A Randomized Trial of Choice Architecture and Mailed Colorectal Cancer Screening Outreach in a Community Health Setting.

BACKGROUND & AIMS: Mailed outreach for CRC screening increases uptake but it is unclear how to offer the choice of testing. We evaluated if the active choice between colonoscopy and fecal immunochemical test (FIT), or FIT alone, increased response compared to colonoscopy alone. METHODS: This pragmatic randomized controlled trial at a community health center included patients between ages 50-74 who were not up to date with CRC screening. Patients were randomized 1:1:1 to: (1) Colonoscopy Only, (2) Active Choice of Colonoscopy or FIT, or (3) FIT Only. Patients received an outreach letter with instructions for testing (colonoscopy referral and/ or enclosed FIT kit), a reminder letter at 2 months, and another reminder at 3-5 months via text message or automated voice recording. The primary outcome was CRC screening completion within 6 months. RESULTS: Among 738 patients in the final analysis, the mean age was 58.7 (SD 6.2); 48.6% were insured by Medicaid and 24.3% by Medicare; 71.7% were White, 16.9% were Black, and 7.3% were Hispanic/ Latino. At 6 months, 5.6% (95% CI, 2.8 to 8.5) completed screening in the Colonoscopy Only arm, 12.8% (95% CI, 8.6 to 17.0) in the Active Choice arm, and 11.3% (95% CI, 7.4 to 15.3) in the FIT Only arm. Compared to Colonoscopy Only, there was a significant increase in screening in Active Choice (absolute difference of 7.1%; 95% CI, 2.0 to 12.2; p=0.006) and FIT Only (absolute difference of 5.7%; 95% CI, 0.8 to 10.6; p=0.02). CONCLUSION: Both choice of testing and FIT alone increased response and may align with patient preferences.

Historic Cognitive Function Trajectories as Predictors of Sedentary Behavior and Physical Activity in Older Adults.

BACKGROUND: We examined whether trajectories of cognitive function over 10 years predict later life physical activity (PA), sedentary time (ST), and sleep. METHODS: Participants were from the Adult Changes in Thought (ACT) cohort study. We included 611 ACT participants who wore accelerometers and had 3+ measures of cognition in the 10 years prior to accelerometer wear. The Cognitive Assessment Screening Instrument (CASI) measured cognition and was scored using item-response theory (IRT). activPAL and ActiGraph accelerometers worn over 7 days measured ST and PA outcomes. Self-reported time in bed and sleep quality measured sleep outcomes. Analyses used growth mixture modeling to classify CASI-IRT scores into latent groups and examine associations with PA, ST, and sleep including demographic and health covariates. Results: Participants (Mean age = 80.3 (6.5) years, 90.3% White, 57.1% female, 29.3% had less than 16 years of education) fell into 3 latent trajectory groups: average stable CASI (56.1%), high stable CASI (34.0%), and declining CASI (9.8%). The declining group had 16 min less stepping time (95% CI 0.6, 31.4), 1517 fewer steps/day (95% CI 138, 2896), and 16.3 min/day less moderate-to-vigorous PA (95% CI (1.3, 31.3) compared to the average stable group. There were no associations between CASI trajectory and sedentary or sleep outcomes. CONCLUSION: Declining cognition predicted lower PA providing some evidence of a reverse relationship between PA and cognition in older adults. However, this conclusion is limited by having outcomes at only one time point, a non-representative sample, self-reported sleep outcomes, and using a global cognition measure.

Testing the missing at random assumption in generalized linear models in the presence of instrumental variables.

Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present a new hypothesis testing approach for deciding between the conventional notions of missing at random and missing not at random in generalized linear models in the presence of instrumental variables. The foundational idea is to develop appropriate discrepancy measures between estimators whose properties significantly differ only when missing at random does not hold. We show that our testing approach achieves an objective data-oriented choice between missing at random or not. We demonstrate the feasibility, validity, and efficacy of the new test by theoretical analysis, simulation studies, and a real data analysis.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

Associations Between Gestational Weight Gain, Gestational Diabetes, and Childhood Obesity Incidence.

