Disparities and Trends in Routine Adult Vaccination Rates Among Disaggregated Asian American Subgroups, National Health Interview Survey 2006-2018.

Introduction: Vaccination rates may be improved through culturally tailored messages, but little is known about them among disaggregated Asian American subgroups. We assessed the vaccination rates for key vaccines among these subgroups. Methods: Using the National Health Interview Survey, we analyzed recent vaccination rates (2015-2018, n=188,250) and trends (2006-2018) among Asians (Chinese [n=3,165], Asian Indian [n=3,525], Filipino [n=3,656], other Asian [n=5,819]) and non-Hispanic White adults (n=172,085) for 6 vaccines (the human papillomavirus, hepatitis B, pneumococcal, influenza, tetanus-diphtheria [tetanus], and shingles vaccines). We controlled demographic, socioeconomic, and health-related variables in multivariable logistic regression and predicted marginal modeling analyses. We also computed vaccination rates among Asian American subgroups on the 2015-2018 National Health Interview Survey data stratified by foreign-born and U.S.-born status. We used Joinpoint regression to analyze trends in vaccination rates. All analyses were conducted in 2021 and 2022. Results: Among Asians, shingles (29.2%; 95% CI=26.6, 32.0), tetanus (53.7%; 95% CI=51.8, 55.6), and pneumococcal (53.8%; 95% CI=50.1, 57.4) vaccination rates were lower than among non-Hispanic Whites. Influenza (47.9%; 95% CI=46.2, 49.6) and hepatitis B (40.5%; 95% CI=39.0, 42.7) vaccination rates were similar or higher than among non-Hispanic Whites (48.4%; 95% CI=47.9, 48.9 and 30.7%; 95% CI=30.1, 31.3, respectively). Among Asians, we found substantial variations in vaccination rates and trends. For example, Asian Indian women had lower human papillomavirus vaccination rates (12.9%; 95% CI=9.1, 18.0) than all other Asian subgroups (Chinese: 37.9%; 95% CI=31.1, 45.2; Filipinos: 38.7%; 95% CI=29.9, 48.3; other Asians: 30.4%; 95% CI=24.8, 36.7) and non-Hispanic Whites (36.1%; 95% CI=34.8, 37.5). Being male, having lower educational attainment and income, having no health insurance or covered by public health insurance only, and lower frequency of doctor visits were generally associated with lower vaccine uptakes. Foreign-born Asian aggregate had lower vaccination rates than U.S.-born Asian aggregate for all vaccines except for influenza. We also found subgroup-level differences in vaccination rates between foreign-born and U.S.-born Asians. We found that (1) foreign-born Chinese, Asian Indians, and other Asians had lower human papillomavirus and hepatitis B vaccination rates; (2) foreign-born Chinese and Filipinos had lower pneumococcal vaccination rates; (3) foreign-born Chinese and Asian Indians had lower influenza vaccination rates; and (4) all foreign-born Asian subgroups had lower tetanus vaccination rates. Conclusions: Vaccination rates and trends differed among Asian American subgroups. Culturally tailored messaging and interventions may improve vaccine uptakes.

PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens.

The peripheral T cell repertoire of healthy individuals contains self-reactive T cells1,2. Checkpoint receptors such as PD-1 are thought to enable the induction of peripheral tolerance by deletion or anergy of self-reactive CD8 T cells3-10. However, this model is challenged by the high frequency of immune-related adverse events in patients with cancer who have been treated with checkpoint inhibitors11. Here we developed a mouse model in which skin-specific expression of T cell antigens in the epidermis caused local infiltration of antigen-specific CD8 T cells with an effector gene-expression profile. In this setting, PD-1 enabled the maintenance of skin tolerance by preventing tissue-infiltrating antigen-specific effector CD8 T cells from (1) acquiring a fully functional, pathogenic differentiation state, (2) secreting significant amounts of effector molecules, and (3) gaining access to epidermal antigen-expressing cells. In the absence of PD-1, epidermal antigen-expressing cells were eliminated by antigen-specific CD8 T cells, resulting in local pathology. Transcriptomic analysis of skin biopsies from two patients with cutaneous lichenoid immune-related adverse events showed the presence of clonally expanded effector CD8 T cells in both lesional and non-lesional skin. Thus, our data support a model of peripheral T cell tolerance in which PD-1 allows antigen-specific effector CD8 T cells to co-exist with antigen-expressing cells in tissues without immunopathology.

