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Dementia incidence is lower among Asian Americans than among Whites, despite higher prevalence of type 2 diabetes, a well-known dementia risk factor. Determinants of dementia, including type 2 diabetes, have rarely been studied in Asian Americans. We followed 4846 Chinese, 4129 Filipino, 2784 Japanese, 820 South Asian, and 123 360 non-Latino White members of a California-based integrated health-care delivery system from 2002 to 2020. We estimated dementia incidence rates by race/ethnicity and type 2 diabetes status, and we fitted Cox proportional hazards and Aalen additive hazards models for the effect of type 2 diabetes (assessed 5 years before baseline) on age of dementia diagnosis, controlling for sex/gender, educational attainment, nativity, height, race/ethnicity, and a race/ethnicity × diabetes interaction. Type 2 diabetes was associated with higher dementia incidence in Whites (hazard ratio [HR] = 1.46; 95% CI, 1.40-1.52). Compared with Whites, the estimated effect of diabetes was larger in South Asians (HR = 2.26; 95% CI, 1.48-3.44), slightly smaller in Chinese (HR = 1.32; 95% CI, 1.08-1.62) and Filipino (HR = 1.31; 95% CI, 1.08-1.60) individuals, and similar in Japanese individuals (HR = 1.44; 95% CI, 1.15-1.81). Heterogeneity in this association across Asian subgroups may be related to type 2 diabetes severity. Understanding this heterogeneity may inform prevention strategies to prevent dementia for all racial and ethnic groups. [ABSTRACT FROM AUTHOR]

For Black students in the United States, attending schools with a higher proportion of White students is associated with worse mental and physical health outcomes in adolescence/early adulthood. To our knowledge, no prior studies have evaluated the association between school racial/ethnic composition from kindergarten through grade 12 and later-life mental health. In a cohort of Black adults aged ≥50 years in Northern California who retrospectively reported (2017-2020) school racial/ethnic composition for grades 1, 6, 9, and 12, we assessed the association between attending a school with mostly Black students versus not and mid-/late-life depressive symptoms (8-item Patient-Reported Outcomes Measurement Information System (PROMIS) depression score, standardized to the 2000 US adult population) using age-, sex/gender-, southern US birth–, and parental education–adjusted generalized estimating equations, and assessed effect modification by the presence of a caring teacher/staff member. Levels of later-life depressive symptoms were lower among those who attended schools with mostly Black students in grades 1 and 6 (β = −0.12 [95% CI, −0.23 to 0.00] and β = −0.11 [95% CI, −0.22 to 0.00], respectively). In grade 6, this difference was larger for students without an adult at school who cared about them (β = −0.29 [95% CI, −0.51 to −0.07] vs β = −0.04 [95% CI, −0.17 to 0.09]). Among Black Americans, experiencing early schooling with mostly Black students may have later-life mental health benefits; this protective association appears more important for students without the presence of caring teachers/staff. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Dementia represents a growing public health burden with large social, racial, and ethnic disparities. The etiology of dementia is poorly understood, and the lack of robust biomarkers in diverse, population-representative samples is a barrier to moving dementia research forward. Existing biomarkers and other measures of pathology—derived from neuropathology, neuroimaging, and cerebrospinal fluid samples—are commonly collected from predominantly White and highly educated samples drawn from academic medical centers in urban settings. Blood-based biomarkers are noninvasive and less expensive, offering promise to expand our understanding of the pathophysiology of dementia, including in participants from historically excluded groups. Although largely not yet approved by the Food and Drug Administration or used in clinical settings, blood-based biomarkers are increasingly included in epidemiologic studies on dementia. Blood-based biomarkers in epidemiologic research may allow the field to more accurately understand the multifactorial etiology and sequence of events that characterize dementia-related pathophysiological changes. As blood-based dementia biomarkers continue to be developed and incorporated into research and practice, we outline considerations for using them in dementia epidemiology, and illustrate key concepts with Alzheimer's Disease Neuroimaging Initiative (2003–present) data. We focus on measurement, including both validity and reliability, and on the use of dementia blood-based biomarkers to promote equity in dementia research and cognitive aging. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Incomplete longitudinal data are common in life-course epidemiology and may induce bias leading to incorrect inference. Multiple imputation (MI) is increasingly preferred for handling missing data, but few studies explore MI-method performance and feasibility in real-data settings. We compared 3 MI methods using real data under 9 missing-data scenarios, representing combinations of 10%, 20%, and 30% missingness and missing completely at random, at random, and not at random. Using data from Health and Retirement Study (HRS) participants, we introduced record-level missingness to a sample of participants with complete data on depressive symptoms (1998–2008), mortality (2008–2018), and relevant covariates. We then imputed missing data using 3 MI methods (normal linear regression, predictive mean matching, variable-tailored specification), and fitted Cox proportional hazards models to estimate effects of 4 operationalizations of longitudinal depressive symptoms on mortality. We compared bias in hazard ratios, root mean square error, and computation time for each method. Bias was similar across MI methods, and results were consistent across operationalizations of the longitudinal exposure variable. However, our results suggest that predictive mean matching may be an appealing strategy for imputing life-course exposure data, given consistently low root mean square error, competitive computation times, and few implementation challenges. [ABSTRACT FROM AUTHOR]

