Lower social vulnerability is associated with a higher prevalence of social media-involved violent crimes in Prince George's County, Maryland, 2018-2023.

BACKGROUND: Social vulnerability may play a role in social media-involved crime, but few studies have investigated this issue. We investigated associations between social vulnerability and social media-involved violent crimes. METHODS: We analyzed 22,801 violent crimes occurring between 2018 and 2023 in Prince George's County, Maryland. Social media involvement was obtained from crime reports at the Prince George's County Police Department. Social media application types included social networking, advertising/selling, ridesharing, dating, image/video hosting, mobile payment, instant messaging/Voice over Internet Protocol, and other. We used the Centers for Disease Control and Prevention's Social Vulnerability Index to assess socioeconomic status (SES), household characteristics, racial and ethnic minority status, housing type and transportation, and overall vulnerability. Modified Poisson models estimated adjusted prevalence ratios (aPRs) among the overall sample and stratified by crime type (assault and homicide, robbery, and sexual offense). Covariates included year and crime type. RESULTS: Relative to high tertile areas, we observed a higher prevalence of social media-involved violent crimes in areas with low SES vulnerability (aPR: 1.82, 95% CI: 1.37-2.43), low housing type and transportation vulnerability (aPR: 1.53, 95% CI: 1.17-2.02), and low overall vulnerability (aPR: 1.63, 95% CI: 1.23-2.17). Low SES vulnerability areas were significantly associated with higher prevalences of social media-involved assaults and homicides (aPR: 1.64, 95% CI: 1.02-2.62), robberies (aPR: 2.00, 95% CI: 1.28-3.12), and sexual offenses (aPR: 2.07, 95% CI: 1.02-4.19) compared to high SES vulnerability areas. Low housing type and transportation vulnerability (vs. high) was significantly associated with a higher prevalence of social media-involved robberies (aPR: 1.54, 95% CI:1.01-2.37). Modified Poisson models also indicated that low overall vulnerability areas had higher prevalences of social media-involved robberies (aPR: 1.71, 95% CI: 1.10-2.67) and sexual offenses (aPR: 2.14, 95% CI: 1.05-4.39) than high overall vulnerability areas. CONCLUSIONS: We quantified the prevalence of social media-involved violent crimes across social vulnerability levels. These insights underscore the need for collecting incident-based social media involvement in crime reports among law enforcement agencies across the United States and internationally. Comprehensive data collection at the national and international levels provides the capacity to elucidate the relationships between neighborhoods, social media, and population health.

Determinants of Breast Cancer Screening Adherence During the COVID-19 Pandemic in a Cohort at Increased Inherited Cancer Risk in the United States.

BACKGROUND: We examined neighborhood characteristics concerning breast cancer screening annual adherence during the COVID-19 pandemic. METHODS: We analyzed 6673 female patients aged 40 or older at increased inherited cancer risk in 2 large health care systems (NYU Langone Health [NYULH] and the University of Utah Health [UHealth]). Multinomial models were used to identify predictors of mammogram screening groups (non-adherent, pre-pandemic adherent, pandemic period adherent) in comparison to adherent females. Potential determinants included sociodemographic characteristics and neighborhood factors. RESULTS: Comparing each cancer group in reference to the adherent group, a reduced likelihood of being non-adherent was associated with older age (OR: 0.97, 95% CI: 0.95, 0.99), a greater number of relatives with cancer (OR: 0.80, 95% CI: 0.75, 0.86), and being seen at NYULH study site (OR: 0.42, 95% CI: 0.29, 0.60). More relatives with cancer were correlated with a lesser likelihood of being pandemic period adherent (OR: 0.89, 95% CI: 0.81, 0.97). A lower likelihood of being pre-pandemic adherent was seen in areas with less education (OR: 0.77, 95% CI: 0.62, 0.96) and NYULH study site (OR: 0.35, 95% CI: 0.22, 0.55). Finally, greater neighborhood deprivation (OR: 1.47, 95% CI: 1.08, 2.01) was associated with being non-adherent. CONCLUSION: Breast screening during the COVID-19 pandemic was associated with being older, having more relatives with cancer, residing in areas with less educational attainment, and being seen at NYULH; non-adherence was linked with greater neighborhood deprivation. These findings may mitigate risk of clinically important screening delays at times of disruptions in a population at greater risk for breast cancer.

