusted linear regression models estimated associations between K6 scores and the discrimination measures. Participants' mean K6 score was 7.47 (SD = 5.15); 17.0% experienced \"severe psychological distress\" (K6 ≥ 13). Target vs dominant groups experienced higher discrimination for both the implicit and explicit measures (<i>P</i> < .01). Participants who both self-reported <i>and</i> implicitly recognized 4-6 vs 0-3 types of discrimination had elevated K6 (adjusted model <i>β</i>: 0.29 [95% CI, 0.07-0.51]). In crude models, K6 scores were positively associated with self-reported discrimination for all 6 types, and with implicit measures for sexism, heterosexism, cissexism, and ageism. In adjusted models, K6 was positively associated with <i>both</i> explicit and implicit metrics only for sexism, for example, for self-reported experiences of sexism among women (<i>β</i>: 0.25 [95% CI, 0.13-0.36]) and nonbinary/genderqueer participants (<i>β</i>: 0.26 [95% CI, 0.13-0.39]), and for implicit recognition of sexism toward women among women (<i>β</i>: 0.12 [95% CI, -0.01 to 0.24]). Using both implicit and explicit measures for multiple discrimination types, with attention to differences between target vs dominant groups, is warranted.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13175132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147966967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The LIBRA index of modifiable risk factors for mild cognitive impairment and dementia in a late-life population-based cohort study.","authors":"Yueting Wang, Erin Jacobsen, Tiffany F Hughes, Beth E Snitz, Chung-Chou H Chang, Mary Ganguli","doi":"10.1093/ajeadv/uuaf018","DOIUrl":"10.1093/ajeadv/uuaf018","url":null,"abstract":"<p><p>The Lifestyle for Brain Health (LIBRA) index, a weighted measure of modifiable risk factors for dementia, has been widely applied in European cohorts but less explored in US populations. We applied it to a cohort aged 65+ years, sampled from a relatively low-socioeconomic US region, and compared its predictive validity for incident mild cognitive impairment (MCI) and incident dementia risk. We used both reference factor weights derived from published meta-analyses and cluster-specific factor weights based on estimation results using our own cohort data. We also compared the original LIBRA index with an updated version (LIBRA2), which added three new risk factors including hearing impairment, sleep disturbance, and low social contact. We used joint modeling to account for informative attrition over time. Higher baseline LIBRA indexes were consistently associated with increased dementia risk across factor weight estimation methods. LIBRA2 demonstrated better predictive validity for incident dementia compared to MCI, further improving its performance. Cohort-specific factor weights strengthened associations with cognitive impairment risk but may limit comparability across studies. These findings contribute to valid use of LIBRA for dementia prediction in a late-life US cohort and support the incorporation of additional factors and cohort-specific adjustments to enhance its utility in diverse populations.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12713165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.","authors":"Keyu Li, Charles Wood, Liz Nichols, Zachary D Calhoun, Nrupen A Bhavsar, David Carlson","doi":"10.1093/ajeadv/uuaf011","DOIUrl":"10.1093/ajeadv/uuaf011","url":null,"abstract":"<p><p>Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass index across the full pediatric spectrum. Additionally, they consider only the immediate neighborhood within which a child lives, ignoring contextual factors from expanded (i.e., distal) neighborhoods. This study uses expanded neighborhoods' social, environmental, and climate data to improve individual-level body mass index prediction-from underweight through obesity-using a novel machine learning approach. We obtained demographic and clinical data from the electronic health records of the Duke University Health System, identifying 12,226 children aged 6-18 years in Durham County, North Carolina, with body mass index data from 2014 to 2022. Participants' data were linked to socioeconomic and environmental information at the census block group level. We captured expanded neighborhood effects with a graph neural network and combined this information with individual-level factors to predict body mass index. Our model predicted body mass index more accurately than simpler models for children aged 6-11 (R² = 0.234, mean absolute error = 3.352, root mean square error = 4.370) and 12-18 (R² = 0.147, mean absolute error = 4.980, root mean square error = 6.343) using all features. Key predictive factors identified included rent burden, poverty rate, and tree coverage. This research highlights the value of including broader socioeconomic and environmental factors in body mass index prediction, offering insights that could guide targeted, neighborhood-level interventions.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12539641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145350924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of estimation in Mendelian randomization: should Mendelian randomization investigations provide estimates?","authors":"Benjamin Woolf, Stephen Burgess","doi":"10.1093/ajeadv/uuaf003","DOIUrl":"10.1093/ajeadv/uuaf003","url":null,"abstract":"<p><p>Mendelian randomization (MR) makes causal claims by treating genetic variation in an analogous way to randomization in a clinical trial. MR investigations can be viewed as analogous to a randomized encouragement design, in that genetic variants do not determine the precise level of an exposure, but increase liability to it. As such, an MR estimate typically does not represent an achievable or well-defined causal effect in terms of the exposure, as it reflects the impact of a life-long shift in the trajectory of the exposure, which likely differs between individuals. We advocate for MR investigations to be performed to assess evidence for a causal hypothesis, rather than to estimate a well-defined causal quantity. MR estimates are useful to combine evidence across genetic variants, to assess the validity of variants as instruments, to provide confidence intervals, and to compare estimates across outcomes. However, numerical estimates from MR should not be over-interpreted. The value of an MR investigation is not to quantify the magnitude of effect for a well-defined intervention in the exposure. Instead, it provides a distinct source of evidence to increase or decrease confidence in a causal hypothesis, which can be triangulated with evidence from other sources.</p>","PeriodicalId":521078,"journal":{"name":"AJE advances : research in epidemiology","volume":"1 1","pages":"uuaf003"},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7618185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}