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Objectives: Few researchers have evaluated whether school racial segregation, a key manifestation of structural racism, affects child health, despite its potential impacts on school quality, social networks, and stress from discrimination. We investigated whether school racial segregation affects Black children's health and health behaviors., Methods: We estimated the association of school segregation with child health, leveraging a natural experiment in which school districts in recent years experienced increased school segregation. School segregation was operationalized as the Black-White dissimilarity index. We used ordinary least squares models as well as quasi-experimental instrumental variables analysis, which can reduce bias from unobserved confounders. Data from the Child Development Supplement of the Panel Study of Income Dynamics (1997-2014, n = 1248 Black children) were linked with district-level school segregation measures. Multivariable regressions were adjusted for individual-, neighborhood-, and district-level covariates. We also performed subgroup analyses by child sex and age., Results: In instrumental variables models, a one standard deviation increase in school segregation was associated with increased behavioral problems (2.53 points on a 27-point scale; 95% CI, 0.26 to 4.80), probability of having ever drunk alcohol (0.23; 95% CI, 0.049 to 0.42), and drinking at least monthly (0.20; 95% CI, 0.053 to 0.35). School segregation was more strongly associated with drinking behaviors among girls., Conclusions: School segregation was associated with worse outcomes on several measures of well-being among Black children, which may contribute to health inequities across the life span. These results highlight the need to promote school racial integration and support Black youth attending segregated schools., (Copyright © 2022 by the American Academy of Pediatrics.)

Background: It is unknown whether an incident cancer diagnosis differentially impacts acute and long-term memory aging between older White and Black Americans., Methods: Incident cancer diagnoses and memory (immediate and delayed recall, combined with proxy-reported memory) were assessed at biennial study interviews in the US Health and Retirement Study (N=14,235, 1998-2016). We used multivariable segmented linear mixed-effects models to evaluate the rate of change in standardized memory score (SD/decade) in the years before, acutely at the time of, and in the years following an incident cancer diagnosis, compared to cancer-free adults, by race., Results: Black participants experienced faster memory decline than White participants (cancer-free group: -1.211 vs. -1.077; P<0.0001). An incident cancer diagnosis was associated with an acute memory drop in White, but not Black participants (-0.065 vs. 0.024; P<0.0001). However, White cancer survivors experienced slower memory decline than cancer-free White adults before and after diagnosis, but this memory advantage was not observed among Black cancer survivors., Conclusions: Racial disparities in memory aging are not modified by an incident cancer diagnosis. The acute cancer-related memory decline and long-term memory advantage experienced by White, but not Black, cancer survivors relative to cancer-free older adults, requires further investigation., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Importance: Racial and ethnic inequities in COVID-19 mortality may be driven by occupation and education, but limited evidence has assessed these mechanisms., Objective: To estimate whether occupational characteristics or educational attainment explained the associations between race and ethnicity and COVID-19 mortality., Design, Setting, and Participants: This population-based retrospective cohort study of Californians aged 18 to 65 years linked COVID-19 deaths to population estimates within strata defined by race and ethnicity, gender, age, nativity in the US, region of residence, education, and occupation. Analysis was conducted from September 2020 to February 2022., Exposures: Education and occupational characteristics associated with COVID-19 exposure (essential sector, telework option, wages)., Main Outcomes and Measures: All confirmed COVID-19 deaths in California through February 12, 2021. The study estimated what COVID-19 mortality would have been if each racial and ethnic group had (1) the COVID-19 mortality risk associated with the education and occupation distribution of White people and (2) the COVID-19 mortality risk associated with the lowest-risk educational and occupational positions., Results: Of 25 235 092 participants (mean [SD] age, 40 [14] years; 12 730 395 [50%] men), 14 783 died of COVID-19, 8 125 565 (32%) had a Bachelor's degree or higher, 13 345 829 (53%) worked in essential sectors, 11 783 017 (47%) could not telework, and 12 812 095 (51%) had annual wages under $51 700. COVID-19 mortality ranged from 15 deaths per 100 000 for White women and Asian women to 139 deaths per 100 000 for Latinx men. Accounting for differences in age, nativity, and region of residence, if all races and ethnicities had the COVID-19 mortality associated with the occupational characteristics of White people (sector, telework, wages), COVID-19 mortality would be reduced by 10% (95% CI, 6% to 14%) for Latinx men, but increased by 5% (95% CI, -8% to 17%) for Black men. If all working-age Californians had the COVID-19 mortality associated with the lowest-risk educational and occupational position (Bachelor's degree, nonessential, telework, and highest wage quintile), there would have been 43% fewer COVID-19 deaths among working-age adults (8441 fewer deaths; 95% CI, 32%-54%), with the largest absolute risk reductions for Latinx men (3755 deaths averted; 95% CI, 3304-4255 deaths) and Latinx women (2329 deaths averted; 95% CI, 2038-2621 deaths)., Conclusions and Relevance: In this population-based cohort study of working-age California adults, occupational disadvantage was associated with excess COVID-19 mortality for Latinx men. For all racial and ethnic groups, excess risk associated with low-education, essential, on-site, and low-wage jobs accounted for a substantial fraction of COVID-19 mortality.

Importance: Identifying the youngest age when Alzheimer disease (AD) influences cognition and the earliest affected cognitive domains will improve understanding of the natural history of AD and approaches to early diagnosis., Objective: To evaluate the age at which cognitive differences between individuals with higher compared with lower genetic risk of AD are first apparent and which cognitive assessments show the earliest difference., Design, Setting, and Participants: This cross-sectional study used data from UK Biobank participants of European genetic ancestry, aged 40 years or older, who contributed genotypic and cognitive test data from January 1, 2006, to December 31, 2015. Data analysis was performed from March 10, 2020, to January 4, 2022., Exposure: The AD genetic risk score (GRS), which is a weighted sum of 23 single-nucleotide variations., Main Outcomes and Measures: Seven cognitive tests were administered via touchscreen at in-person visits or online. Cognitive domains assessed included fluid intelligence, episodic memory, processing speed, executive functioning, and prospective memory. Multiple cognitive measures were derived from some tests, yielding 32 separate measures. Interactions between age and AD-GRS for each of the 32 cognitive measures were tested with linear regression using a Bonferroni-corrected P value threshold. For cognitive measures with significant evidence of age by AD-GRS interaction, the youngest age of interaction was assessed with new regression models, with nonlinear specification of age terms. Models with youngest age of interaction from 40 to 70 years, in 1-year increments, were compared, and the best-fitting model for each cognitive measure was chosen. Results across cognitive measures were compared to determine which cognitive indicators showed earliest AD-related change., Results: A total of 405 050 participants (mean [SD] age, 57.1 [7.9] years; 54.1% female) were included. Sample sizes differed across cognitive tests (from 12 455 to 404 682 participants). The AD-GRS significantly modified the association with age on 13 measures derived from the pairs matching (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.5%-11.5%), symbol digit substitution (range in difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 2.0%-5.8%), and numeric memory tests (difference in mean cognition per decade increase in age for 1-SD higher AD-GRS, 8.8%) (P = 1.56 × 10-3). Best-fitting models suggested that cognitive scores of individuals with a high vs low AD-GRS began to diverge by 56 years of age for all 13 measures and by 47 years of age for 9 measures., Conclusions and Relevance: In this cross-sectional study, by early midlife, subtle differences in memory and attention were detectable among individuals with higher genetic risk of AD.