The article discusses the principles and structure of collider stratification bias, along with underlying theoretical framework. Topics include illustration of the relationship between height and atrial fibrillation among National Basketball Association (NBA) players, how conditioning on a collider can introduce a noncausal association between previously uncorrelated variables in a selected sample, and relationship of collider stratification bias with selection bias, and

Dementia is a complex, progressive syndrome characterized by cognitive decline and disability. Gold-standard dementia diagnosis requires several hours of cognitive and clinical assessment and review by a panel of clinicians and is infeasible in large population-based cohort studies. Alternatively, algorithmic dementia classification methods, which use models that take into account measures of cognition and functional limitations or cognitive and functional-limitation score cutoffs, have been developed to predict dementia status for participants in large studies. Developing accurate dementia classification algorithms is crucial for high-quality studies of the distribution and determinants of dementia. The accompanying article by Nichols et al. (Am J Epidemiol. 2023;192(4):520–534) assesses differences in associations of measures of cognition and functional limitations with prevalent dementia versus incident dementia and discusses implications for algorithmic dementia classification in research studies. This work highlights important opportunities for tailoring measures of cognition and functional limitations to study goals by selecting optimal measures and developing and validating algorithms specific to study needs. Combining efficient, high-quality assessments of cognition and functional limitations with innovative study designs will facilitate collection of higher-quality measurements in larger samples and support future development of accurate dementia classifications, ultimately leading to more impactful epidemiologic studies. [ABSTRACT FROM AUTHOR]

Objectives Telephone-administered cognitive assessments are a cost-effective and sometimes necessary alternative to face-to-face assessments. There is limited information in large studies concerning mode effects, or differences in cognition attributable to the assessment method, as a potential measurement threat. We evaluated mode effects on cognitive scores using a population-based sample of community-living older adults. Methods We used data from participants aged 65–79 in the 2014 Health and Retirement Study for whom the interview mode was randomized (n = 6,825). We assessed mode differences in test means, whether mode modifies associations of cognition with criterion variables, and formal measurement invariance testing. Results Relative to face-to-face assessment, telephone assessment was associated with higher scores for memory and calculation (0.06 to 0.013 standard deviations [ SD ]) and lower scores for nonmemory items (−0.09 to −0.01 SD). Cognition was significantly differentially related to instrumental activities of daily living difficulty depending on assessment mode. Measurement invariance testing identified evidence of mode differences in certain tests as a function of mode: adjusting for underlying cognition, the largest mode differences in memory and attention: immediate noun recall, delayed word recall, and serial-7s scores were higher given telephone administration. Discussion Differences by mode of administration are apparent in cognitive measurement in older adults, albeit to a small degree in our study, and most pronounced for tests of memory and attention. The importance of accounting for mode differences ultimately depends on one's research question and study sample: not all associations may be affected by mode differences, and such modification may only be apparent among those with lower cognitive functioning. [ABSTRACT FROM AUTHOR]

Alzheimer disease (AD) is a progressive disorder common among older adults and culminating in profound cognitive impairments and high mortality risk. The US Food and Drug Administration (FDA) recently provided accelerated approval for Aduhelm, a medication for AD treatment. Aduhelm (Biogen Inc. Cambridge, Massachusetts) has been described as the first disease-modifying treatment for AD but has not been demonstrated to improve patients' cognitive or functional outcomes. In this commentary, we describe why Aduhelm approval was controversial and aspects of the current evidence of special pertinence to epidemiologists. The FDA decision was based primarily on 2 randomized controlled trials (RCTs), both terminated early, with conflicting findings about the cognitive benefits of Aduhelm. Both RCTs showed important adverse effects of the medication. The FDA cited the documented reduction in brain amyloid, an AD biomarker hypothesized as a surrogate outcome, to justify accelerated approval. Despite lack of racial/ethnic diversity in the RCT participants, concerns about health disparities have been invoked to argue for public funding of this expensive medication. The Centers for Medicare and Medicaid Services recently made a "Coverage with Evidence Development" determination for Aduhelm and similar medications. We end by describing how innovative study designs could accelerate postapproval research and evaluate the proposed surrogate outcomes. [ABSTRACT FROM AUTHOR]

