Scott C Zimmerman, Ruijia Chen, Mary Thoma, Lucia Pacca, Jillian Hebert, Alicia Riley, Min Hee Kim, Annie Pederson, Yulin Yang, Peter Buto, Willa D Brenowitz, M Maria Glymour, Ashwin Kotwal, Jacqueline M Torres
{"title":"The Association of Alzheimer's Disease Genetic Risk with Social Connectedness in Middle- And Older-Ages.","authors":"Scott C Zimmerman, Ruijia Chen, Mary Thoma, Lucia Pacca, Jillian Hebert, Alicia Riley, Min Hee Kim, Annie Pederson, Yulin Yang, Peter Buto, Willa D Brenowitz, M Maria Glymour, Ashwin Kotwal, Jacqueline M Torres","doi":"10.1093/aje/kwaf122","DOIUrl":"10.1093/aje/kwaf122","url":null,"abstract":"<p><p>Observational evidence suggests that social connectedness protects against Alzheimer's Disease (AD), but reverse causality has not been ruled out. We evaluated the potential for a reverse path by estimating associations between AD genetic risk score (AD-GRS) and social connectedness across mid and late-life. We used data from 487,194 UK Biobank participants aged 40+ years and considered social connectedness measures capturing social isolation, loneliness, relationship satisfaction, emotional support, and diverse social activity participation. Participants' mean age was 56.5 (SD: 8.2) years. Higher AD-GRS was associated with a lower social isolation score (β = 0.01; 95% CI: -0.014 to -0.001); these associations strengthened with age. Higher AD-GRS was associated with higher levels of family relationship satisfaction (β = 0.01; 95% CI: 0.001 to 0.01), but this association was attenuated with age. Higher AD-GRS was associated with engaging in a wider variety of social activities (β = 0.02, 95% CI: 0.004 to 0.03), with no evidence of heterogeneity by age. Associations with loneliness, friendship relationship quality, and perceived emotional support were null. Overall, we did not find evidence that higher risk of AD is associated with reduced social connectedness. Instead, preclinical AD symptoms may lead to stronger family relationships and lower social isolation.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parker R Tope, Bronner P Gonçalves, Mariam El-Zein, Eduardo L Franco
{"title":"The health related impact of disruptions in cancer care and the Waiting Time Paradox.","authors":"Parker R Tope, Bronner P Gonçalves, Mariam El-Zein, Eduardo L Franco","doi":"10.1093/aje/kwaf128","DOIUrl":"https://doi.org/10.1093/aje/kwaf128","url":null,"abstract":"<p><p>Healthcare system disruptions, such as that caused by the COVID-19 pandemic, can lead to delays in, or lag time to, cancer diagnosis and treatment. In order to quantify the negative impact of disruptions such as this on the health of populations, a better grasp on biases that might affect estimation of effects of diagnosis or treatment delays on clinical outcomes is needed. Here, we discuss some of the methodological difficulties in these analyses, including those posed by what has been referred to as the Waiting Time Paradox. In doing so, we define the effect of lag time using potential outcomes, describe evidence for the Waiting Time Paradox, and present directed acyclic graphs to characterize different contexts when it might occur. Although our discussion is motivated by disruptions in cancer care, accurate quantification of lag time's effect would be valuable for the study of other medical conditions and different types of medical service inefficiencies, as well as in settings where policy changes to minimize waiting time are considered.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tiansheng Wang, Virginia Pate, Richard Wyss, John B Buse, Michael R Kosorok, Til Stürmer
{"title":"High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.","authors":"Tiansheng Wang, Virginia Pate, Richard Wyss, John B Buse, Michael R Kosorok, Til Stürmer","doi":"10.1093/aje/kwaf127","DOIUrl":"https://doi.org/10.1093/aje/kwaf127","url":null,"abstract":"<p><p>We tested a novel high-dimensional approach (using 1 ordinal variable per code with up to four levels: zero, occurred once, sporadically, or frequent) against the standard high-dimensional propensity score (hdPS) method (up to 3 binary variables per code) for detecting heterogeneous treatment effects (HTE). Using the iterative causal forest (iCF) subgrouping algorithm, we