Social epidemiology is concerned with how social forces influence population health. Rather than focusing on a single disease (as in cancer or cardiovascular epidemiology) or a single type of exposure (e.g., nutritional epidemiology), social epidemiology encompasses all the social and economic determinants of health, both historical and contemporary. These include features of social and physical environments, the network of relationships in a society, and the institutions, politics, policies, norms and cultures that shape all of these forces. This commentary presents the perspective of several editors at the Journal with expertise in social epidemiology. We articulate our thinking to encourage submissions to the Journal that: 1) expand knowledge of emerging and underresearched social determinants of population health; 2) advance new empirical evidence on the determinants of health inequities and solutions to advance health equity; 3) generate evidence to inform the translation of research on social determinants of health into public health impact; 4) contribute to innovation in methods to improve the rigor and relevance of social epidemiology; and 5) encourage critical self-reflection on the direction, challenges, successes, and failures of the field., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Racial residential segregation is associated with multiple adverse health outcomes in Black individuals. Yet, the influence of structural racism and racial residential segregation on brain aging is less understood. In this study, we investigated the association between cumulative exposure to racial residential segregation over 25 years (1985-2010) in young adulthood, as measured by the Getis-Ord Gi* statistic, and year 25 measures of brain volume (cerebral, gray matter, white matter, and hippocampal volumes) in midlife. We studied 290 Black participants with available brain imaging data who were enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) Study, a prospective cohort study. CARDIA investigators originally recruited 2,637 Black participants aged 18-30 years from 4 field centers across the United States. We conducted analyses using marginal structural models, incorporating inverse probability of treatment weighting and inverse probability of censoring weighting. We found that compared with low/medium segregation, greater cumulative exposure to a high level of racial residential segregation throughout young adulthood was associated with smaller brain volumes in general (e.g., for cerebral volume, β = -0.08, 95% confidence interval: -0.15, -0.02) and with a more pronounced reduction in hippocampal volume, though results were not statistically significant. Our findings suggest that exposure to segregated neighborhoods may be associated with worse brain aging., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In the absence of effective pharmacological treatment to halt or reverse the course of Alzheimer's disease and related dementias (ADRDs), population-level research on the modifiable determinants of dementia risk and outcomes for those living with ADRD is critical. The Harmonized Cognitive Assessment Protocol (HCAP), fielded in 2016 as part of the U.S. Health and Retirement Study (HRS) and multiple international counterparts, has the potential to play an important role in such efforts. The stated goals of the HCAP are to improve our ability to understand the determinants, prevalence, costs, and consequences of cognitive impairment and dementia in the U.S. and to support cross-national comparisons. The first wave of the HCAP demonstrated the feasibility and value of the more detailed cognitive assessments in the HCAP compared to the brief cognitive assessments in the core HRS interviews. To achieve its full potential, we provide eight recommendations for improving future iterations of the HCAP. Our highest priority recommendation is to increase the representation of historically marginalized racial/ethnic groups disproportionately affected by ADRDs. Additional recommendations relate to the timing of the HCAP assessments; clinical and biomarker validation data, including to improve cross-national comparisons; dropping lower performing items; enhanced documentation; and the addition of measures related to caregiver impact. We believe that the capacity of the HCAP to achieve its stated goals will be greatly enhanced by considering these changes and additions., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

COVID-19 mortality has disproportionately affected specific occupations and industries. The Occupational Safety and Health Administration (OSHA) protects the health and safety of workers by setting and enforcing standards for working conditions. Workers may file OSHA complaints about unsafe conditions. Complaints may indicate poor workplace safety during the pandemic. We evaluated COVID-19-related complaints filed with California (Cal)/OSHA between January 1, 2020 and December 14, 2020 across seven industries. To assess whether workers in occupations with high COVID-19-related mortality were also most likely to file Cal/OSHA complaints, we compared industry-specific per-capita COVID-19 confirmed deaths from the California Department of Public Health with COVID-19-related complaints. Although 7820 COVID-19-related complaints were deemed valid by Cal/OSHA, only 627 onsite inspections occurred, and 32 citations were issued. Agricultural workers had the highest per-capita COVID-19 death rates (402 per 100,000 workers) but were least represented among workplace complaints (44 per 100,000 workers). Health Care workers had the highest complaint rates (81 per 100,000 workers) but the second lowest COVID-19 death rate (81 per 100,000 workers). Industries with the highest inspection rates also had high COVID-19 mortality. Our findings suggest complaints are not proportional to COVID-19 risk. Instead, higher complaint rates may reflect worker groups with greater empowerment, resources, or capacity to advocate for better protections. This capacity to advocate for safe workplaces may account for relatively low mortality rates in potentially high-risk occupations. Future research should examine factors determining worker complaints and complaint systems to promote participation of those with the greatest need of protection., Competing Interests: None., (© 2021 Published by Elsevier Ltd.)

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference., Competing Interests: Competing interest The authors declare no competing interests.

