Correlates of HIV Testing Among Asian Immigrant Female Sex Workers in New York City and Los Angeles County.

This analysis examined correlates of HIV testing among Asian immigrant female sex workers in massage parlors. We interviewed 69 Chinese and Korean immigrant women who provided sexual services in massage parlors in New York City or Los Angeles County (2014-2016). Multivariable logistic regression results showed that participants who were younger, have lived in the U.S. for a longer period of time, had greater English proficiency, perceived higher HIV risk, or were living with an intimate partner were more likely to have had an HIV test. Disclosing sex work to a close friend was also positively associated with HIV testing at p < .1. These correlates may reflect differential access to information, systems, and social networks that would facilitate HIV testing, highlighting the importance of reducing social isolation and increasing HIV education, especially for older women who have come to the U.S. more recently. As the literature has indicated that Asian immigrant female sex workers experience high rates of intersectional stigma, efforts to mitigate these intersecting stigmas could further these objectives.

Evaluation of an artificial intelligence model for identification of mass effect and vasogenic edema on computed tomography of the head.

BACKGROUND AND PURPOSE: Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) to consensus neuroradiologist interpretations in detecting mass effect and vasogenic edema. MATERIALS AND METHODS: A retrospective standalone performance assessment was conducted on datasets of non-contrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients aged 18 years or older from five hospitals in the United States. The positive cases were selected consecutively based on the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up to three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the AI model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The AI model separately received thin (≤1.5mm) and/or thick (>1.5 and ≤5mm) axial series. RESULTS: The two cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The AI model identified mass effect with sensitivity 96.6% (95% CI, 94.9-98.2) and specificity 89.8% (95% CI, 84.7-94.2) for the thin series, and 95.3% (95% CI, 93.5-96.8) and 93.1% (95% CI, 89.1-96.6) for the thick series. It identified vasogenic edema with sensitivity 90.2% (95% CI, 82.0-96.7) and specificity 93.5% (95% CI, 88.9-97.2) for the thin series, and 90.0% (95% CI, 84.0-96.0) and 95.5% (95% CI, 92.5-98.0) for the thick series. The corresponding areas under the curve were at least 0.980. CONCLUSIONS: The assessed AI model accurately identified mass effect and vasogenic edema in this CT dataset. It could assist the clinical workflow by prioritizing interpretation of abnormal cases, which could benefit patients through earlier identification and subsequent treatment. ABBREVIATIONS: AI = artificial intelligence; AUC = area under the curve; CADt = computer assisted triage devices; FDA = Food and Drug Administration; NPV = negative predictive value; PPV = positive predictive value; SD = standard deviation.

Head CT deep learning model is highly accurate for early infarct estimation.

Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.

Factors Influencing Recovery and Well-Being Among Asian Survivors of International Criminal Sex Trafficking in an Urban U.S. City.

Sex trafficking is serious form of gender-based violence that results in profound adverse health outcomes, yet one that is poorly understood. New York City is a major hub for sex trafficking, with a significant but unquantified number of victims originating from East Asian countries and trafficked via illicit massage businesses. Peer-reviewed studies among Asian survivors of international criminal sex trafficking do not exist. The aim of this study is to qualitatively examine the factors at various levels of influence that impact the recovery and reintegration process of Asian criminal sex trafficking survivors in the United States from the perspective of survivors and front-line service providers. The study was guided by community-based participatory research and trauma-informed approaches, leveraging a collaboration with a well-established service provider organization. Ten in-depth interviews were conducted between 2018 and 2019 with three Korean survivors and seven key informants who were anti-trafficking service providers working with East Asian clients. Data were analyzed using a grounded theory approach. Survivors and service providers vocalized factors at multiple levels that either facilitate or impede recovery and well-being. Levels of influence included structural (e.g., poverty/debt bondage, immigration status, limited English proficiency), cultural (e.g., fatalism, collectivism), institutional (e.g., lack of culturally appropriate, trauma-informed care), interpersonal (e.g., exploitation, social support), and individual (e.g., resilience). Stigma was a crosscutting factor that spanned all levels of influence. This study highlights the voices of survivors and front-line service providers to understand the lives of an under-researched population of Asian sex trafficking survivors. Ultimately, the root, structural causes of survivor marginalization need to be addressed, which stem from the intersection of class-, gender-, and race-related inequities. While survivors continue to experience exploitation and marginalization post-trafficking, they also carry an enormous amount of resilience that must be leveraged in their path to recovery from trauma.

