Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

Polycystic ovary syndrome (PCOS) is the most common female endocrine disorder and has important evolutionary implications for female reproduction and health. PCOS presents an interesting paradox, as it results in significant anovulation and potential sub-fecundity in industrialized populations, yet it has a surprisingly high prevalence and has a high heritability. In this review, we discuss an overview of PCOS, current diagnostic criteria, associated hormonal pathways and a review of proposed evolutionary hypotheses for the disorder. With a multifactorial etiology that includes ovarian function, metabolism, insulin signaling and multiple genetic risk alleles, PCOS is a complex disorder. We propose that PCOS is a mismatch between previously neutral genetic variants that evolved in physically active subsistence settings that have the potential to become harmful in sedentary industrialized environments. Sedentary obesogenic environments did not exist in ancestral times and exacerbate many of these pathways, resulting in the high prevalence and severity of PCOS today. Overall, the negative impacts of PCOS on reproductive success would likely have been minimal during most of human evolution and unlikely to generate strong selection. Future research and preventative measures should focus on these gene-environment interactions as a form of evolutionary mismatch, particularly in populations that are disproportionately affected by obesity and metabolic disorders. Lay Summary The most severe form of polycystic ovary syndrome (PCOS) is likely a result of interactions between genetic predispositions for PCOS and modern obesogenic environments. PCOS would likely have been less severe ancestrally and the fitness reducing effects of PCOS seen today are likely a novel product of sedentary, urban environments.

In post-industrial settings, apolipoprotein E4 (APOE4) is associated with increased cardiovascular and neurological disease risk. However, the majority of human evolutionary history occurred in environments with higher pathogenic diversity and low cardiovascular risk. We hypothesize that in high-pathogen and energy-limited contexts, the APOE4 allele confers benefits by reducing innate inflammation when uninfected, while maintaining higher lipid levels that buffer costs of immune activation during infection. Among Tsimane forager-farmers of Bolivia (N = 1266, 50% female), APOE4 is associated with 30% lower C-reactive protein, and higher total cholesterol and oxidized LDL. Blood lipids were either not associated, or negatively associated with inflammatory biomarkers, except for associations of oxidized LDL and inflammation which were limited to obese adults. Further, APOE4 carriers maintain higher levels of total and LDL cholesterol at low body mass indices (BMIs). These results suggest that the relationship between APOE4 and lipids may be beneficial for pathogen-driven immune responses and unlikely to increase cardiovascular risk in an active subsistence population.

Value-associated cues in the environment often enhance subsequent goal-directed behaviors in adults, a phenomenon supported by the integration of motivational and cognitive neural systems. Given that the interactions among these systems change throughout adolescence, we tested when the beneficial effects of value associations on subsequent cognitive control performance emerge during adolescence. Participants (N = 81) aged 13–20 completed a reinforcement learning task with four cue-incentive pairings that could yield high gain, low gain, high loss, or low loss outcomes. Next, participants completed a Go/NoGo task during fMRI where the NoGo targets comprised the previously learned cues, which tested how prior value associations influence cognitive control performance. Improved accuracy for previously learned high gain relative to low gain cues emerged with age. Older adolescents exhibited enhanced recruitment of the dorsal striatum and ventrolateral prefrontal cortex during cognitive control execution to previously learned high gain relative to low gain cues. Older adolescents also expressed increased coupling between the dorsal striatum and dorsolateral prefrontal cortex for high gain cues, whereas younger adolescents expressed increased coupling between the striatum and ventromedial prefrontal cortex. These findings reveal that learned high value cue-incentive associations enhance cognitive control in late adolescence in parallel with value-selective recruitment of corticostriatal systems.

During summer 2020, the Maricopa County Department of Public Health (MCDPH) responded to a surge in COVID-19 cases. We used internet-based platforms to automate case notifications, prioritized investigation of cases more likely to have onward transmission or severe COVID-19 based on available preinvestigation information, and partnered with Arizona State University (ASU) to scale investigation capacity. We assessed the speed of automated case notifications and accuracy of our investigation prioritization criteria. Timeliness of case notification—the median time between receipt of a case report at MCDPH and first case contact—improved from 11 days to <1 day after implementation of automated case notification. We calculated the sensitivity and positive predictive value (PPV) of the investigation prioritization system by applying our high-risk prioritization criteria separately to data available pre- and postinvestigation to determine whether a case met these criteria preinvestigation, postinvestigation, or both. We calculated the sensitivity as the percentage of cases classified postinvestigation as high risk that had also been classified as high risk preinvestigation. We calculated PPV as the percentage of all cases deemed high risk preinvestigation that remained so postinvestigation. During June 30 to July 31, 2020, a total of 55 056 COVID-19 cases with an associated telephone number (94% of 58 570 total cases) were reported. Preinvestigation, 8799 (16%) cases met high-risk criteria. Postinvestigation, 17 037 (31%) cases met high-risk criteria. Sensitivity was 52% and PPV was 98%. Automating case notifications, prioritizing investigations, and collaborating with ASU improved the timeliness of case contact, focused public health resources toward high-priority cases, and increased investigation capacity. Establishing partnerships between health departments and academia might be a helpful strategy for future surge capacity planning.