INTRODUCTION: Excessive maternal gestational weight gain (GWG) is strongly correlated with childhood obesity, yet how excess maternal weight gain and gestational diabetes mellitus (GDM) interact to affect early childhood obesity is poorly understood. The purpose of this study was to investigate whether overall and trimester-specific maternal GWG and GDM were associated with obesity in offspring by age 6 years. METHODS: A cohort of 10,335 maternal-child dyads was established from electronic health records. Maternal weights at conception and delivery were estimated from weight trajectory fits using functional principal components analysis. Kaplan-Meier curves and Cox regression, together with generalized raking, examined time-to-childhood-obesity. RESULTS: Obesity diagnosed prior to age 6 years was estimated at 19.7% (95% CI: 18.3, 21.1). Maternal weight gain during pregnancy was a strong predictor of early childhood obesity (p < 0.0001). The occurrence of early childhood obesity was lower among mothers with GDM compared with those without diabetes (adjusted hazard ratio = 0.58, p = 0.014). There was no interaction between maternal weight gain and GDM (p = 0.55). Higher weight gain during the first trimester was associated with lower risk of early childhood obesity (p = 0.0002) whereas higher weight gain during the second and third trimesters was associated with higher risk (p < 0.0001). DISCUSSION: Results indicated total and trimester-specific maternal weight gain was a strong predictor of early childhood obesity, though obesity risk by age 6 was lower for children of mothers with GDM. Additional research is needed to elucidate underlying mechanisms directly related to trimester-specific weight gain and GDM that impede or protect against obesity prevalence during early childhood.

Excessive maternal gestational weight gain (GWG) and gestational diabetes mellitus (GDM) have been linked to childhood obesity. Yet, research on how excessive total and trimester-specific GWG and GDM interact to affect early childhood obesity remains inconclusive. This study found that inadequate weight gain in the first trimester and excessive weight gain in the second and third trimester were associated with higher risks of childhood obesity by age 6. No significant interaction between maternal GWG and GDM was noted suggesting that these two important maternal conditions do not have a combined effect on the risk of early childhood obesity.

Three-phase generalized raking and multiple imputation estimators to address error-prone data.

Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more than one round of data validation may be required, and direct application of standard approaches for combining validation data into analyses may lead to inefficient estimators since the information available from intermediate validation steps is only partially considered or even completely ignored. In this paper, we present two novel extensions of multiple imputation and generalized raking estimators that make full use of all available data. We show through simulations that incorporating information from intermediate steps can lead to substantial gains in efficiency. This work is motivated by and illustrated in a study of contraceptive effectiveness among 83 671 women living with HIV, whose data were originally extracted from electronic medical records, of whom 4732 had their charts reviewed, and a subsequent 1210 also had a telephone interview to validate key study variables.

Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions.

In response to the escalating global obesity crisis and its associated health and financial burdens, this paper presents a novel methodology for analyzing longitudinal weight loss data and assessing the effectiveness of financial incentives. Drawing from the Keep It Off trial-a three-arm randomized controlled study with 189 participants-we examined the potential impact of financial incentives on weight loss maintenance. Given that some participants choose not to weigh themselves because of small weight change or weight gains, which is a common phenomenon in many weight-loss studies, traditional methods, for example, the Generalized Estimating Equations (GEE) method tends to overestimate the effect size due to the assumption that data are missing completely at random. To address this challenge, we proposed a framework which can identify evidence of missing not at random and conduct bias correction using the estimating equation derived from pairwise composite likelihood. By analyzing the Keep It Off data, we found that the data in this trial are most likely characterized by non-random missingness. Notably, we also found that the enrollment time (i.e., duration time) would be positively associated with the weight loss maintenance after adjusting for the baseline participant characteristics (e.g., age, sex). Moreover, the lottery-based intervention was found to be more effective in weight loss maintenance compared with the direct payment intervention, though the difference was non-statistically significant. This framework's significance extends beyond weight loss research, offering a semi-parametric approach to assess missing data mechanisms and robustly explore associations between exposures (e.g., financial incentives) and key outcomes (e.g., weight loss maintenance). In essence, the proposed methodology provides a powerful toolkit for analyzing real-world longitudinal data, particularly in scenarios with data missing not at random, enriching comprehension of intricate dataset dynamics.