Background: Cancer is inversely associated with dementia. Using simulations, we examined whether this inverse association may be explained by dementia diagnosis timing, including death before dementia diagnosis and differential diagnosis patterns by cancer history.Methods: We used multistate Markov simulation models to generate cohorts 65 years of age and free of cancer and dementia at baseline; follow-up for incident cancer (all cancers, breast, prostate, and lung cancer), dementia, dementia diagnosis among those with dementia, and death occurred monthly over 30 years. Models specified no true effect of cancer on dementia, and used age-specific transition rates calibrated to U.S. population and cohort data. We varied the average lapse between dementia onset and diagnosis, including nondifferential and differential delays by cancer history, and examined observed incidence rate ratios (IRRs) for the effect of cancer on dementia diagnosis.Results: Nondifferential dementia diagnosis delay introduced minimal bias (IRRs = 0.98-1.02) for all cancer, breast, and prostate models and substantial bias (IRR = 0.78) in lung cancer models. For the differential dementia diagnosis delay model of all cancer types combined, simulation scenarios with ≥20% lower dementia diagnosis rate (additional 4.5-month delay) in those with cancer history versus without yielded results consistent with literature estimates. Longer dementia diagnosis delays in those with cancer and higher mortality in those with cancer and dementia yielded more bias.Conclusions: Delays in dementia diagnosis may play a role in the inverse cancer-dementia relationship, especially for more fatal cancers, but moderate differential delays in those with cancer were needed to fully explain the literature-reported IRRs. [ABSTRACT FROM AUTHOR]

Background: Despite growing research on the association between discrimination and disparities in cognitive aging, an evidence gap remains on how the association varies by racial/ethnic group. This study evaluates the associations of experiences of discrimination with cognitive function and whether these associations varied by race/ethnicity and nativity.Method: Using the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) cohort (N = 1 712) with approximately equal groups of Black, White, Latino, and Asian community-dwelling older adults aged 65 years and older, we evaluated the associations between self-reported experiences of everyday and major lifetime discrimination with overall cognitive performance and domain-specific cognition (verbal episodic memory, semantic memory, and executive functioning) across race/ethnicity and nativity. Linear regression models examined the cross-sectional association between self-reported experiences of everyday and major lifetime discrimination with z-standardized coefficients for cognition. We tested for effect modification by race and nativity. All models controlled for age, sex, and education.Results: Among KHANDLE participants (mean age: 76 years; SD: 6.8), everyday discrimination was not associated with cognitive scores. Major lifetime discrimination was associated with better average cognitive scores among Black participants but not among other racial/ethnic groups. Major lifetime discrimination was associated with better average cognitive scores among U.S.-born but not among non-U.S.-born individuals.Conclusion: Our findings do not imply that discrimination improves cognition, but rather suggest that future research should include more detailed measures on discrimination and unfair treatment that could help disentangle the extent to which relationships are causal or reflect some other underlying factor. [ABSTRACT FROM AUTHOR]

Objectives Understanding racial/ethnic disparities in late-life cognitive health is a public health imperative. We used baseline data from the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study to examine how age, education, gender, and clinical diagnosis, a proxy for brain health, are associated with cross-sectional measures of cognition in diverse racial/ethnic groups. Methods Comprehensive measures of cognition were obtained using the Spanish and English Neuropsychological Assessment Scales and the National Institutes of Health Toolbox Cognitive Health Battery in a sample of 1,695 KHANDLE participants (Asians 24%, Blacks 26%, Latinos 20%, Whites 29%). A 25% random subsample was clinically evaluated and diagnosed with normal cognition, mild cognitive impairment (MCI), or dementia. Cognitive test scores were regressed on core demographic variables and diagnosis in the combined sample and in multiple group analyses stratified by racial/ethnic group. Results Race/ethnicity and education were variably associated with test scores with strongest associations with tests of vocabulary and semantic memory. Older age was associated with poorer performance on all measures, and gender differences varied across cognitive tests. Clinical diagnosis of MCI or dementia was associated with average decrements in test scores that ranged from −0.41 to −0.84 SD , with largest differences on tests of executive function and episodic memory. With few exceptions, associations of demographic variables and clinical diagnosis did not differ across racial/ethnic groups. Discussion The robust associations of cognitive test results with clinical diagnosis independent of core demographic variables and race/ethnicity support the validity of cognitive tests as indicators for brain health in diverse older adults. [ABSTRACT FROM AUTHOR]