Breast Cancer Screening Adherence in the US During COVID-19: We examined predictors of breast cancer screening adherence during COVID-19 at two large healthcare systems. Adherence was associated with older age, having more relatives with a cancer history, and living in areas with less educational attainment. Nonadherence was associated with greater neighborhood deprivation.

An Immediate but Fleeting Interest in MPH Programs After the Onset of COVID-19: An Interrupted Time-Series Analysis.

OBJECTIVES: The relationship between the onset of the COVID-19 pandemic and interest in master of public health (MPH) programs is unknown. We examined trends in MPH application rates for 31 MPH concentrations and specifically for the MPH concentration in epidemiology and differences by race and ethnicity before and after the onset of the COVID-19 pandemic. METHODS: We constructed a quasi-experimental design to examine trends in MPH application rates from academic years 2015-2016 through 2022-2023 by using Centralized Application Service for Schools and Programs of Public Health data. We used an interrupted time-series analysis to test whether application rates surged after the pandemic's onset (academic years 2019-2020 through 2020-2021) and whether this increase persisted during the pandemic (academic years 2020-2021 through 2022-2023). We fit models for the overall sample, a combined racially and ethnically minoritized sample, each racial and ethnic group separately, and a non-US citizen sample. RESULTS: The pandemic's onset correlated with an immediate increase in application rates across most samples: overall (38%) and among American Indian/Alaska Native/Native Hawaiian/Pacific Islander (91%), Asian (35%), Black (42%), Hispanic (60%), multiracial (30%), racially and ethnically minoritized (44%), and White (53%) samples. However, this trend was not sustained; application rate trends during the pandemic were significantly lower than prepandemic trends. Application rate trends for all MPH concentrations and the MPH in epidemiology concentration among non-US citizens were significantly higher during the pandemic than prepandemic. CONCLUSIONS: Our results highlight the need for innovative strategies to sustain MPH degree interest and a diverse applicant pool.

Associations between perceived discrimination over the life course, subjective social status, and health literacy: A racial/ethnic stratification analysis.

Objective: To analyze the relationship between perceived discrimination over the life course, social status, and limited health literacy (HL). Methods: 5040 adults who participated in the 2023 Survey of Racism and Public Health. We applied stratified multilevel models adjusted for sociodemographic characteristics. Results: The average age was 47 years, 48% identified as White, 20% as Latinx, and 17% as Black. In the overall sample, we observed associations of perceived discrimination (b = 0.05, 95% CI: 0.01, 0.09), subjective social status (b = -0.16, 95% CI: -0.23, -0.10), and their interaction (b = 0.02, 95% CI: 0.01, 0.03). More perceived discrimination was associated with lower HL in the White and Multiracial participants. Higher subjective social status was associated with higher HL in the White and Latinx participants. There was a statistically significant interaction between perceived discrimination and subjective social status on HL among the White, Latinx, and Multiracial participants. Conclusion: This analysis has implications for public health practice, indicating that multi-level interventions are needed to address limited HL. Innovation: Our findings provide novel insights for identifying key SDOH indicators to assess in clinical settings to provide health literate care.

Designing for data sharing: Considerations for advancing health equity in data management and dissemination.

Data sharing, the act of making scientific research data available to others, can accelerate innovation and discoveries, and ultimately enhance public health. The National Cancer Institute Implementation Science Centers in Cancer Control convened a diverse group of research scientists, practitioners, and community partners in three interactive workshops (May-June 2022) to identify and discuss factors that must be considered when designing research for equitable data sharing with a specific emphasis on implementation science and social, behavioral, and population health research. This group identified and operationalized a set of seven key considerations for equitable data sharing-conceptualized as an inclusive process that fairly includes the perspectives and priorities of all partners involved in and impacted by data sharing, with consideration of ethics, history, and benefits-that were integrated into a framework. Key data-sharing components particularly important for health equity included: elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and adverse impact of data sharing on diverse beneficiaries of health data and research.

Data sharing is a key strategy for advancing our understanding of human health and healthcare. Three interactive workshops that included researcher scientists, physicians, and community members were held by the National Cancer Institute Implementation Science Centers in Cancer Control. The group discussed ways to incorporate health equity and equitable data sharing into implementation science and social, behavioral, and population health research. Equitable data sharing is an inclusive process that considers the points of view and priorities of all partners involved with data sharing and considers ethics, history, and benefits. Seven key components emerged from these discussions and were included in a framework. The components included elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and negative impacts of data sharing on diverse beneficiaries of health data and research.