Background: Higher educational attainment predicts lower hypertension. Yet, associations between nontraditional educational trajectories (eg, interrupted degree programs) and hypertension are less well understood, particularly among structurally marginalized groups who are more likely to experience these non-traditional trajectories., Methods: In National Longitudinal Survey of Youth 1979 cohort data (N = 6 317), we used sequence and cluster analyses to identify groups of similar educational sequences-characterized by timing and type of terminal credential-that participants followed from age 14-48 years. Using logistic regression, we estimated associations between the resulting 10 educational sequences and hypertension at age 50. We evaluated effect modification by individual-level indicators of structural marginalization (race, gender, race and gender, and childhood socioeconomic status [cSES])., Results: Compared to terminal high school (HS) diploma completed at traditional age, terminal GED (OR: 1.32; 95%CI: 1.04, 1.66) or Associate degree after

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Directed acyclic graphs (DAGs) are an intuitive yet rigorous tool to communicate about causal questions in clinical and epidemiologic research and inform study design and statistical analysis. DAGs are constructed to depict prior knowledge about biological and behavioral systems related to specific causal research questions. DAG components portray who receives treatment or experiences exposures; mechanisms by which treatments and exposures operate; and other factors that influence the outcome of interest or which persons are included in an analysis. Once assembled, DAGs - via a few simple rules - guide the researcher in identifying whether the causal effect of interest can be identified without bias and, if so, what must be done either in study design or data analysis to achieve this. Specifically, DAGs can identify variables that, if controlled for in the design or analysis phase, are sufficient to eliminate confounding and some forms of selection bias. DAGs also help recognize variables that, if controlled for, bias the analysis (e.g., mediators or factors influenced by both exposure and outcome). Finally, DAGs help researchers recognize insidious sources of bias introduced by selection of individuals into studies or failure to completely observe all individuals until study outcomes are reached. DAGs, however, are not infallible, largely owing to limitations in prior knowledge about the system in question. In such instances, several alternative DAGs are plausible, and researchers should assess whether results differ meaningfully across analyses guided by different DAGs and be forthright about uncertainty. DAGs are powerful tools to guide the conduct of clinical research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Social epidemiology aims to identify social structural risk factors, thus informing targets and timing of interventions. Ascertaining which interventions will be most effective and when they should be implemented is challenging because social conditions vary across the life course and are subject to time-varying confounding. Marginal structural models (MSMs) may be useful but can present unique challenges when studying social epidemiologic exposures over the life course. We describe selected MSMs corresponding to common theoretical life-course models and identify key issues for consideration related to time-varying confounding and late study enrollment. Using simulated data mimicking a cohort study evaluating the effects of depression in early, mid-, and late life on late-life stroke risk, we examined whether and when specific study characteristics and analytical strategies may induce bias. In the context of time-varying confounding, inverse-probability-weighted estimation of correctly specified MSMs accurately estimated the target causal effects, while conventional regression models showed significant bias. When no measure of early-life depression was available, neither MSMs nor conventional models were unbiased, due to confounding by early-life depression. To inform interventions, researchers need to identify timing of effects and consider whether missing data regarding exposures earlier in life may lead to biased estimates., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Social policies have great potential to improve population health and reduce health disparities. Increasingly, those doing empirical research have sought to quantify the health effects of social policies by exploiting variation in the timing of policy changes across places. Multiple social policies are often adopted simultaneously or in close succession in the same locations, creating co-occurrence that must be handled analytically for valid inferences. Although this is a substantial methodological challenge for researchers aiming to isolate social policy effects, only in a limited number of studies have researchers systematically considered analytic solutions within a causal framework or assessed whether these solutions are being adopted. We designated 7 analytic solutions to policy co-occurrence, including efforts to disentangle individual policy effects and efforts to estimate the combined effects of co-occurring policies. We used an existing systematic review of social policies and health to evaluate how often policy co-occurrence is identified as a threat to validity and how often each analytic solution is applied in practice. Of the 55 studies, only in 17 (31%) did authors report checking for any co-occurring policies, although in 36 studies (67%), at least 1 approach was used that helps address policy co-occurrence. The most common approaches were adjusting for measures of co-occurring policies; defining the outcome on subpopulations likely to be affected by the policy of interest (but not other co-occurring policies); and selecting a less-correlated measure of policy exposure. As health research increasingly focuses on policy changes, we must systematically assess policy co-occurrence and apply analytic solutions to strengthen studies on the health effects of social policies., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence that must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco use. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by covariation with other policies. We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent. Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority-rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57-fold. Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: US preterm-birth rates are 1.6 times higher for Black mothers than for White mothers. Although traffic-related air pollution (TRAP) may increase the risk of preterm birth, evaluating its effect on preterm birth and disparities has been challenging because TRAP is often measured inaccurately. This study sought to estimate the effect of TRAP exposure, measured at the street level, on the prevalence of preterm birth by race/ethnicity., Methods: We linked birth-registry data with TRAP measured at the street level for singleton births in sampled communities during 2013-2015 in Oakland and San Jose, California. Using logistic regression and marginal standardization, we estimated the effects of exposure to black carbon, nitrogen dioxide and ultrafine particles on preterm birth after confounder adjustment and stratification by race/ethnicity., Results: There were 8823 singleton births, of which 760 (8.6%) were preterm. Shifting black-carbon exposure from the 10th to the 90th percentile was associated with: 6.8%age point higher risk of preterm birth (95% confidence interval = 0.1 to 13.5) among Black women; 2.1%age point higher risk (95% confidence interval = -1.1 to 5.2) among Latinas; and inconclusive null findings among Asian and White women. For Latinas, there was evidence of a positive association between the other pollutants and risk of preterm birth, although effect sizes were attenuated in models that co-adjusted for other TRAP., Conclusions: Exposure to TRAP, especially black carbon, may increase the risk of preterm birth for Latina and Black women but not for Asian and White women., (© The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Long-lasting insecticidal nets (LLINs) are the main vector control tool for pregnant women, but their efficacy may be compromised, in part, due to pyrethroid resistance. In 2017, the Ugandan Ministry of Health embedded a cluster randomized controlled trial into the national LLIN campaign, where a random subset of health subdistricts (HSDs) received LLINs treated with piperonyl butoxide (PBO), a chemical synergist known to partially restore pyrethroid sensitivity. Using data from a small, non-randomly selected subset of HSDs, this secondary analysis used quasi-experimental methods to quantify the overall impact of the LLIN campaign on pregnancy outcomes. In an exploratory analysis, differences between PBO and conventional (non-PBO) LLINs on pregnancy outcomes were assessed., Methods: Birth registry data (n = 39,085) were retrospectively collected from 21 health facilities across 12 HSDs, 29 months before and 9 months after the LLIN campaign (from 2015 to 2018). Of the 12 HSDs, six received conventional LLINs, five received PBO LLINs, and one received a mix of conventional and PBO LLINs. Interrupted time-series analyses (ITSAs) were used to estimate changes in monthly incidence of stillbirth and low birthweight (LBW; <2500 g) before-and-after the campaign. Poisson regression with robust standard errors modeled campaign effects, adjusting for health facility-level differences, seasonal variation, and time-varying maternal characteristics. Comparisons between PBO and conventional LLINs were estimated using difference-in-differences estimators., Results: ITSAs estimated the campaign was associated with a 26% [95% CI: 7-41] reduction in stillbirth incidence (incidence rate ratio (IRR) = 0.74 [0.59-0.93]) and a 15% [-7, 33] reduction in LBW incidence (IRR=0.85 [0.67-1.07]) over a 9-month period. The effect on stillbirth incidence was greatest for women delivering 7-9 months after the campaign (IRR=0.60 [0.41-0.87]) for whom the LLINs would have covered most of their pregnancy. The IRRs estimated from difference-in-differences analyses comparing PBO to conventional LLINs was 0.78 [95% CI: 0.52, 1.16] for stillbirth incidence and 1.15 [95% CI: 0.87, 1.52] for LBW incidence., Conclusions: In this region of Uganda, where pyrethroid resistance is high, this study found that a mass LLIN campaign was associated with reduced stillbirth incidence. Effects of the campaign were greatest for women who would have received LLINs early in pregnancy, suggesting malaria protection early in pregnancy can have important benefits that are not necessarily realized through antenatal malaria services. Results from the exploratory analyses comparing PBO and conventional LLINs on pregnancy outcomes were inconclusive, largely due to the wide confidence intervals that crossed the null. Thus, future studies with larger sample sizes are needed., (© 2022. The Author(s).)