Is there any role of staging laparoscopy in pancreatic adenocarcinoma?

This is a letter to the editor on a study by Jambor et al. on the role of staging laparoscopy in identifying occult and distant metastases in pancreatic adenocarcinoma patients. In this study, inclusion of staging laparoscopy as an adjunct to computed tomography resulted in an absolute risk reduction of 12.5% for non-therapeutic laparotomy. The study found no correlation between the presence of occult and distant metastases, and serum CA 19-9 level, tumour size or location, which was in significant contrast to a number of other studies. This was likely due to the smaller sample size of the study and restriction to a single high-volume referral centre. It is also noted that staging laparoscopy cannot detect vascular invasion, lymph node involvement and deep hepatic metastases. The sensitivity of peritoneal lavage cytology in detecting occult metastases is low as well. Inclusion of biomarkers like peritoneal lavage tumour DNA may improve sensitivity. Hence, even as this study adds to the evidence supporting staging laparoscopy, further studies on improving the sensitivity of staging laparoscopy are warranted.

Toward precision sleep medicine: variations in sleep outcomes among disaggregated Asian Americans in the National Health Interview Survey (2006-2018).

STUDY OBJECTIVES: Asian Americans report higher rates of insufficient sleep than non-Hispanic Whites (NHWs). It is unclear how sleep outcomes differ among disaggregated Asian subgroups. METHODS: The National Health Interview Survey (2006-2018) was used to analyze self-reported sleep duration and quality measures for Asian American subgroups (Chinese [n = 11,056], Asian Indian [n = 11,249], Filipino [n = 13,211], and other Asians [n = 21,767]). Outcomes included hours of sleep per day, the number of days reporting trouble falling asleep, staying asleep, waking up rested, and taking sleep medication in the past week. Subsetted multivariate logistic regression was used to assess factors impacting sleep outcomes by ethnicity. RESULTS: 29.2% of NHWs, 26.4% of Chinese, 24.5% of Asian Indians, and 38.4% of Filipinos reported insufficient sleep duration. Filipinos were less likely to report sufficient sleep duration (odds ratio 0.55, [confidence interval 95% 0.50-0.59]) and more likely to report trouble falling asleep (1.16 [1.01-1.33]) than NHWs. Chinese and Asian Indians had less trouble staying asleep (0.67 [0.58-0.77], 0.51 [0.44-0.59]) and falling asleep (0.77 [0.66-0.89], 0.72, [0.62-0.82]) than NHWs, and Asian Indians were more likely to wake feeling well rested (1.66 [1.48-1.87]). All Asian subgroups were less likely to report using sleep medications than NHWs. Foreign-born status had a negative association with sufficient sleep duration in Filipinos but a positive association in Asian Indians and Chinese. CONCLUSIONS: Filipinos report the highest burden of poor sleep outcomes, and Asian Indians report significantly better sleep outcomes. These findings highlight the importance of disaggregating Asian ethnic subgroups to address their health needs. CITATION: Wang RZ, Jamal A, Wang Z, et al. Toward precision sleep medicine: variations in sleep outcomes among disaggregated Asian Americans in the National Health Interview Survey (2006-2018). J Clin Sleep Med. 2023;19(7):1259-1270.

Association of Acculturation with Cardiovascular Risk Factors in Asian-American Subgroups.

Objective: This cross-sectional study aims to better understand the heterogeneous associations of acculturation level on CV risk factors among disaggregated Asian subgroups. We hypothesize that the association between acculturation level and CV risk factors will differ significantly by Asian subgroup. Methods: We used the National Health Interview Survey (NHIS), a nationally representative US survey, years 2014-18. Acculturation was defined using: (a) years in the US, (b) US citizenship status, and (c) level of English proficiency. We created an acculturation index, categorized into low vs. high (scores of 0-3 and 4, respectively). Self-reported CV risk factors included diabetes, high cholesterol, hypertension, obesity, tobacco use, and sufficient physical activity. Rao-Scott Chi Square was used to compare age-standardized, weighted prevalence of CV risk factors between Asian subgroups. We used logistic regression analysis to assess associations between acculturation and CV risk factors, stratified by Asian subgroup. Results: The study sample consisted of 6,051 adults ≥ 18 years of age (53.9% female; mean age 46.6 [SE 0.33]). The distribution by race/ethnicity was Asian Indian 26.9%, Chinese 22.8%, Filipino 18.1%, and other Asian 32.3%. The association between acculturation and CV risk factors differed by Asian subgroups. From multivariable adjusted models, high vs. low acculturation was associated with: high cholesterol amongst Asian Indian (OR=1.57, 95% CI: 1.11, 2.37) and other Asian (OR=1.48, 95% CI: 1.10, 2.01) adults, obesity amongst Filipino adults (OR= 1.62, 95% CI: 1.07, 2.45), and sufficient physical activity amongst Chinese (OR= 1.54, 95% CI: 1.09, 2.19) and Filipino adults (OR=1.58, 95% CI: 1.10, 2.27). Conclusion: This study demonstrates that acculturation is heterogeneously associated with higher prevalence of CV risk factors among Asian subgroups. More studies are needed to better understand these differences that can help to inform targeted, culturally specific interventions.