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From May through July 2020, Arizona was a global hotspot for new COVID-19 cases. In response to the surge of cases, local public health departments looked for innovative ways to form external partnerships to address their staffing needs. In collaboration with the Maricopa County Department of Public Health, the Arizona State University Student Outbreak Response Team (SORT) created and implemented a virtual call center to conduct public health case investigations for COVID-19. SORT officially launched a dedicated COVID-19 case investigation program after 3 weeks of program design and training. From June 29 through November 8, 2020, SORT recruited and trained 218 case investigators, completed 5000 case patient interviews, and closed 10 000 cases. Our team also developed process improvements to address disparities in case investigation timeliness. A strong infrastructure designed to accommodate remote case investigations, paired with a large workforce, enabled SORT to provide additional surge capacity for the county’s high volume of cases. University-driven multidisciplinary case investigator teams working in partnership with state, tribal, and local public health staff members can be an effective tool for supporting a diverse and growing public health workforce. We discuss the essential design factors involved in building a university program to complement local COVID-19 response efforts, including workflows for case management, volunteer case investigator recruitment and training, secure technology platforms for conducting case investigations remotely, and robust data-tracking procedures for maintaining quality control and timely case reporting.

STUDY QUESTION How did the first two coronavirus disease 2019 (COVID-19) waves affect fertility rates in the USA? SUMMARY ANSWER States differed widely in how their fertility rates changed following the COVID-19 outbreak and these changes were influenced more by state-level economic, racial, political, and social factors than by COVID-19 wave severity. WHAT IS KNOWN ALREADY The outbreak of the COVID-19 pandemic contributed to already declining fertility rates in the USA, but not equally across states. Identifying drivers of differential changes in fertility rates can help explain variations in demographic shifts across states in the USA and motivate policies that support families in general, not only during crises. STUDY DESIGN, SIZE, DURATION This is an ecological study using state-level data from 50 US states and the District of Columbia (n = 51). The study period extends from 2020 to 2021 with historical data from 2016 to 2019. We identified Wave 1 as the first apex for each state after February 2020 and Wave 2 as the second apex, during Fall/Winter 2020–2021. PARTICIPANTS/MATERIALS, SETTING, METHODS State-level COVID-19 wave severity, defined as case acceleration during each 3-month COVID-19 wave (cases/100 000 population/month), was derived from 7-day weekly moving average COVID-19 case rates from the US Centers for Disease Control and Prevention (CDC). State-level fertility rate changes (change in average monthly fertility rate/100 000 women of reproductive age (WRA)/year) were derived from the CDC Bureau of Vital Statistics and from 2020 US Census and University of Virginia 2021 population estimates 9 months after each COVID-19 wave. We performed univariate analyses to describe national and state-level fertility rate changes following each wave, and simple and multivariable linear regression analyses to assess the relation of COVID-19 wave severity and other state-level characteristics with fertility rate changes. MAIN RESULTS AND THE ROLE OF CHANCE Nationwide, fertility dropped by 17.5 births/month/100 000 WRA/year following Wave 1 and 9.2 births/month/100 000 WRA/year following Wave 2. The declines following Wave 1 were largest among majority-Democrat, more non-White states where people practiced greater social distancing. Greater COVID-19 wave severity was associated with steeper fertility rate decline post-Wave 1 in simple regression, but the association was attenuated when adjusted for other covariates. Adjusting for the economic impact of the pandemic (hypothesized mediator) also attenuated the effect. There was no relation between COVID-19 wave severity and fertility rate change following Wave 2. LIMITATIONS, REASONS FOR CAUTION Our study harnesses state-level data so individual-level conclusions cannot be inferred. There may be residual confounding in our multivariable regression and we were underpowered to detect some effects. WIDER IMPLICATIONS OF THE FINDINGS The COVID-19 pandemic initially impacted the national fertility rate but, overall, the fertility rate rebounded to the pre-pandemic level following Wave 2. Consistent with prior literature, COVID-19 wave severity did not appear to predict fertility rate change. Economic, racial, political, and social factors influenced state-specific fertility rates during the pandemic more than the severity of the outbreak alone. Future studies in other countries should also consider whether these factors account for internal heterogeneity when examining the impact of the COVID-19 pandemic and other crises on fertility. STUDY FUNDING/COMPETING INTEREST(S) L.G.K. received funding from the National Institute of Environmental Health Sciences (R00ES030403), M.C. from the National Science Foundation Graduate Research Fellowship Program (20-A0-00-1005789), and M.L. and E.S. from the National Institute of Environmental Health Sciences (R01ES032808). None of the authors have competing interests. TRIAL REGISTRATION NUMBER N/A.