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas., Competing Interests: Conflict of interest Author RT is a consultant for Bayer, Astellas, and Happify. Author HS is a full-time employee of the Alzheimer’s Association. Author TH is a member of the Scientific Advisory Board for Vivid Genomics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Dynamical models, commonly used in infectious disease epidemiology, are formal mathematical representations of time-changing systems or processes. For many chronic disease epidemiologists, the link between dynamical models and predominant causal inference paradigms is unclear. In this commentary, we explain the use of dynamical models for representing causal systems and the relevance of dynamical models for causal inference. In certain simple settings, dynamical modeling and conventional statistical methods (e.g., regression-based methods) are equivalent, but dynamical modeling has advantages over conventional statistical methods for many causal inference problems. Dynamical models can be used to transparently encode complex biological knowledge, interference and spillover, effect modification, and variables that influence each other in continuous time. As our knowledge of biological and social systems and access to computational resources increases, there will be growing utility for a variety of mathematical modeling tools in epidemiology., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVD-related outcomes explained by genetic versus socioeconomic predictors., Methods: Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained., Results: The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes. Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF., Conclusions: Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings. See video abstract at, http://links.lww.com/EDE/B879., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Housing mobility impacts adolescent alcohol use, and the neighborhood built environment may impact this relationship. Methods: Moving to Opportunity (MTO) was a multi-site, three-arm, household-level experiment. MTO randomly assigned one of three treatment arms (1994-1997) allowing families living in public housing to (1) receive a voucher to be redeemed any neighborhood (2) receive a voucher to be redeemed in a neighborhood with less than 10% poverty (3) remain in public housing (control). MTO decreased girls' alcohol use, but increased boys' alcohol use. Treatment groups were pooled because they are similar conceptually and statistically on our primary outcome. Among youth aged 12-19 in 2001-2002 (N = 2829), we estimated controlled direct effects mediation of MTO treatment effects on youth with housing vouchers (N = 1950) vs. controls (N = 879) on past 30-day number of drinks per day on days drank, using gender-stratified Poisson regression. Mediators were density of on- and off-premises alcohol outlets per square mile at the families' census tract of residence in 1997. Results: Treatment group youth were randomized to live in 1997 census tracts with lower off-premises, but higher on-premises, outlet density. MTO treatment (vs. controls) decreased drinking for girls via alcohol outlet density, but only at higher levels of outlet density. Treatment was 18% more beneficial when girls moved to high density neighborhoods, compared to controls who stayed living in public housing in high density neighborhoods. Conclusion: Additional social processes unmeasured in the current study may play an important role in the alcohol use and other health risks for girls.