Background: Midlife cardiovascular risk factors (CVRF) increase risk of dementia. Black Americans experience an elevated prevalence of CVRF and dementia. However, little is known of how CVRF prior to midlife affect late-life cognition. We examined CVRF in adolescence, young adulthood, and midlife with late-life cognition in The Study of Healthy Aging in African Americans (STAR).Methods: STAR assesses cognitive aging among 764 Black Americans ages ≥50 (mean age=69;SD=9;range 53-95). Participants' body mass index, blood pressure, glucose, and total cholesterol were collected during Multiphasic Health Check-ups (MHC;1964-1985). At STAR baseline (2018-2019), executive function, verbal episodic memory, and semantic memory were measured using the Spanish and English Neuropsychological Assessment Scales. Linear regression models examined associations between CVRF and cognition adjusting for demographics and years since MHC.Results: At MHC, 36% of participants had 1 CVRF and 26% had ≥2. Twenty-two percent of participants were adolescents (ages:12-20), 62% young adults (ages:21-34), and 16% midlife adults (ages:35-56). Overweight/obesity was not associated with cognition. Hypertension was associated with worse executive function [β(95%CI):-0.14(-0.28,-0.0003)] and verbal episodic memory [β(95%CI):-0.22(-0.37,-0.07)] compared to normotension. Diabetes was associated with worse executive function [β(95%CI):-0.43(-0.83,-0.03)]. Having ≥2 CVRF (versus 0) was associated with worse executive function [β(95%CI):-0.19(-0.34,-0.03)] and verbal episodic memory [β(95%CI):-0.25(-0.41,-0.08)]. Adolescents with hypertension had lower late-life executive function compared to normotensive adolescents [β(95%CI):-0.39(-0.67,-0.11)]. Young adulthood hypertension [β(95%CI):-0.29(-0.49,-0.09)] and midlife hyperlipidemia [β(95%CI):-0.386(-0.70,-0.02)] were associated with lower verbal episodic memory.Conclusions: Among Black Americans, lifecourse CVRF were associated with poorer executive function and verbal episodic memory emphasizing the importance of cardiovascular health on the aging brain. [ABSTRACT FROM AUTHOR]

Objective: It is unclear whether relatively low glycated haemoglobin (HbA1c) levels are beneficial or harmful for the long-term health outcomes among people without diabetes. We aimed to investigate the association between low HbA1c levels and mortality among the US general population.Methods: This study includes a nationally representative sample of 39 453 US adults from the National Health and Nutrition Examination Surveys 1999-2014, linked to mortality data through 2015. We employed the parametric g-formula with pooled logistic regression models and the ensemble machine learning algorithms to estimate the time-varying risk of all-cause and cardiovascular mortality by HbA1c categories (low, 4.0 to <5.0%; mid-level, 5.0 to <5.7%; prediabetes, 5.7 to <6.5%; and diabetes, ≥6.5% or taking antidiabetic medication), adjusting for 72 potential confounders including demographic characteristics, lifestyle, biomarkers, comorbidities and medications.Results: Over a median follow-up of 7.5 years, 5118 (13%) all-cause deaths, and 1116 (3%) cardiovascular deaths were observed. Logistic regression models and machine learning algorithms showed nearly identical predictive performance of death and risk estimates. Compared with mid-level HbA1c, low HbA1c was associated with a 30% (95% CI, 16 to 48) and a 12% (95% CI, 3 to 22) increased risk of all-cause mortality at 5 years and 10 years of follow-up, respectively. We found no evidence that low HbA1c levels were associated with cardiovascular mortality risk. The diabetes group, but not the prediabetes group, also showed an increased risk of all-cause mortality.Conclusions: Using the US national database and adjusting for an extensive set of potential confounders with flexible modelling, we found that adults with low HbA1c were at increased risk of all-cause mortality. Further evaluation and careful monitoring of low HbA1c levels need to be considered. [ABSTRACT FROM AUTHOR]

Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature. [ABSTRACT FROM AUTHOR]