Effects of Aircraft Noise on Sleep: Federal Aviation Administration National Sleep Study Protocol.

Aircraft noise can disrupt sleep and impair recuperation. The last U.S. investigation into the effects of aircraft noise on sleep dates back more than 20 years. Since then, traffic patterns and the noise levels produced by single aircraft have changed substantially. It is therefore important to acquire current data on sleep disturbance relative to varying degrees of aircraft noise exposure in the U.S. that can be used to check and potentially update the existing noise policy. This manuscript describes the design, procedures, and analytical approaches of the FAA's National Sleep Study. Seventy-seven U.S. airports with relevant nighttime air traffic from 39 states are included in the sampling frame. Based on simulation-based power calculations, the field study aims to recruit 400 participants from four noise strata and record an electrocardiogram (ECG), body movement, and sound pressure levels in the bedroom for five consecutive nights. The primary outcome of the study is an exposure-response function between the instantaneous, maximum A-weighted sound pressure levels (dBA) of individual aircraft measured in the bedroom and awakening probability inferred from changes in heart rate and body movement. Self-reported sleep disturbance due to aircraft noise is the secondary outcome that will be associated with long-term average noise exposure metrics such as the Day-Night Average Sound Level (DNL) and the Nighttime Equivalent Sound Level (Lnight). The effect of aircraft noise on several other physiological and self-report outcomes will also be investigated. This study will provide key insights into the effects of aircraft noise on objectively and subjectively assessed sleep disturbance.

Practical considerations for sandwich variance estimation in two-stage regression settings.

We present a practical approach for computing the sandwich variance estimator in two-stage regression model settings. As a motivating example for two-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has been rarely applied in regression calibration, despite it requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is likely due to requiring specialized statistical coding. We first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich to a resampling variance approach for both settings. Finally, we further compare these two variance estimation approaches for data examples from the Women's Health Initiative (WHI) and Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or over-covered in certain settings, particularly when there was high correlation between covariates or large measurement error.

An augmented likelihood approach for the Cox proportional hazards model with interval-censored auxiliary and validated outcome data-with application to the Hispanic Community Health Study/Study of Latinos.

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet.

Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study.

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Nutritional Blood Concentration Biomarkers in the Hispanic Community Health Study/Study of Latinos: Measurement Characteristics and Power.

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes.

A Randomized Controlled Pilot Study of Just4Us, a Counseling and Navigation Intervention to Promote Oral HIV Prophylaxis Uptake Among PrEP-Eligible Cisgender Women.

HIV pre-exposure prophylaxis (PrEP) uptake among cisgender women in the United States is low. Just4Us, a theory-based counseling and navigation intervention, was evaluated in a pilot randomized controlled trial among PrEP-eligible women (n = 83). The comparison arm was a brief information session. Women completed surveys at baseline, post-intervention, and at three months. In this sample, 79% were Black, and 26% were Latina. This report presents results on preliminary efficacy. At 3 months follow-up, 45% made an appointment to see a provider about PrEP; only 13% received a PrEP prescription. There were no differences in PrEP initiation by study arm (9% Info vs. 11% Just4Us). PrEP knowledge was significantly higher in the Just4Us group at post-intervention. Analysis revealed high PrEP interest with many personal and structural barriers along the PrEP continuum. Just4Us is a promising PrEP uptake intervention for cisgender women. Further research is needed to tailor intervention strategies to multilevel barriers.Clinicaltrials.gov registration NCT03699722: A Women-Focused PrEP Intervention (Just4Us).