Introduction: The Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study enrolled Asian, Black, Latino, and White adults ages 65+ without prior dementia diagnosis (N = 1709). We evaluated the prevalence of cognitive impairment (mild cognitive impairment or dementia) accounting for potential biases., Methods: A random subgroup (N = 541) received clinical evaluation and others were evaluated if they failed a cognitive screen. Diagnoses were made under two conditions: (1) demographics-blind, based on clinical exam and demographically adjusted neuropsychological test scores; and (2) all available information (clinical exam, demographics, and adjusted and unadjusted test scores)., Results: Cognitive impairment prevalence was 28% for blinded-adjusted diagnosis and 25% using all available information. Black participants had higher impairment rates than White (both conditions) and Latino (blinded-adjusted diagnosis) participants. Incomplete assessments negatively biased prevalence estimates for White participants., Discussion: Racial/ethnic disparities in cognitive impairment were amplified by attrition bias in White participants but were unaffected by type of test norms and diagnosticians' knowledge of demographics., Competing Interests: The authors have no conflicts of interest related to this work., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Purpose: Migrating from Mexico to the U.S. is a major, stressful life event with potentially profound influences on mental health. However, estimating the health effects of migration is challenging because of differential selection into migration and time-varying confounder mediators of migration effects on health., Methods: We pooled data from the Mexican Health and Aging Study (N = 17,771) and Mexican-born U.S. Health and Retirement Study (N = 898) participants to evaluate the effects of migration to the U.S. (at any age and in models for migration in childhood or adulthood) on depressive symptom-count, measured with a modified Centers for Epidemiologic Studies-Depression scale. We modeled probability of migrating in each year of life from birth to either age at initial migration to the U.S. or enrollment and used these models to calculate inverse probability of migration weights. We applied the weights to covariate-adjusted negative binomial GEE models, estimating the ratio of average symptom-count associated with migration., Results: Mexico to U.S. migration was unrelated to depressive symptoms among men (ratio of average symptom-count= 0.98 [95% CI: 0.89, 1.08]) and women (ratio of average symptom-count = 1.00 [95% CI: 0.92, 1.09]). Results were similar for migration in childhood, early adulthood, or later adulthood., Conclusions: In this sample of older Mexican-born adults, migration to the U.S. was unrelated to depressive symptoms., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: We examined the association of Stroke Belt birth state with late-life cognition in The Study of Healthy Aging in African Americans (STAR)., Methods: STAR enrolled 764 Black Americans ages 50+ who were long-term Kaiser Permanente Northern California members. Participants completed Multiphasic Health Check-ups (MHC; 1964-1985) where early-life overweight/obesity, hypertension, diabetes, and hyperlipidemia were measured. At STAR (2018), birth state, self-reported early-life socioeconomic status (SES), and executive function, verbal episodic memory, and semantic memory scores were collected. We used linear regression to examine the association between Stroke Belt birth and late-life cognition adjusting for birth year, gender, and parental education. We evaluated early-life SES and cardiovascular risk factors (CVRF) as potential mechanisms., Results: Twenty-seven percent of participants were born in the Stroke Belt with a mean age of 69 (standard deviation = 9) at STAR. Stroke Belt birth was associated with worse late-life executive function (β [95% confidence interval]: -0.18 [-0.33, -0.02]) and semantic memory (-0.37 [-0.53, -0.21]), but not verbal episodic memory (-0.04 [-0.20, 0.12]). Adjustment for SES and CVRF attenuated associations of Stroke Belt birth with cognition (executive function [-0.05 {-0.25, 0.14}]; semantic memory [-0.28 {-0.49, -0.07}])., Conclusions: Black Americans born in the Stroke Belt had worse late-life cognition than those born elsewhere, underscoring the importance of early-life exposures on brain health., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Low socioeconomic position (SEP) across the lifecourse is associated with Type 2 diabetes (T2DM). We examined whether these economic disparities differ by race and sex. We included 5448 African American (AA) and white participants aged ≥45 years from the national (United States) REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort without T2DM at baseline (2003-07). Incident T2DM was defined by fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or using T2DM medications at follow-up (2013-16). Derived SEP scores in childhood (CSEP) and adulthood (ASEP) were used to calculate a cumulative (CumSEP) score. Social mobility was defined as change in SEP. We fitted race-stratified logistic regression models to estimate the association between each lifecourse SEP indicator and T2DM, adjusting for covariates; additionally, we tested SEP-sex interactions. Over a median of 9.0 (range 7-14) years of follow-up, T2DM incidence was 167.1 per 1000 persons among AA and 89.9 per 1000 persons among white participants. Low CSEP was associated with T2DM incidence among AA (OR = 1.61; 95%CI 1.05-2.46) but not white (1.06; 0.74-2.33) participants; this was attenuated after adjustment for ASEP. In contrast, low CumSEP was associated with T2DM incidence for both racial groups. T2DM risk was similar for stable low SEP and increased for downward mobility when compared with stable high SEP in both groups, whereas upward mobility increased T2DM risk among AAs only. No differences by sex were observed. Among AAs, low CSEP was not independently associated with T2DM, but CSEP may shape later-life experiences and health risks., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Exposure to nitrogen dioxide (NO 2 ), black carbon (BC), and ultrafine particles (UFPs) during pregnancy may increase the risk of preeclampsia, but previous studies have not assessed hyperlocalized differences in pollutant levels, which may cause exposure misclassification. We used data from Google Street View cars with mobile air monitors that repeatedly sampled NO 2 , BC, and UFPs every 30 m in Downtown and West Oakland neighborhoods during 2015-2017. Data were linked to electronic health records of pregnant women in the 2014-2016 Sutter Health population, who resided within 120 m of monitoring data ( N = 1095), to identify preeclampsia cases. We used G-computation with log-binomial regression to estimate risk differences (RDs) associated with a hypothetical intervention reducing pollutant levels to the 25th percentile observed in our sample on preeclampsia risk, overall and stratified by race/ethnicity. Prevalence of preeclampsia was 6.8%. Median (interquartile range) levels of NO 2 , BC, and UFPs were 10.8 ppb (9.0, 13.0), 0.34 μg/m 3 (0.27, 0.42), and 29.2 # × 10 3 /cm 3 (26.6, 32.6), respectively. Changes in the risk of preeclampsia achievable by limiting each pollutant to the 25th percentile were NO 2 RD = -1.5 per 100 women (95% confidence interval (CI): -2.5, -0.5); BC RD = -1.0 (95% CI: -2.2, 0.02); and UFP RD = -0.5 (95% CI: -1.8, 0.7). Estimated effects were the largest for non-Latina Black mothers: NO 2 RD = -2.8 (95% CI: -5.2, -0.3) and BC RD = -3.0 (95% CI: -6.4, 0.4).

Importance: Prior research suggests schooling differences for Black individuals in the US are associated with worse cognitive aging. It is unknown whether age when experiencing school desegregation is associated with differences in late-life cognition in this population., Objective: To examine patterns of association between age of school desegregation in grades 1 to 12 and late-life cognition., Design, Setting, and Participants: This cohort study analyzed baseline data from the Study of Healthy Aging in African Americans (STAR) cohort collected from 2018 through 2019 in Northern California, primarily in the cities of Richmond and Oakland. Participants were 699 self-identified Black individuals aged 50 years or older who were community-dwelling, long-term members of Kaiser Permanente Northern California and dementia free at baseline., Exposures: Participants reported whether they attended a segregated school in grades 1, 6, 9, and 12 and were placed in 1 of 6 transition categories: (1) always attended integrated schools; (2) integrated between grades 1 through 5; (3) integrated between grades 6 through 8; (4) integrated between grades 9 through 12; (5) ever moved from integrated to segregated school; (6) never attended integrated schools., Main Outcomes and Measures: Executive function, semantic memory, and verbal episodic memory ascertained via the Spanish and English Neuropsychological Assessment Battery and z standardized for analyses., Results: The mean (SD) age of the 699 participants was 68.5 (8.7) years, and 484 (69.2%) were female. Most participants transitioned from segregated to integrated schools owing to historical timing and cohort geography. Compared with 111 participants who never attended integrated schools (reference group), executive function was better among 50 participants who transitioned to integrated schools between grades 1 and 5 (β = 0.35; 95% CI, 0.08-0.61; P = .01). Semantic memory was better among 435 participants who only attended integrated schools (β = 0.34; 95% CI, 0.14-0.54; P = .001) or among 50 participants who transitioned to integrated schools between grades 1 and 5 (β = 0.43; 95% CI, 0.15-0.72; P = .003). However, no significant differences were found by group for verbal episodic memory function (eg, for 50 participants who transitioned to integrated schools between grades 1 and 5: β = 0.07; 95% CI, -0.22 to 0.35; P = .66). No significant differences were found when testing for potential interactions by sex, Southern birth, or baseline age., Conclusions and Relevance: The STAR cohort data indicated that executive function and semantic memory were higher among Black individuals with some integrated school experience. These results suggest that racially segregated schooling experiences, including de facto segregation present today, may be associated with worse late-life cognition.