The majority of people living with Alzheimer disease (AD) and related dementias are women. Longer life expectancy is one factor thought to contribute to this observation, but possible sex-specific biological mechanisms have received considerable attention from the research community. In the current issue of the Journal , Buckley et al. (Am J Epidemiol. 2019;188(7):1213–1223) use death certificate information on all deaths occurring among adults aged ≥60 years in Australia between 2006 and 2014 to evaluate sex/gender differences in rates of death with dementia (all types), AD dementia, and vascular dementia listed on the death certificate. The paper by Buckley et al. highlights several important methodological challenges for research examining sex/gender differences in risk of AD and related dementias, including challenges in measurement, survival bias and competing risks, and selection bias arising from sample selection. The current evidence on possible sex-specific biological risk factors for AD is intriguing, but there are numerous alternative explanations for differences in AD dementia and AD biomarkers between women and men. Triangulation of evidence from study designs with different strengths and weaknesses and transdisciplinary collaboration will be vital to generating conclusive evidence about sex/gender differences in risk of AD and related dementias. [ABSTRACT FROM AUTHOR]

Background: The relationship between education and late-life cognitive decline is controversial. Selective survival between early life, when education is typically completed, and late life, when cognitive ageing studies take place, could attenuate effect estimates.Methods: We quantified potential survival bias (collider-stratification bias) in estimation of the effect of education on late-life cognitive decline by simulating hypothetical cohorts of 20-year-olds and applying cumulative mortality from US life tables. For each of four causal scenarios (2000 replications each), we compared the estimated versus causal effect of education on cognitive decline over 9 years, starting at age 60, 75 or 90 in random samples of n = 2000 people who survived to each age.Results: Effects of education on cognitive decline were underestimated when both education and U, another determinant of cognitive decline, influenced mortality (collider-stratification bias). The magnitude of bias was sensitive to the magnitude of the effect of U on cognitive decline and whether there was a multiplicative interaction between education and U on mortality. For example, when there was a multiplicative interaction between education and U on mortality, 95% confidence interval coverage of the causal effect ranged from 83.4% to 50.4% at age 60 and 25.8% to 0.2% at age 90.Conclusions: Selective survival could lead to underestimation of effects of education on late-life cognitive decline. Our simulations map survival bias to testable assumptions about underlying causal structures. [ABSTRACT FROM AUTHOR]

Background: Walking speed is associated with functional status and all-cause mortality. Yet the relationship between walking speed and stroke, also a leading cause of disability, remains poorly understood, especially in older Latino adults who suffer from a significant burden of stroke.Methods: A total of 1,486 stroke-free participants from the Sacramento Area Latino Study on Aging, aged 60 and older at baseline in 1998-1999, were followed annually through 2010. Participants reported their usual walking speed outdoors which was classified into slow, medium, or fast. We also assessed timed tandem walk ability (unable or eight or more errors vs less than eight errors). We ascertained three incident stroke endpoints: total stroke, nonfatal stroke, and fatal stroke. Using Cox proportional hazards models, we estimated hazard ratios (HRs) for stroke at different walking speed and timed tandem walk categories.Results: Over an average of 6 years of follow-up (SD = 2.8), the incidence rate of total strokes was 23.2/1,000 person-years for slow walkers compared to 15.6/1,000 person-years for medium walkers, and 7.6/1,000 person-years for fast walkers. In Cox models adjusted for sociodemographics, cardiovascular risk, cognition and functional status, and self-rated health, the hazard of total stroke was 31% lower for medium walkers (HR: 0.69, 95% confidence interval [CI]: 0.47, 1.02) and 56% lower for fast walkers (HR: 0.44, 95% CI: 0.24, 0.82) compared with slow walkers. We found similar associations with timed tandem walk ability (fully adjusted HR: 0.66, 95% CI: 0.45, 0.98).Conclusions: Our findings suggest perceived walking speed captures more than self-rated health alone and is a strong risk factor for stroke risk in Latino older adults. [ABSTRACT FROM AUTHOR]

Bias due to selective mortality is a potential concern in many studies and is especially relevant in cognitive aging research because cognitive impairment strongly predicts subsequent mortality. Biased estimation of the effect of an exposure on rate of cognitive decline can occur when mortality is a common effect of exposure and an unmeasured determinant of cognitive decline and in similar settings. This potential is often represented as collider-stratification bias in directed acyclic graphs, but it is difficult to anticipate the magnitude of bias. In this paper, we present a flexible simulation platform with which to quantify the expected bias in longitudinal studies of determinants of cognitive decline. We evaluated potential survival bias in naive analyses under several selective survival scenarios, assuming that exposure had no effect on cognitive decline for anyone in the population. Compared with the situation with no collider bias, the magnitude of bias was higher when exposure and an unmeasured determinant of cognitive decline interacted on the hazard ratio scale to influence mortality or when both exposure and rate of cognitive decline influenced mortality. Bias was, as expected, larger in high-mortality situations. This simulation platform provides a flexible tool for evaluating biases in studies with high mortality, as is common in cognitive aging research. [ABSTRACT FROM AUTHOR]