Biological Magnetic Resonance Data Bank.

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics. Advances including GHz-class NMR instruments, national and trans-national NMR cyberinfrastructure, hybrid structural biology methods and machine learning are driving increases in the amount, type, and applications of NMR data in the biosciences. BMRB is a Core Archive and member of the World-wide Protein Data Bank (wwPDB).

Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT.

Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or mass-containing and to assess algorithm performance. Materials and Methods This retrospective study included two groups of contrast-enhanced abdominal CT examinations (development data set and secondary test set). Adrenal glands in the development data set were manually segmented by radiologists. Images in both the development data set and the secondary test set were manually classified as normal or mass-containing. Deep learning segmentation and classification models were trained on the development data set and evaluated on both data sets. Segmentation performance was evaluated with use of the Dice similarity coefficient (DSC), and classification performance with use of sensitivity and specificity. Results The development data set contained 274 CT examinations (251 patients; median age, 61 years; 133 women), and the secondary test set contained 991 CT examinations (991 patients; median age, 62 years; 578 women). The median model DSC on the development test set was 0.80 (IQR, 0.78-0.89) for normal glands and 0.84 (IQR, 0.79-0.90) for adrenal masses. On the development reader set, the median interreader DSC was 0.89 (IQR, 0.78-0.93) for normal glands and 0.89 (IQR, 0.85-0.97) for adrenal masses. Interreader DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = .35). On the development test set, the model had a classification sensitivity of 83% (95% CI: 55, 95) and specificity of 89% (95% CI: 75, 96). On the secondary test set, the model had a classification sensitivity of 69% (95% CI: 58, 79) and specificity of 91% (95% CI: 90, 92). Conclusion A two-stage machine learning pipeline was able to segment the adrenal glands and differentiate normal adrenal glands from those containing masses. © RSNA, 2022 Online supplemental material is available for this article.

Idiopathic Granulomatous Lobular Mastitis: A Mimicker of Inflammatory Breast Cancer.

Background: Idiopathic granulomatous lobular mastitis (IGLM) is a rare, chronic inflammatory breast disease without a known etiology. Even though the current literature proposes several treatment strategies, there is no universal consensus for long-term management. Case Presentation: A 43-year-old White woman (gravida 5, para 4) presented with a 2-week history of right lower outer quadrant breast tenderness, heaviness, warmth, and redness. Mammography and ultrasound were concerning for inflammatory breast cancer. Biopsies returned as granulomatous mastitis without malignancy. After 8 months of unsuccessful therapy with prednisone and methotrexate, surgeons excised the breast tissue. Cultures and special stains were negative for other organisms. At the 7-month follow-up, no evidence of recurrence was seen. Conclusions: As there remains no consensus behind the etiology or management of IGLM, our case demonstrates a reasonable and successful stepwise treatment beginning with medical therapy before proceeding to surgical cure. Because of possible malignancy risk with chronic IGLM, patients should not delay surgical excision if their condition remains refractory to medical therapy alone.

Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs.

Importance: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. Objective: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. Design, Setting, and Participants: This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. Main Outcomes and Measures: The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points were the sensitivities and specificities for the detection of pneumothorax and tension pneumothorax. Results: The final analysis included radiographs from 985 patients (mean [SD] age, 60.8 [19.0] years; 436 [44.3%] female patients), including 307 patients with nontension pneumothorax, 128 patients with tension pneumothorax, and 550 patients without pneumothorax. The AI model detected any pneumothorax with an AUC of 0.979 (95% CI, 0.970-0.987), sensitivity of 94.3% (95% CI, 92.0%-96.3%), and specificity of 92.0% (95% CI, 89.6%-94.2%) and tension pneumothorax with an AUC of 0.987 (95% CI, 0.980-0.992), sensitivity of 94.5% (95% CI, 90.6%-97.7%), and specificity of 95.3% (95% CI, 93.9%-96.6%). Conclusions and Relevance: These findings suggest that the assessed AI model accurately detected pneumothorax and tension pneumothorax in this chest radiograph data set. The model's use in the clinical workflow could lead to earlier identification and improved care for patients with pneumothorax.

Reporting Coronary Artery Calcium on Low-Dose Computed Tomography Impacts Statin Management in a Lung Cancer Screening Population.