analyzed a new-user cohort of 8,075 sodium-glucose cotransporter-2 inhibitors and 7,313 glucagon-like peptide-1 receptor agonists from a 20% random Medicare sample (2015-2019) with ≥1-year parts A/B/D enrollment and without severe renal disease. We extracted the top 200 prevalent codes across diagnoses, procedures, and prescriptions during the 1-year baseline. Subgroup-specific conditional average treatment effects (CATEs) were assessed for 2-year risk differences (aRD) in hospitalized heart failure using inverse-probability treatment weighting. The overall population exhibited an aRD of -0.4% (95% CI -1.1%, 0.2%). Our high-dimensional setting identified patients with ≥2 loop diuretic prescriptions (aRD: -2.6%, 95% CI: -5.0%, -0.2%) as the subgroup with the largest CATE. In contrast, the high-dimensional setting from hdPS identified patients with chronic kidney disease (aRD: -1.7%, 95% CI: -3.6%, 0.2%). Across various sensitivity analyses, our high-dimensional approach more accurately identified expected subgroups with HTE that aligns with prior clinical evidence.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katherine A Ahrens, Jennifer A Hutcheon, Erin C Strumpf, Arijit Nandi, Justin R Ortiz, Teresa Janevic
{"title":"Paid family leave and reduced acute respiratory infections in young infants: Does everyone benefit equally?","authors":"Katherine A Ahrens, Jennifer A Hutcheon, Erin C Strumpf, Arijit Nandi, Justin R Ortiz, Teresa Janevic","doi":"10.1093/aje/kwaf124","DOIUrl":"10.1093/aje/kwaf124","url":null,"abstract":"<p><strong>Background and objectives: </strong>To examine whether the effect of a paid family leave program on acute care encounters for respiratory tract infections (RTI) among young infants differed by subgroups.</p><p><strong>Methods: </strong>We examined 52943 hospitalizations and emergency department visits between Oct 2015 and Feb 2020 among infants aged ≤8 weeks in New York, which introduced paid family leave in January 2018, and four New England control states (Massachusetts, New Hampshire, Vermont, Maine). We conducted a controlled time series analysis that compared observed counts in New York during the putative respiratory virus season (Oct-Mar) in each population subgroup to those predicted in the absence of the policy.</p><p><strong>Results: </strong>Absolute reductions in RTI-related acute care encounters among young infants were greater for Hispanic as compared to non-Hispanic white infants (5.60 fewer cases per 1000 infants [95% CI: -8.74 to -2.51]) and for encounters paid for by Medicaid as compared to private payer (4.22 fewer cases per 1000 [95% CI: -6.45, -2.18]). Findings by Child Opportunity Index 2.0 quintiles showed no clear pattern.</p><p><strong>Conclusions: </strong>Our findings suggest the program may have larger benefits for infants from less advantaged groups.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan R Powell, Maria F Gallo, Payal Chakraborty, Colleen A Reynolds, Morgan Anderson, Parvati Singh
{"title":"Increase in 9-1-1 activations for obstetric-related emergencies following the Dobbs decision in the US.","authors":"Jonathan R Powell, Maria F Gallo, Payal Chakraborty, Colleen A Reynolds, Morgan Anderson, Parvati Singh","doi":"10.1093/aje/kwaf123","DOIUrl":"https://doi.org/10.1093/aje/kwaf123","url":null,"abstract":"<p><p>The United States Supreme Court's Dobbs v. Jackson Women's Health Organization decision in June 2022 may have preceded a surge in 9-1-1 activations for obstetric-related conditions. We used time-series analysis to examine whether the Dobbs decision corresponded with a proximate increase in obstetric-related 9-1-1 activations among reproductive-aged (15-49 years) female patients using national, monthly data from January 2018 to December 2023. Monthly national counts of obstetric-related emergent 9-1-1 activations with patient contact from January 2018 to December 2023 were retrieved from the National Emergency Medical Services Information System (NEMSIS) dataset. Monthly series of non-obstetric 9-1-1 activations among reproductive-aged female patients and all other 9-1-1 activations served as controls. Analysis was also stratified by three state groups per restrictiveness of state abortion policies (protective, mixed, and restrictive) within the first 3 months post Dobbs. A binary indicator of June 2022 with 0-to-3-month lags served as the exposure. Results from time-series analysis showed 913 additional 9-1-1 activations for obstetric-related conditions one month following the Dobbs decision. States with protective abortion policies accounted for about 50% of the national increase. Findings indicate an immediate surge in obstetric-related 9-1-1 activations following the Dobbs ruling, primarily in states with protective abortion policies.