Programming for data wrangling and statistical analysis is an essential technical tool of modern epidemiology, yet many epidemiologists receive limited formal training in strategies to optimize the quality of our code. In complex projects, coding mistakes are easy to make, even for skilled practitioners. Such mistakes can lead to invalid research claims that reduce the credibility of the field. Code review is a straightforward technique used by the software industry to reduce the likelihood of coding bugs. The systematic implementation of code review in epidemiologic research projects could not only improve science but also decrease stress, accelerate learning, contribute to team building, and codify best practices. In the present article, we argue for the importance of code review and provide some recommendations for successful implementation for 1) the research laboratory, 2) the code author (the initial programmer), and 3) the code reviewer. We outline a feasible strategy for implementation of code review, though other successful implementation processes are possible to accommodate the resources and workflows of different research groups, including other practices to improve code quality. Code review isn't always glamorous, but it is critically important for science and reproducibility. Humans are fallible; that's why we need code review., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Weight loss or lower body mass index (BMI) could be an early symptom of Alzheimer disease (AD), but when this begins to emerge is difficult to estimate with traditional observational data. In an extension of Mendelian randomization, we leveraged variation in genetic risk for late-onset AD risk to estimate the causal effect of AD on BMI and the earliest ages at which AD-related weight loss (or lower BMI as a proxy) occurs. We studied UK Biobank participants enrolled in 2006-2010, who were without dementia, aged 39-73, with European genetic ancestry. BMI was calculated with measured height/weight (weight (kg)/height (m)2). An AD genetic risk score (AD-GRS) was calculated based on 23 genetic variants. Using linear regressions, we tested the association of AD-GRS with BMI, stratified by decade, and calculated the age of divergence in BMI trends between low and high AD-GRS. AD-GRS was not associated with BMI in 39- to 49-year-olds (β = 0.00, 95% confidence interval (CI): -0.03, 0.03). AD-GRS was associated with lower BMI in 50- to 59-year-olds (β = -0.03, 95% CI: -0.06, -0.01) and 60- to 73-year-olds (β = -0.09, 95% CI:-0.12, -0.07). Model-based BMI age curves for high versus low AD-GRS began to diverge after age 47 years. Sensitivity analyses found no evidence for pleiotropy or survival bias. Longitudinal replication is needed; however, our findings suggest that AD genes might begin to reduce BMI decades prior to dementia diagnosis., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Whether requiring Graduate Record Examinations (GRE) results for doctoral applicants affects the diversity of admitted cohorts remains uncertain. This study randomized applications to 2 population-health doctoral programs at the University of California San Francisco to assess whether masking reviewers to applicant GRE results differentially affects reviewers' scores for underrepresented minority (URM) applicants from 2018-2020. Applications with GRE results and those without were randomly assigned to reviewers to designate scores for each copy (1-10, 1 being best). URM was defined as self-identification as African American/Black, Filipino, Hmong, Vietnamese, Hispanic/Latinx, Native American/Alaska Native, or Native Hawaiian/Other Pacific Islander. We used linear mixed models with random effects for the applicant and fixed effects for each reviewer to evaluate the effect of masking the GRE results on the overall application score and whether this effect differed by URM status. Reviewer scores did not significantly differ for unmasked versus masked applications among non-URM applicants (β = 0.15; 95% CI: -0.03, 0.33) or URM applicants (β = 0.02, 95% CI: -0.49, 0.54). We did not find evidence that removing GREs differentially affected URM compared with non-URM students (β for interaction = -0.13, 95% CI: -0.55, 0.29). Within these doctoral programs, results indicate that GRE scores neither harm nor help URM applicants., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: Integrating results from multiple samples is often desirable, but privacy restrictions may preclude full data pooling, and most datasets do not include fully harmonized variable sets. We propose a simulation-based method leveraging partial information across datasets to guide creation of synthetic data based on explicit assumptions about the underlying causal structure that permits pooled analyses that adjust for all desired confounders in the context of privacy restrictions., Methods: This proof-of-concept project uses data from the Health and Retirement Study (HRS) and Atherosclerosis Risk in Communities (ARIC) study. We specified an estimand of interest and a directed acyclic graph (DAG) summarizing the presumed causal structure for the effect of glycated hemoglobin (HbA1c) on cognitive change. We derived publicly reportable statistics to describe the joint distribution of each variable in our DAG. These summary estimates were used as data-generating rules to create synthetic datasets. After pooling, we imputed missing covariates in the synthetic datasets and used the synthetic data to estimate the pooled effect of HbA1c on cognitive change, adjusting for all desired covariates., Results: Distributions of covariates and model coefficients and associated standard errors for our model estimating the effect of HbA1c on cognitive change were similar across cohort-specific original and preimputation synthetic data. The estimate from the pooled synthetic incorporates control for confounders measured in either original dataset., Discussion: Our approach has advantages over meta-analysis or individual-level pooling/data harmonization when privacy concerns preclude data sharing and key confounders are not uniformly measured across datasets., Competing Interests: The authors report no conflicts of interest. None of the funders had any role in design and conduct of the study; management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Individuals increasingly experience delays or interruptions in schooling; we evaluate the association between these non-traditional education trajectories and mental health., Methods: Using year-by-year education data for 7,501 National Longitudinal Survey of Youth 1979 participants, ages 14-48 (262,535 person-years of education data), we applied sequence analysis and a clustering algorithm to identify educational trajectory groups, incorporating both type and timing to credential. Linear regression models, adjusted for early-life confounders, evaluated relationships between educational trajectories and mental health component scores (MCS) from the 12-item short form instrument at age 50. We evaluated effect modification by race, gender, and race by gender., Results: We identified 24 distinct educational trajectories based on highest credential and educational timing. Compared to high school (HS) diplomas, < HS (beta=-3.41, 95%CI:-4.74,-2.07) and general educational development credentials (GEDs) predicted poorer MCS (beta=-2.07,95%CI:-3.16,-0.98). The following educational trajectories predicted better MCS: some college immediately after High School (beta=1.52, 95%CI:0.68,2.37), Associate degrees after long interruptions (beta=1.73, 95%CI:0.27,3.19), and graduate school soon after Bachelor's completion (beta=1.13, 95%CI:0.21,2.06). Compared to White men, Black women especially benefited from educational credentials higher than HS in predicting MCS., Conclusions: Both type and timing of educational credential predicted mental health. Black women's mental higher especially benefited from higher educational credentials., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The prevalence of infections caused by extended-spectrum beta-lactamase producing Escherichia coli (ESBL-E. coli) is increasing worldwide, but the setting in which this increase is occurring is not well defined. We compared trends and risk factors for ESBL-E. coli bacteriuria in community vs healthcare settings., Methods: We collected electronic health record data on all patients with E. coli isolated from urine cultures in a safety-net public healthcare system from January 2014 to March 2020. All analyses were stratified by healthcare-onset/associated (bacteriuria diagnosed > 48 h after hospital admission or in an individual hospitalized in the past 90 days or in a skilled nursing facility resident, N = 1277) or community-onset bacteriuria (bacteriuria diagnosed < 48 h after hospital admission or in an individual seen in outpatient clinical settings without a hospitalization in the past 90 days, N = 7751). We estimated marginal trends from logistic regressions to evaluate annual change in prevalence of ESBL-E. coli bacteriuria among all bacteriuria. We evaluated risk factors using logistic regression models., Results: ESBL-E. coli prevalence increased in both community-onset (0.91% per year, 95% CI 0.56%, 1.26%) and healthcare-onset/associated (2.31% per year, CI 1.01%, 3.62%) bacteriuria. In multivariate analyses, age > 65 (RR 1.88, CI 1.17, 3.05), male gender (RR 2.12, CI 1.65, 2.73), and Latinx race/ethnicity (RR 1.52, CI 0.99, 2.33) were associated with community-onset ESBL-E. coli. Only male gender (RR 1.53, CI 1.03, 2.26) was associated with healthcare-onset/associated ESBL-E. coli., Conclusions: ESBL-E. coli bacteriuria frequency increased at a faster rate in healthcare-associated settings than in the community between 2014 and 2020. Male gender was associated with ESBL-E. coli bacteriuria in both settings, but additional risks-age > 65 and Latinx race/ethnicity-were observed only in the community., (© 2021. The Author(s).)