Uptake of Cancer Genetic Services for Chatbot vs Standard-of-Care Delivery Models: The BRIDGE Randomized Clinical Trial.

Importance: Increasing numbers of unaffected individuals could benefit from genetic evaluation for inherited cancer susceptibility. Automated conversational agents (ie, chatbots) are being developed for cancer genetics contexts; however, randomized comparisons with standard of care (SOC) are needed. Objective: To examine whether chatbot and SOC approaches are equivalent in completion of pretest cancer genetic services and genetic testing. Design, Setting, and Participants: This equivalence trial (Broadening the Reach, Impact, and Delivery of Genetic Services [BRIDGE] randomized clinical trial) was conducted between August 15, 2020, and August 31, 2023, at 2 US health care systems (University of Utah Health and NYU Langone Health). Participants were aged 25 to 60 years, had had a primary care visit in the previous 3 years, were eligible for cancer genetic evaluation, were English or Spanish speaking, had no prior cancer diagnosis other than nonmelanoma skin cancer, had no prior cancer genetic counseling or testing, and had an electronic patient portal account. Intervention: Participants were randomized 1:1 at the patient level to the study groups at each site. In the chatbot intervention group, patients were invited in a patient portal outreach message to complete a pretest genetics education chat. In the enhanced SOC control group, patients were invited to complete an SOC pretest appointment with a certified genetic counselor. Main Outcomes and Measures: Primary outcomes were completion of pretest cancer genetic services (ie, pretest genetics education chat or pretest genetic counseling appointment) and completion of genetic testing. Equivalence hypothesis testing was used to compare the study groups. Results: This study included 3073 patients (1554 in the chatbot group and 1519 in the enhanced SOC control group). Their mean (SD) age at outreach was 43.8 (9.9) years, and most (2233 of 3063 [72.9%]) were women. A total of 204 patients (7.3%) were Black, 317 (11.4%) were Latinx, and 2094 (75.0%) were White. The estimated percentage point difference for completion of pretest cancer genetic services between groups was 2.0 (95% CI, -1.1 to 5.0). The estimated percentage point difference for completion of genetic testing was -1.3 (95% CI, -3.7 to 1.1). Analyses suggested equivalence in the primary outcomes. Conclusions and Relevance: The findings of the BRIDGE equivalence trial support the use of chatbot approaches to offer cancer genetic services. Chatbot tools can be a key component of sustainable and scalable population health management strategies to enhance access to cancer genetic services. Trial Registration: ClinicalTrials.gov Identifier: NCT03985852.

Bayesian Kernel Machine Regression for Social Epidemiologic Research.

BACKGROUND: Little attention has been devoted to framing multiple continuous social variables as a "mixture" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects. METHODS: Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable. RESULTS: We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% CI: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31). CONCLUSIONS: With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

Racial Composition of Past and Current Social Environments and Health Literacy.

BACKGROUND: Research is needed to understand the impact of social determinants of health on health literacy throughout the life course. This study examined how racial composition of multiple past and current social environments was related to adults' health literacy. METHODS: In this study, 546 adult patients at a primary care clinic in St. Louis, Missouri, completed a self-administered written questionnaire that assessed demographic characteristics and a verbally administered component that assessed health literacy with the Rapid Estimate of Adult Literacy in Medicine - Revised (REALM-R) and Newest Vital Sign (NVS), and self-reported racial composition of six past and four current social environments. Multilevel logistic regression models were built to examine the relationships between racial composition of past and current social environments and health literacy. RESULTS: Most participants identified as Black or multiracial (61%), had a high school diploma or less (54%), and household income <$20,000 (72%). About 56% had adequate health literacy based on REALM-R and 38% based on NVS. In regression models, participants with multiple past white environments (e.g., locations/conditions in which most of the people who live, go to school, work, and have leisure time are White) and (vs. 0 or 1) were more likely to have adequate health literacy based on REALM-R (adjusted odds ratio [aOR] = 1.79; 95% confidence interval [CI]: 1.04-3.07). Similarly, participants who had multiple past white social environments were more likely (aOR = 1.94, 95% CI: 1.15-3.27) to have adequate health literacy based on NVS than those who had not. The racial composition of current social environments was not significantly associated with health literacy in either model. CONCLUSIONS: Racial composition of past, but not current, educational and residential social environments was significantly associated with adult health literacy. The results highlight the importance of examining the impact of social determinants over the life course on health literacy. The findings suggest that policies ensuring equitable access to educational resources in school and community contexts is critical to improving equitable health literacy. [HLRP: Health Literacy Research and Practice. 2024;8(3):e130-e139.].

