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In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P  < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers. [ABSTRACT FROM AUTHOR]

In 2019, the National Institutes of Health combined the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) into the MACS/WIHS Combined Cohort Study (MWCCS). In this paper, participants who made a study visit during October 2018–September 2019 (targeted for MWCCS enrollment) are described by human immunodeficiency virus (HIV) serostatus and compared with people living with HIV (PLWH) in the United States. Participants include 2,115 women and 1,901 men with a median age of 56 years (interquartile range, 48–63); 62% are PLWH. Study sites encompass the South (18%), the Mid-Atlantic/Northeast (45%), the West Coast (22%), and the Midwest (15%). Participant race/ethnicity approximates that of PLWH throughout the United States. Longitudinal data and specimens collected for 35 years (men) and 25 years (women) were combined. Differences in data collection and coding were reviewed, and key risk factor and comorbidity data were harmonized. For example, recent use of alcohol (62%) and tobacco (28%) are common, as are dyslipidemia (64%), hypertension (56%), obesity (42%), mildly or severely impaired daily activities (31%), depressive symptoms (28%), and diabetes (22%). The MWCCS repository includes serum, plasma, peripheral blood mononuclear cells, cell pellets, urine, cervicovaginal lavage samples, oral samples, B-cell lines, stool, and semen specimens. Demographic differences between the MACS and WIHS can confound analyses by sex. The merged MWCCS is both an ongoing observational cohort study and a valuable resource for harmonized longitudinal data and specimens for HIV-related research. [ABSTRACT FROM AUTHOR]

In this article, we present a variety of measures that quantify equity in cancer mortality outcomes, demonstrate how the measures perform with various cancer types, and identify counties, or "bright spots," that meet the criteria of those measures. Using county-level age-adjusted mortality rates for 2007–2016 from the National Center for Health Statistics, we identified counties that had both equitable and optimal outcomes for Black and White death rates across 5 types of cancer: cancers of the lung/bronchus, prostate, female breast, colorectum, and liver. The number of counties that met the criteria ranged from 0 to 442, depending on cancer type and measure used. Prostate cancer and male liver cancer consistently had the lowest number of "bright spots," with a maximum of 3 counties meeting the most lenient criteria. This paper presents several ways to examine equity, using rate ratios and standard error measures, in cancer mortality outcomes. It highlights areas with positive progress toward equity and areas with a potential need for equity-focused cancer-control planning. Examining local areas of positive deviance can inform cancer-control programming and planning around health equity. [ABSTRACT FROM AUTHOR]

This paper examines short-term birth sequelae of the influenza pandemic of 1918–1920 in the United States using monthly data on births and all-cause deaths for 19 US states in conjunction with data on maternal deaths, stillbirths, and premature births. The data on births and all-cause deaths are adjusted for seasonal and trend effects, and the residual components of the 2 time series coinciding with the timing of peak influenza mortality are examined for these sequelae. Notable findings include: 1) a drop in births in the 3 months following peak mortality; 2) a reversion in births to normal levels occurring 5–7 months after peak mortality; and 3) a steep drop in births occurring 9–10 months after peak mortality. Interpreted in the context of parallel data showing elevated premature births, stillbirths, and maternal mortality during times of peak influenza mortality, these findings suggest that the main impacts of the 1918–1920 influenza on reproduction occurred through: 1) impaired conceptions, possibly due to effects on fertility and behavioral changes; 2) an increase in the preterm delivery rate during the peak of the pandemic; and 3) elevated maternal and fetal mortality, resulting in late-term losses in pregnancy. [ABSTRACT FROM AUTHOR]

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011–2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2–18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data. [ABSTRACT FROM AUTHOR]