Subgroup analyses of randomized controlled trials guide resource allocation and implementation of new interventions by identifying groups of individuals who are likely to benefit most from the intervention. Unfortunately, trial populations are rarely representative of the target populations of public health or clinical interest. Unless the relevant differences between trial and target populations are accounted for, subgroup results from trials might not reflect which groups in the target population will benefit most from the intervention. Transportability provides a rigorous framework for applying results derived in potentially highly selected study populations to external target populations. The method requires that researchers measure and adjust for all variables that 1) modify the effect of interest and 2) differ between the target and trial populations. To date, applications of transportability have focused on the external validity of overall study results and understanding within-trial heterogeneity; however, this approach has not yet been used for subgroup analyses of trials. Through an example from the Iniciativa Profilaxis Pre-Exposición (iPrEx) study (multiple countries, 2007-2010) of preexposure prophylaxis for human immunodeficiency virus, we illustrate how transporting subgroup analyses can produce target-specific subgroup effect estimates and numbers needed to treat. This approach could lead to more tailored and accurate guidance for resource allocation and cost-effectiveness analyses., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Clinic-based study samples, including the Alzheimer's Disease Neuroimaging Initiative (ADNI), offer rich data, but findings may not generalize to community-based settings. We compared associations in ADNI to those in the Atherosclerosis Risk in Communities (ARIC) study to assess generalizability across the two settings., Methods: We estimated cohort-specific associations among risk factors, cognitive test scores, and neuroimaging outcomes to identify and quantify the extent of significant and substantively meaningful differences in associations between cohorts. We explored whether using more homogenous samples improved comparability in effect estimates., Results: The proportion of associations that differed significantly between cohorts ranged from 27% to 34% across sample subsets. Many differences were substantively meaningful (e.g., odds ratios [OR] for apolipoprotein E ε4 on amyloid positivity in ARIC: OR = 2.8, in ADNI: OR = 8.6)., Discussion: A higher proportion of associations differed significantly and substantively than would be expected by chance. Findings in clinical samples should be confirmed in more representative samples., (© 2021 the Alzheimer's Association.)

Latino people in the US are experiencing higher excess deaths during the COVID-19 pandemic than any other racial/ethnic group, but it is unclear which sociodemographic subgroups within this diverse population are most affected. Such information is necessary to target policies that prevent further excess mortality and reduce inequities. Using death certificate data for January 1, 2016 through February 29, 2020 and time-series models, we estimated the expected weekly deaths among Latino people in California from March 1 through October 3, 2020. We quantified excess mortality as observed minus expected deaths and risk ratios (RR) as the ratio of observed to expected deaths. We considered subgroups categorized by age, sex, nativity, country of birth, educational attainment, occupation, and combinations of these factors. Our results indicate that during the first seven months of the pandemic, Latino deaths in California exceeded expected deaths by 10,316, a 31% increase. Excess death rates were greatest for individuals born in Mexico (RR 1.44; 95% PI, 1.41, 1.48) or a Central American country (RR 1.49; 95% PI, 1.37, 1.64), with less than a high school degree (RR 1.41; 95% PI, 1.35, 1.46), or in food-and-agriculture (RR 1.60; 95% PI, 1.48, 1.74) or manufacturing occupations (RR 1.59; 95% PI, 1.50, 1.69). Immigrant disadvantages in excess death were magnified among working-age Latinos in essential occupations. In sum, the COVID-19 pandemic has disproportionately impacted mortality among Latino immigrants, especially those in unprotected essential jobs. Interventions to reduce these inequities should include targeted vaccination, workplace safety enforcement, and expanded access to medical care and economic support., Competing Interests: Authors receive grant funding from the 10.13039/100000002National Institutes of Health, but no competing interests are declared., (© 2021 The Authors. Published by Elsevier Ltd.)