RESUMEN: La aceptación de la profilaxis previa a la exposición (PrEP) al VIH entre las mujeres cisgénero en los Estados Unidos es baja. Just4Us, una intervención de asesoramiento y navegación basada en la teoría, se evaluó en un ensayo piloto controlado aleatorizado con mujeres aptas para la PrEP (n = 83). El brazo de comparación fue una breve sesión de información. Las mujeres completaron encuestas al inicio, después de la intervención ya los 3 meses. En la muestra, el 79% eran negros y el 26% eran latinas. Este informe presenta resultados sobre la eficacia preliminar. A los 3 meses de seguimiento, el 45% hizo una cita para ver a un proveedor acerca de la PrEP; solo el 13% recibió una receta de PrEP. No hubo diferencias en el inicio de la PrEP por brazo de estudio (9% Info frente a 11% Just4Us). El conocimiento fue significativamente mayor en el grupo Just4Us después de la intervención. El análisis reveló un alto interés por la PrEP con muchas barreras personales y estructurales a lo largo del continuo de la PrEP. Just4Us es una prometedora intervención de adopción de PrEP para mujeres cisgénero. Se necesita más investigación para adaptar las estrategias de intervención a las barreras multinivel.

Multicultural Healthy Diet to Reduce Cognitive Decline & Alzheimer's Disease Risk: Study protocol for a pilot randomized controlled trial.

BACKGROUND: Emerging evidence indicates that healthy dietary patterns are associated with higher cognitive status; however, few clinical trials have explored this association in diverse middle-aged adults before the onset of cognitive decline. We use novel ambulatory methods to assess cognition in natural settings in tandem with diet recording. AIMS: We investigate whether the Multicultural Healthy Diet Study to Reduce Cognitive Decline & Alzheimer's Disease Risk, a pilot randomized controlled trial of an anti-inflammatory dietary pattern compared to usual diet, can mitigate cognitive decline and Alzheimer's Disease risk in a diverse population of 40-65 year old adults in Bronx, New York. METHODS: Primary cognitive outcomes assessed at nine months are collected in an ecological momentary assessment "measurement burst" design, over the course of participants' daily lives. These ultra-brief, ambulatory cognitive assessments examine processing speed, visuospatial working memory, short-term associative memory binding, long-term associative memory, and working memory capacity. Key secondary outcomes relate to comparing dietary intake between study arms with respect to cognitive outcomes. We assess diet with food records using the National Cancer Institute's Automated Self-Administered 24-h record and serum biomarkers. We further investigate the association of self-reported diet and dietary biomarkers with inflammatory-based biomarkers. CONCLUSION: This randomized controlled trial of diet and cognition for the first time combines novel measures of ambulatory cognitive assessment with web-based assessment of dietary intake recording. This new approach enabled the study to continue in the midst of the COVID-19 pandemic in remote format.

Multiwave validation sampling for error-prone electronic health records.

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data.

Carbon Isotope Ratios of Plasma and RBC Fatty Acids Identify Meat Consumers in a 12-Week Inpatient Feeding Study of 32 Men.