Background: Cigarette smoking is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). Concomitant use of low-dose computed tomography (LDCT) for coronary artery calcium (CAC) scoring with lung cancer screening (LCS) has been proposed to further determine ASCVD risk and mortality. We aimed to determine the validity of LDCT in identifying CAC and its impact on statin management. Methods: We conducted a retrospective review from November 2020 to May 2021 of Military Health System (MHS) beneficiaries who received LCS with LDCT and were referred for CAC scoring with electrocardiogram-gated CT. Of the 190 participants initially identified, 170 met study eligibility. The Agatston method was used to score CAC on both scan types. Results: Participants had a mean (SD) age of 62.1 (4.6) years and were 70.6% male. CAC was seen more on ECG-gated CT compared with LDCT (88% vs 74%, P < .001). The Spearman correlation and Kendall W coefficient of concordance of CAC scores between the 2 scan types was 0.945 (P < .001) and 0.643, respectively. The κ statistic between CAC scores on the 2 different scans was 0.49 (SEκ = 0.048; 95% CI, -0.726-1.706), and the weighted κ statistic was 0.711. Bland-Altman analysis demonstrated a mean bias of 111.45 Agatston units, with limits of agreement between -268.64 and 491.54, suggesting CAC scores on electrocardiogram-gated CT were on average about 111 units higher than those on LDCT. There was a statistically significant proportion of nonstatin participants who met statin criteria based on additional CAC reporting (P < .001). Conclusions: CAC scores are highly correlated and concordant between LDCT and electrocardiogram-gated CT. Smokers undergoing annual LDCT may benefit from concomitant CAC scoring to help stratify ASCVD risk.

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.

Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies from Massachusetts General Hospital (MGH; Boston, USA). All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training set. We then tested the model on studies from three clinical scenarios: consecutive stroke team activations for 6-months at MGH, consecutive stroke team activations for 6-months at a hospital that did not provide training data (Brigham and Women's Hospital [BWH]; Boston, USA), and an international site (Diagnósticos da América SA [DASA]; Brazil). The model results were compared to radiologist ground truth interpretations. The model performed better when trained on classification and segmentation annotations (area under the receiver operating curve [AUROC] 0.995 [95% CI 0.992-0.998] and median Dice coefficient for segmentation overlap of 0.797 [IQR 0.642-0.861]) compared to segmentation annotations alone (AUROC 0.982 [95% CI 0.972-0.990] and Dice coefficient 0.776 [IQR 0.584-0.857]). The model accurately identified infarcts for MGH stroke team activations (AUROC 0.964 [95% CI 0.943-0.982], 381 studies), BWH stroke team activations (AUROC 0.981 [95% CI 0.966-0.993], 247 studies), and at DASA (AUROC 0.998 [95% CI 0.993-1.000], 171 studies). The model accurately segmented infarcts with Pearson correlation comparing model output and ground truth volumes between 0.968 and 0.986 for the three scenarios. Acute infarct can be accurately detected and segmented on MRI in real-world clinical scenarios using a machine learning model.

Bacteroides Fragilis Vertebral Osteomyelitis and Discitis: "Back" to Susceptibility Testing.

Genetic testing of anaerobic isolates can be important for proper antimicrobial stewardship to identify the appropriate narrow-spectrum treatment for a polymicrobial infection.

Where and why do illicit businesses cluster? Comparing sexually oriented massage parlors in Los Angeles County and New York City.

Immigrant-run sexually oriented massage parlors embody the intersection of important planning issues, including inequitable distribution of controversial land uses and economic functions of illicit businesses. We analyzed geocoded data from a ratings website to examine sexually oriented massage parlor clustering in Los Angeles County (LAC) and New York City (NYC). In LAC, clustering occurred in areas with more Asian and Hispanic residents. In NYC, clustering occurred mostly in Manhattan and was negatively associated with household size. Local regulation did not appear to affect clustering. Study findings hold lessons about both more effective regulation and enabling economic development in immigrant populations.

Limited Use of Outpatient Stress Testing in Young Patients With Atypical Chest Pain.

Low prevalence of coronary artery disease within this population suggests that younger patients may not require stress testing for chest pain evaluations as long as pretest likelihood is low.

Repeat Hospitalizations Predict Mortality in Patients With Heart Failure.