We evaluated the performance of a semiquantitative food frequency questionnaire (SFFQ), the Automated Self-Administered 24-Hour Dietary Recall (ASA24), and 7-day dietary records (7DDRs), in comparison with biomarkers, in the estimation of nutrient intakes among 627 women in the Women's Lifestyle Validation Study (United States, 2010-2012). Two paper SFFQs, 1 Web-based SFFQ, 4 ASA24s (beta version), 2 7DDRs, 4 24-hour urine samples, 1 doubly labeled water measurement (repeated among 76 participants), and 2 fasting blood samples were collected over a 15-month period. The dietary variables evaluated were energy, energy-adjusted intakes of protein, sodium, potassium, and specific fatty acids, carotenoids, a-tocopherol, retinol, and folate. In general, relative to biomarkers, averaged ASA24s had lower validity than the SFFQ completed at the end of the data-collection year (SFFQ2); SFFQ2 had slightly lower validity than 1 7DDR; the averaged SFFQs had validity similar to that of 1 7DDR; and the averaged 7DDRs had the highest validity. The deattenuated correlation of energy-adjusted protein intake assessed by SFFQ2 with its biomarker was 0.46, similar to its correlation with 7DDRs (deattenuated r = 0.54). These data indicate that the SFFQ2 provides reasonably valid measurements of energy-adjusted intake for most of the nutrients assessed in our study, consistent with earlier conclusions derived using 7DDRs as the comparison method. The ASA24 needs further evaluation for use in large population studies, but an average of 3 days of measurement will not be sufficient for some important nutrients. [ABSTRACT FROM AUTHOR]

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (AmJ Epidemiol. 2018;187(3):568-575). [ABSTRACT FROM AUTHOR]

Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576-584 and Am J Epidemiol. 2018;187(3):585-591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93),while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the "true" full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research. [ABSTRACT FROM AUTHOR]

The acquired immune deficiency syndrome (AIDS) epidemic was first recognized in 1981, and it quickly became a public health emergency. In a 1987 paper in the American Journal of Epidemiology (Am J Epidemiol. 1987;126(2): 310-318), Richard Kaslow et al. described the launch of the Multicenter AIDS Cohort Study (MACS), a cohort study of homosexual men in 4 US cities, the purpose of which was to better understand the natural history of AIDS and its determinants. The MACS enrolled participants through a range of community contacts. These efforts facilitated rapid recruitment, but given the targeted approaches, participants tended to comprise high-risk social networks. At baseline, 4%-26% of participants at the 4 sites reported having a sexual partner who had developed AIDS. Kaslow et al. also described baseline testing for the causative agent of AIDS, the human immunodeficiency virus (HIV). HIV sero- prevalence was remarkably high, ranging from 11 %-26% across age groups in Pittsburgh to 38%-53% in Los Angeles. The major turning point in the epidemic occurred in 1995-1996 when combination antiretroviral therapy was introduced, effectively blocking HIV replication and markedly reducing AIDS morbidity and mortality. The MACS cohort continues to be followed actively 3 decades after its launch and has proven to be an important resource for information on HIV infection and AIDS. [ABSTRACT FROM AUTHOR]

Surveillance systems in public health practice have increased in number and sophistication with advances in data collection, analysis, and communication. When the Communicable Disease Center (now the Centers for Disease Control and Prevention) was founded some 70 years ago, surveillance referred to the close observation of individuals with suspected smallpox, plague, or cholera. Alexander Langmuir, head of the Epidemiology Branch, redefined surveillance as the epidemiology-based critical factor in infectious disease control. I joined Langmuir as assistant chief in 1955 and was appointed chief of the Surveillance Section in 1961. In this paper, I describe Langmuir's redefinition of surveillance. Langmuir asserted that its proper use in public health meant the systematic reporting of infectious diseases, the analysis and epidemiologic interpretation of data, and both prompt and widespread dissemination of results. I outline the Communicable Disease Center's first surveillance systems for malaria, poliomyelitis, and influenza. I also discuss the role of surveillance in the global smallpox eradication program, emphasizing that the establishment of systematic reporting systems and prompt action based on results were critical factors of the program. [ABSTRACT FROM AUTHOR]

The article presents the authors' response to Dr.S. Harper's commentary on their paper and his effort to produce complementary absolute measures. Topics discussed include the use of the age-period-cohort (APC) toolbox to examine temporal evolution of a population health disparity, pitfalls in APC analysis and underlying assumptions necessary to gain useful information and their conclusion that cohort differences contributed more to racial disparities in heart disease mortality.

The article presents the authors' reply to a letter from D. Meyre regarding their systematic review and meta-analysis on the associations of variants of proprotein convertase subtilisine/kexin type I gene (PCSKI) with obesity. The reply addresses Meyre's criticisms of what he perceives as errors in the paper including an assertion that meta-analysis included 2 overlapping cohorts and suggestion that the analysis was underpowered to find significant association of rs632 with body mass index.