A Specialized Acute COVID-19 Outpatient Clinic at an Academic Medical Center.

Health systems are challenged to provide equitable access to coronavirus disease 2019 (COVID-19) outpatient care during the pandemic. Infected patients may have difficulties accessing regular care and rely on emergency rooms. With the goal to improve system efficiencies and access to care, Stanford launched a designated outpatient COVID-19 "Care and Respiratory Observation of Patients With Novel Coronavirus" clinic in April 2020 in which all adult Stanford patients with newly diagnosed severe acute respiratory syndrome coronavirus 2 were offered follow-up for 2-3 weeks through video, telephone, and in-person encounters. Patients were triaged into risk categories and received home pulse oximeters based on a standardized protocol. Between April 15, 2020, and March 26, 2021, the Care and Respiratory Observation of Patients With Novel Coronavirus clinic enrolled 1317 patients. The clinic provided evaluation of Patients under Investigation, management of acute COVID-19 symptoms, care for COVID-19 patients after hospital discharge, clinical advice, and opportunities for research. The authors share crucial implementation lessons related to team agility, care personalization, and resource optimization.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

The durability of immunity against reinfection by SARS-CoV-2: a comparative evolutionary study.

BACKGROUND: Among the most consequential unknowns of the devastating COVID-19 pandemic are the durability of immunity and time to likely reinfection. There are limited direct data on SARS-CoV-2 long-term immune responses and reinfection. The aim of this study is to use data on the durability of immunity among evolutionarily close coronavirus relatives of SARS-CoV-2 to estimate times to reinfection by a comparative evolutionary analysis of related viruses SARS-CoV, MERS-CoV, human coronavirus (HCoV)-229E, HCoV-OC43, and HCoV-NL63. METHODS: We conducted phylogenetic analyses of the S, M, and ORF1b genes to reconstruct a maximum-likelihood molecular phylogeny of human-infecting coronaviruses. This phylogeny enabled comparative analyses of peak-normalised nucleocapsid protein, spike protein, and whole-virus lysate IgG antibody optical density levels, in conjunction with reinfection data on endemic human-infecting coronaviruses. We performed ancestral and descendent states analyses to estimate the expected declines in antibody levels over time, the probabilities of reinfection based on antibody level, and the anticipated times to reinfection after recovery under conditions of endemic transmission for SARS-CoV-2, as well as the other human-infecting coronaviruses. FINDINGS: We obtained antibody optical density data for six human-infecting coronaviruses, extending from 128 days to 28 years after infection between 1984 and 2020. These data provided a means to estimate profiles of the typical antibody decline and probabilities of reinfection over time under endemic conditions. Reinfection by SARS-CoV-2 under endemic conditions would likely occur between 3 months and 5·1 years after peak antibody response, with a median of 16 months. This protection is less than half the duration revealed for the endemic coronaviruses circulating among humans (5-95% quantiles 15 months to 10 years for HCoV-OC43, 31 months to 12 years for HCoV-NL63, and 16 months to 12 years for HCoV-229E). For SARS-CoV, the 5-95% quantiles were 4 months to 6 years, whereas the 95% quantiles for MERS-CoV were inconsistent by dataset. INTERPRETATION: The timeframe for reinfection is fundamental to numerous aspects of public health decision making. As the COVID-19 pandemic continues, reinfection is likely to become increasingly common. Maintaining public health measures that curb transmission-including among individuals who were previously infected with SARS-CoV-2-coupled with persistent efforts to accelerate vaccination worldwide is critical to the prevention of COVID-19 morbidity and mortality. FUNDING: US National Science Foundation.

A robust and interpretable end-to-end deep learning model for cytometry data.

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).