PLAIN LANGUAGE SUMMARY: We studied how the racial make-up of past and current places where people live, work, and go to school were related to their health literacy as adults. We found that the racial make-up of past places, but not current places, was related to health literacy. Our results show the need to study the impact of childhood places on health literacy.

Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

BACKGROUND: Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. OBJECTIVE: This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). METHODS: The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. RESULTS: For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. CONCLUSIONS: These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities: Rigorous Methods Are Available.

In June 2022, the NIH Office of Disease Prevention (ODP) issued a Call for Papers for a Supplemental Issue to Prevention Science on Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities. ODP sought to bring together current thinking and new ideas about design and analytic methods for studies aimed at reducing health disparities, including strategies for balancing methodological rigor with design feasibility, acceptability, and ethical considerations. ODP was particularly interested in papers on design and analytic methods for parallel group- or cluster-randomized trials (GRTs), stepped-wedge GRTs, group-level regression discontinuity trials, and other methods appropriate for evaluating multilevel interventions. In this issue, we include 12 papers that report new methods, provide examples of strong applications of existing methods, or provide guidance on developing multilevel interventions to reduce health disparities. These papers provide examples showing that rigorous methods are available for the design and analysis of multilevel interventions to reduce health disparities.

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression.

Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use research.Objective: To describe the utility of the BKMR approach to study the effects of drug substance mixtures on health outcomes.Methods: We simulated data for 200 individuals. Using the Vale and Maurelli method, we simulated multivariate non-normal drug exposure data: xylazine (mean = 300 ng/mL, SD = 100 ng/mL), fentanyl (mean = 200 ng/mL, SD = 71 ng/mL), benzodiazepine (mean = 300 ng/mL, SD = 55 ng/mL), and nitazene (mean = 200 ng/mL, SD = 141 ng/mL) concentrations. We performed 10,000 MCMC sampling iterations with three Markov chains. Model diagnostics included trace plots, r-hat values, and effective sample sizes. We also provided visual relationships of the univariate and bivariate exposure-response and the overall mixture effect.Results: Higher levels of fentanyl and nitazene concentrations were associated with higher levels of the simulated health outcome, controlling for age. Trace plots, r-hat values, and effective sample size statistics demonstrated BKMR stability across multiple Markov chains.Conclusions: Our understanding of drug mixtures tends to be limited to studies of single-drug models. BKMR offers an innovative way to discern which substances pose a greater health risk than other substances and can be applied to assess univariate, bivariate, and cumulative drug effects on health outcomes.

Associations between subjective social status and predictors of interest in genetic testing among women diagnosed with breast cancer at a young age.

PURPOSE: Genetic testing for gene mutations which elevate risk for breast cancer is particularly important for women diagnosed at a young age. Differences remain in access and utilization to testing across social groups, and research on the predictors of interest in genetic testing for women diagnosed at a young age is limited. METHODS: We examined the relationships between subjective social status (SSS) and variables previously identified as possible predictors of genetic testing, including genome sequencing knowledge, genetic worry, cancer worry, health consciousness, decision-making preferences, genetic self-efficacy, genetic-related beliefs, and subjective numeracy, among a cohort of women who were diagnosed with breast cancer at a young age. RESULTS: In this sample (n = 1,076), those who had higher SSS had significantly higher knowledge about the limitations of genome sequencing (Odds Ratio (OR) = 1.11; 95% CI = 1.01-1.21) and significantly higher informational norms (OR = 1.93; 95% CI = 1.19-3.14) than those with lower SSS. Similarly, education (OR = 2.75; 95% CI = 1.79-4.22), health status (OR = 2.18; 95% CI = 1.44-3.31) were significant predictors among higher SSS women compared to lower SSS women in our multivariate analysis. Lower SSS women with low self-reported income (OR = 0.13; 95% CI = 0.08-0.20) had lower odds of genetic testing interest. Our results are consistent with some prior research utilizing proxy indicators for socioeconomic status, but our research adds the importance of using a multidimensional indicator such as SSS to examine cancer and genetic testing predictor outcomes. CONCLUSION: To develop interventions to improve genetic knowledge, researchers should consider the social status and contexts of women diagnosed with breast cancer at a young age (or before 40 years old) to ensure equity in the distribution of genetic testing benefits.