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"(Re-)match: adjusting for matching factors in case-control studies may be unnecessary or insufficient.","authors":"Maria E Kamenetsky, Alexander P Keil","doi":"10.1093/aje/kwaf116","DOIUrl":"https://doi.org/10.1093/aje/kwaf116","url":null,"abstract":"","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Causal and Replication Analysis of Claims that Jet Lag Affects Team Sport Performance.","authors":"Matthew S Tenan, Ali R Rezai, Andrew D Vigotsky","doi":"10.1093/aje/kwaf120","DOIUrl":"https://doi.org/10.1093/aje/kwaf120","url":null,"abstract":"<p><p>It is broadly accepted that jet lag impacts human performance in sport; however, this has never been validated within a causal inference framework. Here, we used the potential outcomes framework to determine if jet lag, conditional on game time, causes collegiate football teams to under- or over-perform expectations. We also attempted to replicate seminal non-causal analyses in professional football. Nine collegiate football seasons (2013-2022, omitting 2020), for a total of 6,245 games were analyzed. A generalized additive model with penalized splines and independence weights was used to evaluate interactive causal effects of hours gained/lost in travel and game time on the probability of away teams beating the Las Vegas spread. Furthermore, we used our collegiate cohort to exactly replicate two previous studies purporting to show that NFL teams are adversely affected by jet lag. Jet lag's effects were compatible with the null hypothesis in both the causal analysis (P=0.133) and non-causal replications. Practically, if the effects of jet lag are unclear, then it is also unclear whether interventions to treat jet lag in elite sport are warranted for teams crossing fewer than three time zones if the primary goal is to optimize winning.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oliver Eales, Saras M Windecker, James M McCaw, Freya M Shearer
{"title":"Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data.","authors":"Oliver Eales, Saras M Windecker, James M McCaw, Freya M Shearer","doi":"10.1093/aje/kwaf119","DOIUrl":"https://doi.org/10.1093/aje/kwaf119","url":null,"abstract":"<p><p>Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. Surveillance indicators routinely collected to monitor these trends are often a composite of multiple pathogens. For example, 'influenza-like illness'-routinely monitored as a proxy for influenza infections-is a symptom definition that could be caused by a wide range of pathogens, including multiple subtypes of influenza, SARS-CoV-2, and RSV. Inferred trends from such composite time series may not reflect the trends of any one of the component pathogens, each of which can exhibit distinct dynamics. Although many surveillance systems routinely test a subset of individuals contributing to a surveillance indicator-providing information on the relative contribution of the component pathogens-trends may be obscured by time-varying testing rates or substantial noise in the observation process. Here we develop a general statistical framework for inferring temporal trends of multiple pathogens from routinely collected surveillance data. We demonstrate its application to three different surveillance systems covering multiple pathogens (influenza, SARS-CoV-2, dengue), locations (Australia, Singapore, USA, Taiwan, UK), scenarios (seasonal epidemics, non-seasonal epidemics, pandemic emergence), and temporal reporting resolutions (weekly, daily). This methodology is applicable to a wide range of pathogens and surveillance systems.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stanley Xu, Lina S Sy, Xuan Huang, Vennis Hong, Bing Han, Katia J Bruxvoort, Bruno Lewin, Kimberly J Holmquist, Lei Qian