INTRODUCTION: Heart failure (HF) affects more than 5.1 million Americans and is projected to increase. Understanding the relationship between hospitalization and mortality can help to guide clinical management. The aim of the study is to evaluate the impact of repeat HF hospitalizations on all-cause mortality and to determine risk variables related to patient mortality. MATERIALS AND METHODS: Using administrative data from the Military Health System, a cohort of patients with an index admission for HF between 2007 and 2011 was identified. HF hospitalizations were defined as any hospital claim with an International Classification of Diseases, Ninth Revision diagnosis of 428.xx in the primary diagnosis field over the 7-year study period (2007-2013). Patients were subsequently categorized based on total number of HF hospitalizations. A multivariate Cox regression model, adjusting for age, sex, and comorbidities, was used to estimate hazard ratios. Kaplan-Meier survival curves were constructed based on the frequency of HF hospitalizations. RESULTS: Of the 51,286 patients admitted for HF, 54.7% were male with a mean (SD) age of 76.3 (10.8) years, and 29,714 died during 135,211 person-years of follow-up. Mean survival time was 2.6, 1.8, 1.5, and 1.3 years after the first, second, third, and fourth hospitalization, respectively. The mortality rate of patients at 30 days and 1 year postindex HF hospitalization was 7.4% and 27.3%, respectively. A history of dementia and chronic kidney disease without dialysis decreased overall survival. CONCLUSIONS: Repeat HF hospitalizations remain a strong predictor of mortality for existing patients with HF. As a result, clinicians and patients can individualize the optimal treatment strategy and resources on the basis of the suspected prognosis.

Embedded Mental Health: Promotion of Psychological Hygiene Within a Submarine Squadron.

INTRODUCTION: Psychological fitness is an important component to operational unit readiness and success. Embedding behavioral health providers can reduce unplanned personnel losses (UPL) as a result of psychological stress. The U.S. Submarine Forces implemented the Submarine Squadron 6 (CSS-6) Embedded Mental Health Pilot (EMHP) Program to address this type of UPL, which is classified as a Code 2 loss. The aim of the study is to evaluate the effectiveness of the EMHP Program at reducing UPL by improving psychological readiness through expedited access to care. MATERIALS AND METHODS: Using data from the CSS-6 EMHP Program, we identified the cohort of patients who were evaluated and received a full course of treatment from August 1, 2013, to April 30, 2015, and examined their final dispositions. A comparative review of Code 2 losses between 2012 and 2014 was performed to assess for any reduction in the annual incidence of Code 2 losses with EMHP. The Outcome Questionnaire (OQ-45) survey was used to determine the quantitative impact of EMHP on patient psychological readiness. We performed multiple regression analysis to identify any significant correlation between all independent variables and improvement in final OQ-45 scores. We performed logistic regression analysis to assess the logistic score as a function of predicting patient probability of returning vice not returning to duty. The logistic score is a by-product of the end results data and was not an original metric when the program was started. The Clinical Investigations Department at Naval Medical Center, Portsmouth waived this study from institutional review board review. Authorization was obtained from the U.S. Submarine Forces Command Public Affairs Office to publish the contents of this study. RESULTS: EMHP providers conducted a total of 878 patient sessions for 183 sailors over a 21-month period. There were eight fewer Code 2 losses after 2014, the first full calendar year with EMHP. This decrease in the number of Code 2 losses was in fact statistically significant, given p < 0.001. EMHP providers saw a 200% increase in patient volume and contributed to a 12% decrease in the annual incidence of Code 2 losses in 2014. Seventy patients returning to duty demonstrated clinically and statistically significant improvements in OQ-45 scores at the end of treatment. Only the initial symptomatic distress score on the OQ-45 survey demonstrated any statistical significance of predicting an improvement in OQ-45 composite scores by the end of treatment, given p < 0.01. A negative logistic score was significantly associated with not returning to duty (odds ratio, 16.0; 95% confidence interval, 5.22-49.02; χ2 = 30.63; p < 0.001). CONCLUSION: The EMHP Program reduced Code 2 losses and positively promoted psychological hygiene for submariners. With the establishment of embedded programs at other squadrons, we can develop a longitudinal study that provides a more inclusive assessment of this model. A future study may be warranted to evaluate the validity of the logistic score as a metric to determine further fitness for submarine duty.

An Ambivalent Embrace: Service Needs and Gaps for Asian Immigrants in New Destinations.