Racial and Ethnic Composition of Departments of Health Policy & Management and Health Education & Behavioral Sciences.

The diversity of racial/ethnic representation in the health services and policy research (HSPR) workforce plays a crucial role in addressing the health needs of underserved populations. We assessed changes (between 2012 and 2022) in the racial/ethnic composition of students and faculty from departments of Health Policy & Management (HPM) and Health Education & Behavioral Sciences (HEBS) among the Association of Schools and Programs of Public Health member institutions. We analyzed annual data from over 40 institutions that reported student and faculty data in 2012 and 2022 within each department. Racial/ethnic populations included American Indian/Alaska Native (AI/AN), Asian, Hispanic, Native Hawaiian/Pacific Islander (NH/PI), Black, White, Unknown, and Multiracial. We conducted analyses by department and examined racial/ethnic composition by student status, degree level, faculty rank, and tenure status. We found statistically significant increases in Black assistant professors (HPM and HEBS) and tenured faculty (HPM), Hispanic graduates and tenure-track faculty (HPM), Asian professors (HPM: full and tenured, HEBS: associate and tenured), and Multiracial students and graduates (HPM and HEBS). Statistically significant decreases were observed in White professors (HPM: assistant and full, HEBS: all ranks) and tenure-track faculty (HPM and HEBS), AI/AN associate professors and tenured faculty (HEBS), Hispanic associate professors (HPM), Asian assistant professors (HEBS), and NH/PI students (HPM and HEBS). Our findings highlight the importance of increasing racial/ethnic representation. Strategies to achieve this include facilitating workshops to raise awareness about the structural barriers encountered by Hispanic faculty, providing research support, evaluating promotion processes, establishing more pathway programs, and fostering interdisciplinary academic environments studying AI/AN or NH/PI populations.

Love after lockup: examining the role of marriage, social status, and financial stress among formerly incarcerated individuals.

BACKGROUND: Upon reintegration into society, formerly incarcerated individuals (FIIs) experience chronic financial stress due to prolonged unemployment, strained social relationships, and financial obligations. This study examined whether marriage and perceived social status can mitigate financial stress, which is deleterious to the well-being of FIIs. We also assessed whether sociodemographic factors influenced financial stress across marital status. We used cross-sectional data from 588 FIIs, collected in the 2023 Survey of Racism and Public Health. The financial stress outcome (Cronbach's [Formula: see text] = 0.86) comprised of five constructs: psychological distress, financial anxiety, job insecurity, life satisfaction, and financial well-being. Independent variables included marital and social status, age, race/ethnicity, gender identity, educational attainment, employment status, and number of dependents. Multivariable models tested whether financial stress levels differed by marital and perceived social status (individual and interaction effects). Stratified multivariable models assessed whether social status and sociodemographic associations varied by marital status. RESULTS: We found that being married/living with a partner (M/LWP, b = -5.2) or having higher social status (b = -2.4) were protective against financial stress. Additionally, the social status effect was more protective among divorced, separated, or widowed participants (b = -2.5) compared to never married (NM, b = -2.2) and M/LWP (b = -1.7) participants. Lower financial stress correlated with Black race and older age, with the age effect being more pronounced among M/LWP participants (b = -9.7) compared to NM participants (b = -7.3). Higher financial stress was associated with woman gender identity (overall sample b = 2.9, NM sample b = 5.1), higher education (M/LWP sample b = 4.4), and having two or more dependents (overall sample b = 2.3, M/LWP sample b = 3.4). CONCLUSIONS: We provide novel insights into the interrelationship between marriage, perceived social status, and financial stress among FIIs. Our findings indicate the need for policies and programs which may target the family unit, and not only the individual, to help alleviate the financial burden of FIIs. Finally, programs that offer legal aid to assist in expungement or sealing of criminal records or those offering opportunities for community volunteer work in exchange for vouchers specific to legal debt among FIIs could serve to reduce financial stress and improve social standing.