{"title":"A three-part model for the self-controlled case series design to estimate and characterize adverse event risk in an overlapping risk period after multiple vaccines: application to ischemic stroke following Pfizer-BioNTech bivalent COVID-19 vaccine and influenza vaccine.","authors":"Stanley Xu, Lina S Sy, Xuan Huang, Vennis Hong, Bing Han, Katia J Bruxvoort, Bruno Lewin, Kimberly J Holmquist, Lei Qian","doi":"10.1093/aje/kwaf115","DOIUrl":"https://doi.org/10.1093/aje/kwaf115","url":null,"abstract":"<p><p>This study proposes a three-part model to assess and characterize the risk of serious adverse events (SAEs) when two vaccines are administered on the same day or in close proximity within a self-controlled case series framework. Simulations showed that the three-part model yielded unbiased relative incidences (RIs) after each vaccination and during the overlapping risk period, while censoring follow-up at dose 2 reduced estimation precision but produced unbiased point estimates. Assuming positive multiplicative and positive additive effects, including the overlapping risk period in the first risk interval overestimated the RI after the first dose by 6.0%-26.0%, while including it in the second overestimated the second RI by 7.3%-34.0%. Overall analysis using the three-part model found no increased ischemic stroke risk 42 days after Pfizer-BioNTech bivalent COVID-19 vaccination or after influenza vaccination or during the overlapping risk period among Kaiser Permanente Southern California members <65 years. Among those with a prior-year history of SARS-CoV-2 infection, the overlapping period showed a significantly increased risk (RI=2.74 [95% confidence intervals, 1.07-7.07]), indicating both positive multiplicative and additive effects. Further research is needed to validate these ischemic stroke findings with chart review confirmation and to apply the model to other vaccination scenarios.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naiyu Chen, Emma E McGee, Rachel C Nethery, Lorelei A Mucci, Barbra A Dickerman
{"title":"Guideline-based physical activity and health-related quality of life among prostate cancer survivors: a target trial emulation in the Health Professionals Follow-up Study.","authors":"Naiyu Chen, Emma E McGee, Rachel C Nethery, Lorelei A Mucci, Barbra A Dickerman","doi":"10.1093/aje/kwaf117","DOIUrl":"10.1093/aje/kwaf117","url":null,"abstract":"<p><p>Prostate cancer and its treatment can impact health-related quality of life. Evidence for physical activity strategies sustained over long periods to improve quality of life is limited. Given the limited feasibility of a randomized trial to answer this question, we emulated a target trial of physical activity strategies based on current clinical guidelines and 6-year quality of life using observational data from 1,549 men in the Health Professionals Follow-up Study diagnosed with nonmetastatic prostate cancer between 2004-2016. Eligible individuals were free of conditions that may preclude high levels of physical activity at baseline. We estimated 6-year mean physical quality of life scores (based on EPIC-CP symptom domains; range: 1-12, lower is better) under sustained, dynamic physical activity strategies, adjusting for baseline and time-varying variables using the parametric g-formula. Estimated 6-year mean differences (adherence to physical activity recommendations vs. non-adherence) were 0.1 (95% confidence interval: 0.0,0.2) for bowel symptoms, -0.1 (-0.2,0.1) for urinary incontinence, 0.0 (-0.1,0.2) for urinary irritation/obstruction, -0.3 (-0.7,0.1) for sexual symptoms, and -0.1 (-0.2,0.1) for vitality/hormonal symptoms. Estimated 6-year mean differences comparing adherence to physical activity recommendations vs. no intervention (observed physical activity in this population) were close to zero. Adhering to current physical activity recommendations may help to improve 6-year symptoms in the sexual domain, with little expected influence on symptoms in the bowel, urinary, and vitality/hormonal domains.</p>","PeriodicalId":7472,"journal":{"name":"American journal of epidemiology","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}