Asian immigrants to the U.S. are settling in "new destinations," but there has been little research on their health care and social service needs. Our analysis of Census data to identify cities with the fastest Asian immigrant population growth (1990-2000) yielded 33 smaller cities in 13 states. The cities ranged in population from 7,677 to 86,660; were spread across 13 states in the Northeast, South, and Midwest regions of the US; and varied widely demographically. Pilot surveys conducted in 2009 indicated that, although many residents had positive attitudes towards immigrants, many were also concerned about job competition and dilution of American culture. Respondents reported a number of immigrant-targeted services but also service gaps and intergroup violence. We characterize smaller new destination cities' mixed response to their fast-growing immigrant populations as an "ambivalent embrace." Service gaps may be related to small city size and relatively small Asian immigrant population size, despite rapid population growth. Funding shortages were also cited as obstacles to cities' responsiveness, suggesting the importance of state and federal government aid.

Involving immigrant religious organizations in HIV/AIDS prevention: The role of bonding and bridging social capital.

Immigrant religious organizations in the United States are uniquely positioned to address critical issues beyond religion because of their moral, social and cultural prominence in community life. Increasingly, religious organizations have taken on a leadership role around health issues such as decreasing HIV/AIDS stigma and misinformation. However, there are barriers for some religious leaders and organizations in adopting new health programs, especially if the issue is seen as controversial. Our study examines how social network structures among religious members influence organizational acceptance of new information or controversial ideas, like HIV/AIDS. Using social network analysis methods on data from 2841 contacts in 20 immigrant Chinese Buddhist temples and Christian churches in New York City, we tested whether an immigrant religious organization's likelihood of being involved in HIV/AIDS activities was associated with the presence of bonding or bridging social capital. These two forms of social capital have been found to mediate the levels of exposure and openness to new ideas. We found HIV/AIDS-involved religious organizations were more likely to have lower levels of bonding social capital as indicated by members having fewer ties and fewer demographic attributes in common. We also found HIV/AIDS-involved religious organizations were more likely to have higher levels of bridging social capital as indicated by members having significantly more ties to people outside of their organization. Our study highlights the importance of looking beyond religion type and leadership attributes to social network structures among members in order to better explain organization-level receptiveness to HIV/AIDS involvement.

The Health Experts onLine at Portsmouth (HELP) system: One-year review of adult and Pediatric Asynchronous Telehealth Consultations.

INTRODUCTION: The Health Experts onLine at Portsmouth teleconsultation system is designed to connect health providers in the Navy Medicine East Region to specialists at Naval Medical Center Portsmouth. METHODS: A review of the first year of the Health Experts onLine at Portsmouth system was performed. Data on each teleconsultation were extracted from the Health Experts onLine at Portsmouth system database and analyzed. RESULTS: From June 2014 to May 2015 there have been 585 teleconsultations. Providers stationed on 36 ships/submarines and at 28 remote military treatment facilities have utilized the Health Experts onLine at Portsmouth system. Over 280 specialists in 34 different specialties were consulted. The median time to first response from a specialist was 6 h and 8 min, with 75% of all consults being addressed within 24 h. Eighteen medevacs were recommended. Thirty-nine potential medevacs were prevented, and 100 potential civilian network deferrals were prevented, resulting in an estimated savings of over US$580,000. DISCUSSION: Based on the 1-year metrics, Health Experts onLine at Portsmouth has provided improved access and quality of care to service members and their families throughout the Navy Medicine East Region. It has helped avoid over US$580,000 in unnecessary cost burden. Further review at the 2-year time interval will demonstrate the continued growth and effectiveness of the Health Experts onLine at Portsmouth system.

Chinese Immigrant Religious Institutions' Variability in Views on Preventing Sexual Transmission of HIV.

OBJECTIVES: We examined Chinese immigrant religious institutions' views on teaching about preventing sexual transmission of HIV and the consistency of their views with public health best practices in HIV prevention. METHODS: We used 2009 to 2011 survey data from 712 members of 20 New York City-based Chinese immigrant religious institutions to analyze their views on (1) teaching adolescents about condoms, (2) discussing homosexuality nonjudgmentally, and (3) promoting abstinence until marriage. RESULTS: Religion type was a significant predictor of views in the 3 domains, with Evangelical Protestants in least agreement with public health best practices, Buddhists in most agreement, and mainline Protestants between them. Greater HIV knowledge was significantly associated with agreement with best practices in all 3 domains. The frequency of prayer, meditation, or chanting and the level of acculturation were significant predictors of views on teaching adolescents about condoms and promoting abstinence until marriage. CONCLUSIONS: The best practice messages about HIV prevention that Chinese immigrant religious institutions find acceptable vary according to religion type and several other key factors, including HIV knowledge; frequency of prayer, meditation, or chanting; and level of acculturation.