Enhanced family history-based algorithms increase the identification of individuals meeting criteria for genetic testing of hereditary cancer syndromes but would not reduce disparities on their own.

OBJECTIVE: This study aimed to 1) investigate algorithm enhancements for identifying patients eligible for genetic testing of hereditary cancer syndromes using family history data from electronic health records (EHRs); and 2) assess their impact on relative differences across sex, race, ethnicity, and language preference. MATERIALS AND METHODS: The study used EHR data from a tertiary academic medical center. A baseline rule-base algorithm, relying on structured family history data (structured data; SD), was enhanced using a natural language processing (NLP) component and a relaxed criteria algorithm (partial match [PM]). The identification rates and differences were analyzed considering sex, race, ethnicity, and language preference. RESULTS: Among 120,007 patients aged 25-60, detection rate differences were found across all groups using the SD (all P < 0.001). Both enhancements increased identification rates; NLP led to a 1.9 % increase and the relaxed criteria algorithm (PM) led to an 18.5 % increase (both P < 0.001). Combining SD with NLP and PM yielded a 20.4 % increase (P < 0.001). Similar increases were observed within subgroups. Relative differences persisted across most categories for the enhanced algorithms, with disproportionately higher identification of patients who are White, Female, non-Hispanic, and whose preferred language is English. CONCLUSION: Algorithm enhancements increased identification rates for patients eligible for genetic testing of hereditary cancer syndromes, regardless of sex, race, ethnicity, and language preference. However, differences in identification rates persisted, emphasizing the need for additional strategies to reduce disparities such as addressing underlying biases in EHR family health information and selectively applying algorithm enhancements for disadvantaged populations. Systematic assessment of differences in algorithm performance across population subgroups should be incorporated into algorithm development processes.

Investigation of interest in and timing preference for cancer predisposition testing and expanded carrier screening among women of reproductive age.

Objective: To examine cognitive, relational, and social predictors of interest in and timing preference for cancer predisposition testing (CPT) and expanded carrier screening (ECS) offered in routine gynecologic care for women of reproductive age. Methods: Women between 20 and 35 years old who were currently pregnant or had a prior pregnancy (N = 351) completed an online survey. Bivariate and multivariable analyses were used to identify significant predictors of women's interest in and timing preference for CPT and ECS. Results: Most respondents reported high interest in CPT and ECS and preferred to have them when planning for a pregnancy. Perceived importance of genetic information and negative attitude towards uncertainty predicted interest in CPT and ECS in multivariable models. Genetic knowledge predicted preference for CPT or ECS when planning for a pregnancy. Conclusion: Educational and decision support tools should be developed to enhance women's knowledge and awareness of CPT and ECS and to provide them with strategies to manage uncertainty. Innovation: We examined women's timing preference for CPT and ECS and the impact of partner support and trust with gynecologist. A context-specific attitudes toward uncertainty scale was used to investigate women's particular perceptions of uncertainty in genetic testing.

The measurement of racism in health inequities research.

There is limited literature on the measures and metrics used to examine racism in the health inequities literature. Health inequities research is continuously evolving, with the number of publications increasing over time. However, there is limited knowledge on the best measures and methods to examine the impact of different levels of racism (institutionalized, personally mediated, and internalized) on health inequities. Advanced statistical methods have the potential to be used in new ways to examine the relationship between racism and health inequities. In this review, we conduct a descriptive examination of the measurement of racism in the health inequities epidemiologic literature. We examine the study design, methods used for analysis, types of measures used (e.g., composite, absolute, relative), number of measures used, phase of research (detect, understand, solutions), viewpoint (oppressor, oppressed), and components of structural racism measures (historical context, geographical context, multifaceted nature). We discuss methods (e.g., Peters-Belson, latent class analysis, difference in differences) that have demonstrated potential for future work. The articles reviewed were limited to the detect (25%) and understand (75%) phases, with no studies in the solutions phase. Although the majority (56%) of studies had cross-sectional designs, many authors pointed to the need for longitudinal and multilevel data for further exploration. We examined study design features as mutually exclusive elements. However, racism is a multifaceted system and the measurement of racism in many studies does not fit into a single category. As the literature grows, the significance of methodological and measurement triangulation to assess racism should be investigated.