Cancer diagnoses are associated with better long-term memory in older adults, possibly reflecting a range of social confounders that increase cancer risk but improve memory. We used spouse's memory as a negative control outcome to evaluate this possible confounding, since spouses share social characteristics and environments, and individuals' cancers are unlikely to cause better memory among their spouses. We estimated the association of an individual's incident cancer diagnosis (exposure) with their own (primary outcome) and their spouse's (negative control outcome) memory decline in 3601 couples from 1998 to 2014 in the Health and Retirement Study, using linear mixed-effects models. Incident cancer predicted better long-term memory for the diagnosed individual. We observed no association between an individual's cancer diagnosis and rate of spousal memory decline. This negative control study suggests that the inverse association between incident cancer and rate of memory decline is unlikely to be attributable to social/behavioral factors shared between spouses., Competing Interests: K.P.L. and his family member Xiao Zhen Zhou are inventors of cis P-tau antibody technology, which was licensed by BIDMC to the startup Pinteon Therapeutics. K.P.L. and Xiao Zhen Zhou are founders of and own equity in Pinteon. Their interests were reviewed and are managed by BIDMC in accordance with its conflict of interest policy. The remaining authors declare no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: The role of spousal education on dementia risk and how it may differ by gender or race/ethnicity is unknown. This study examines the association between one's own education separate from and in conjunction with spousal education and risk of dementia., Design: Cohort., Setting: Kaiser Permanente Northern California (KPNC), an integrated health care delivery system., Participants: 8835 members of KPNC who were aged 40-55, married and reported own and spousal education in 1964-1973., Primary Outcome Measure: Dementia cases were identified through medical records from 1 January 1996 to 30 September 2017., Methods: Own and spousal education was self-reported in 1964-1973 and each was classified as four indicator variables (≤high school, trade school/some college, college degree and postgraduate) and as ≥college degree versus spousal education≥college degree =0.83 (95% CI: 0.76 to 0.90); adjusting for participant education HR spousal education≥college degree =0.92 (95% CI: 0.83 to 1.01)). These associations did not vary by gender or race/ethnicity., Conclusion: In a large diverse cohort, we found that higher levels of participant's own education were associated with lower dementia risk regardless of spousal education. An inverse association between spousal education and dementia risk was also present, however, the effects became non-significant after adjusting for participant education., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Increasing hospitalizations for COVID-19 in the United States and elsewhere have ignited debate over whether to reinstate shelter-in-place policies adopted early in the pandemic to slow the spread of infection. The debate includes claims that sheltering in place influences deaths unrelated to infection or other natural causes. Testing this claim should improve the benefit/cost accounting that informs choice on reimposing sheltering in place. We used time-series methods to compare weekly nonnatural deaths in California with those in Florida. California was the first state to begin, and among the last to end, sheltering in place, while sheltering began later and ended earlier in Florida. During weeks when California had shelter-in-place orders in effect, but Florida did not, the odds that a nonnatural death occurred in California rather than Florida were 14.4% below expected levels. Sheltering-in-place policies likely reduce mortality from mechanisms unrelated to infection or other natural causes of death., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Using data from the Moving to Opportunity (MTO) experiment (1994-2002), this study examined how a multidimensional measure of neighborhood quality over time influenced adolescent psychological distress, using instrumental variable (IV) analysis. Neighborhood quality was operationalized with the independently validated 19-indicator Child Opportunity Index (COI), linked to MTO family addresses over 4-7 years. We examined whether being randomized to receive a housing subsidy (versus remaining in public housing) predicted neighborhood quality across time. Using IV analysis, we tested whether experimentally induced differences in COI across time predicted psychological distress on the Kessler Screening Scale for Psychological Distress (n = 2,829; mean β = -0.04 points (standard deviation, 1.12)). The MTO voucher treatment improved neighborhood quality for children as compared with in-place controls. A 1-standard-deviation change in COI since baseline predicted a 0.32-point lower psychological distress score for girls (β = -0.32, 95% confidence interval: -0.61, -0.03). Results were comparable but less precisely estimated when neighborhood quality was operationalized as simply average post-random-assignment COI (β = -0.36, 95% confidence interval: -0.74, 0.02). Effect estimates based on a COI excluding poverty and on the most recent COI measure were slightly larger than other operationalizations of neighborhood quality. Improving a multidimensional measure of neighborhood quality led to reductions in low-income girls' psychological distress, and this was estimated with high internal validity using IV methods., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: It is uncertain whether coronary artery bypass grafting (CABG) is associated with cognitive decline in older adults compared with a nonsurgical method of coronary revascularization (percutaneous coronary intervention [PCI])., Objective: To compare the change in the rate of memory decline after CABG vs PCI., Design, Setting, and Participants: Retrospective cohort study of community-dwelling participants in the Health and Retirement Study, who underwent CABG or PCI between 1998 and 2015 at age 65 years or older. Data were modeled for up to 5 years preceding and 10 years following revascularization or until death, drop out, or the 2016-2017 interview wave. The date of final follow-up was November 2017., Exposures: CABG (including on and off pump) or PCI, ascertained from Medicare fee-for-service billing records., Main Outcomes and Measures: The primary outcome was a summary measure of cognitive test scores and proxy cognition reports that were performed biennially in the Health and Retirement Study, referred to as memory score, normalized as a z score (ie, mean of 0, SD of 1 in a reference population of adults aged ≥72 years). Memory score was analyzed using multivariable linear mixed-effects models, with a prespecified subgroup analysis of on-pump and off-pump CABG. The minimum clinically important difference was a change of 1 SD of the population-level rate of memory decline (0.048 memory units/y)., Results: Of 1680 participants (mean age at procedure, 75 years; 41% female), 665 underwent CABG (168 off pump) and 1015 underwent PCI. In the PCI group, the mean rate of memory decline was 0.064 memory units/y (95% CI, 0.052 to 0.078) before the procedure and 0.060 memory units/y (95% CI, 0.048 to 0.071) after the procedure (within-group change, 0.004 memory units/y [95% CI, -0.010 to 0.018]). In the CABG group, the mean rate of memory decline was 0.049 memory units/y (95% CI, 0.033 to 0.065) before the procedure and 0.059 memory units/y (95% CI, 0.047 to 0.072) after the procedure (within-group change, -0.011 memory units/y [95% CI, -0.029 to 0.008]). The between-group difference-in-differences estimate for memory decline for PCI vs CABG was 0.015 memory units/y (95% CI, -0.008 to 0.038; P = .21). There was statistically significant increase in the rate of memory decline after off-pump CABG compared with after PCI (difference-in-differences: mean increase in the rate of decline of 0.046 memory units/y [95% CI, 0.008 to 0.084] after off-pump CABG), but not after on-pump CABG compared with PCI (difference-in-differences: mean slowing of decline of 0.003 memory units/y [95% CI, -0.024 to 0.031] after on-pump CABG)., Conclusions and Relevance: Among older adults undergoing coronary revascularization with CABG or PCI, the type of revascularization procedure was not significantly associated with differences in the change of rate of memory decline.

Background: Low socioeconomic status (SES) in early and late life has been associated with lower late-life cognition. Less is known about how changes in SES from childhood to late life are associated with late-life cognition, especially among diverse populations of older adults., Methods: In a multi-ethnic sample (n = 1353) of older adults, we used linear regression to test associations of change in comprehensive measures of SES (financial, cultural, and social domains) from childhood to late life with semantic memory, episodic memory, and executive function. We tested whether the association between SES trajectory and late-life cognition differed by populations who resided in the U.S. during childhood or immigrated to the U.S. as adults., Results: Participants with low childhood/high late-life financial capital had better semantic memory (β = 0.18; 95% CI: 0.04, 0.32) versus those with low financial capital in both childhood and late life, regardless of childhood residence. We observed a significant interaction in the association of verbal episodic memory and cultural capital by childhood residence (p = 0.08). Participants with a foreign childhood residence had higher verbal episodic memory if they had low childhood/high late-life cultural capital (β = 0.32; 95% CI: 0.01, 0.63), but lower verbal episodic memory if they had high childhood/low late-life cultural capital (β = - 0.40; 95% CI: - 0.94, 0.13). Having high lifecourse social capital was associated with better verbal episodic memory scores among those with a U.S. childhood (β = 0.34; 95% CI: 0.14, 0.55), but lower verbal episodic memory among those with a foreign childhood (β = - 0.10; 95% CI: - 0.51, 0.31)., Conclusions: High financial and cultural capital in late life is associated with better cognition, regardless of early childhood SES or childhood residence.