BACKGROUND: Molecular stable isotope ratios are a novel type of dietary biomarker with high sensitivity and specificity for certain foods. Among these, fatty acid carbon isotope ratios (CIRs) have strong potential but have not been investigated as dietary biomarkers. OBJECTIVES: We evaluated whether fatty acid CIRs and mass proportions were associated with meat, fish, and sugar-sweetened beverage (SSB) intake. METHODS: Thirty-two men [aged 46.2 ± 10.5 y; BMI (kg/m2): 27.2 ± 4.0] underwent a 12-wk inpatient dietary intervention at the National Institute of Diabetes and Digestive and Kidney Diseases in Phoenix, Arizona. Men were randomly assigned to 1 of 8 dietary treatments varying the presence/absence of dietary meat, fish, and SSBs in all combinations. Fatty acid CIRs and mass proportions were measured in fasting blood samples and adipose tissue biopsies that were collected pre- and postintervention. Dietary effects were analyzed using multivariable regression and receiver operating characteristic AUCs were calculated using logistic regression. RESULTS: CIRs of the several abundant SFAs, MUFAs and arachidonic acid (20:4n-6) in plasma were strongly associated with meat, as were a subset of these fatty acids in RBCs. Effect sizes in plasma ranged from 1.01‰ to 1.93‰ and were similar but attenuated in RBCs. Mass proportions of those fatty acids were not associated with diet. CIRs of plasma dihomo-γ-linolenic acid (20:3n-6) and adipose palmitic acid (16:0) were weakly associated with SSBs. Mass proportions of plasma odd-chain fatty acids were associated with meat, and mass proportions of plasma EPA and DHA (20:5n-3 and 22:6n-3) were associated with fish. CONCLUSIONS: CIRs of plasma and RBC fatty acids show promise as sensitive and specific measures of dietary meat. These provide different information from that provided by fatty acid mass proportions, and are informative where mass proportion is not. This trial is registered at www.clinicaltrials.gov as NCT01237093.

HIV Self-testing and Risk Behaviors Among Men Who Have Sex With Men in 23 US Cities, 2017.

Importance: HIV self-testing (HIVST) is a promising strategy to expand the HIV care continuum, particularly among priority populations at high risk of HIV infection. However, little is known about HIVST uptake among men who have sex with men (MSM) outside of clinical trial settings. Objective: To evaluate HIVST use among urban MSM in the US who reported testing within the past 12 months. Design, Setting, and Participants: A cross-sectional study of adult MSM in the 2017 National HIV Behavioral Surveillance system, which used venue-based sampling methods to collect data related to HIV testing, receipt of prevention services, and risk factors for HIV, was conducted at 588 venues in 23 urban areas in the contiguous US and Puerto Rico. All participants were offered HIV testing. Adult cisgender MSM who reported HIV-negative or unknown HIV status and obtained HIV testing in the past 12 months were included. Data for this study were collected between June 4, 2017, and December 22, 2017, and analyzed between October 23, 2020, and August 20, 2021. Main Outcomes and Measures: Self-reported HIVST in the past year. Adjusted prevalence ratios (aPRs) using survey weights were calculated to assess factors associated with HIVST. Results: A total of 6563 MSM in 23 urban areas met inclusion criteria, of whom 506 (7.7%) individuals reported HIVST in the past year. The median age of self-testers was 29 (IQR, 25-35) years, 52.8% had completed college, and 37.9% reported non-Hispanic White race. One self-tester reported seroconverting in the prior 12 months, and an additional 10 self-testers were diagnosed with HIV during the survey. HIVST was associated with sexual orientation disclosure (aPR, 10.27; 95% CI, 3.45-30.60; P < .001), perceived discrimination against people with HIV (aPR, 1.53; 95% CI, 1.09-2.03; P = .01), younger age (aPR, 0.74; 95% CI, 0.66-0.84; P < .001), higher educational level (aPR, 1.20; 95% CI, 1.04-1.37; P = .01), and higher income levels (aPR, 1.18; 95% CI, 1.04-1.32; P = .009). No association was noted with condomless anal sex (aPR, 0.96; 95% CI, 0.88-1.06, P = .88), sexually transmitted infections (aPR, 0.96; 95% CI, 0.70-1.30; P = .77), or preexposure prophylaxis use (aPR, 0.99; 95% CI, 0.75-1.30; P = .92). Conclusions and Relevance: In this study, HIVST was relatively uncommon in this sample of urban MSM. HIVST may not be reaching those with lower socioeconomic status or who have not disclosed their sexual identity. The findings of this study suggest that efforts to increase HIVST should focus on engaging underserved and vulnerable subgroups of MSM.