Individual- and Neighborhood-Level Disparities in Audio-Only Telemedicine Utilization Across a Large Academic Health System.

Introduction: The objective of this study was to understand whether use of audio-only telemedicine visits differed by individual- and neighborhood-level patient characteristics during the COVID-19 pandemic. Methods: We conducted a retrospective cross-sectional study of telemedicine encounter data from a large academic health system. The primary outcome was rate of audio-only versus video visits. The exposures of interest were individual- (age, race, insurance, preferred language) and neighborhood-level (Social Deprivation Index [SDI]) patient characteristics. Results: Our study included 1,054,465 patient encounters from January 1, 2020 to December 31, 2021, of which 18.33% were completed via audio-only. Encounters among adults 75 years or older, Black patients, Spanish-speakers, and those with public insurance were more frequently conducted by audio-only (p < 0.001). Overall, populations showed decreasing rates of audio-only visits over time. We also observed an increase in the rate of audio-only encounters as SDI scores increased. Discussion: We found that audio-only disparities exist in telemedicine utilization by individual and zip code level characteristics. Though these disparities have improved over time as seen by our temporal analysis, marginalized and minority groups still showed the lowest rates of video utilization. In conclusion, access to audio-only care is a critical component to ensure that telemedicine is accessible to all populations. State and federal policy should support continued reimbursement of audio-only care to ensure equitable access to care while the implications of different care modalities are further studied.

An invasive human commensal and a native marsupial maintain tick populations at the urban fringe.

Ticks (Acari: Ixodidae) are major disease vectors globally making it increasingly important to understand how altered vertebrate communities in urban areas shape tick population dynamics. In urban landscapes of Australia, little is known about which native and introduced small mammals maintain tick populations preventing host-targeted tick management and leading to human-wildlife conflict. Here, we determined (1) larval, nymphal, and adult tick burdens on host species and potential drivers, (2) the number of ticks supported by the different host populations, and (3) the proportion of medically significant tick species feeding on the different host species in Northern Sydney. We counted 3551 ticks on 241 mammals at 15 sites and found that long-nosed bandicoots (Perameles nasuta) hosted more ticks of all life stages than other small mammals but introduced black rats (Rattus rattus) were more abundant at most sites (33%-100%) and therefore important in supporting larval and nymphal ticks in our study areas. Black rats and bandicoots hosted a greater proportion of medically significant tick species including Ixodes holocyclus than other hosts. Our results show that an introduced human commensal contributes to maintaining urban tick populations and suggests ticks could be managed by controlling rat populations on urban fringes.

Partnerships, Lawsuits, and Competing Accountabilities in CCAA Agreements.

Private lands are often critical for successful species conservation, and the US Fish and Wildlife Service has increasingly utilized voluntary Candidate Conservation Agreements with Assurances (CCAAs) as a strategy for promoting private land conservation. CCAAs, however, present a challenge where the FWS, with its history as a regulatory entity, must now engage landowners as conservation partners. There is a deep culture of distrust among landowners, who are often suspicious of engaging with the agency, making it necessary for the FWS to build trusting relationships. Furthermore, FWS decisions often face litigation in the courts, where they may be overturned. This creates a challenge for CCAAs, as the agency is pulled between landowner demands for greater flexibility and a court system that emphasizes rigid compliance to established rules and procedures. This study seeks to understand what factors influenced the flexibility of agency staff and officials as they navigate the process of negotiating CCAAs amidst these competing demands for accountability. Three cases of CCAA development are presented, each aiming to protect the habitat for the greater sage-grouse and ease the regulatory burden on ranching communities, should the grouse become a federally protected species. In addition to the well-documented need for trust-building and maintenance, the findings of the study highlight the importance of shared goals, the participation of trusted intermediary organizations, and as well as the meaningful support and investment of senior FWS leadership in exploring creative, innovative solutions.

Genomic considerations for FHIR®; eMERGE implementation lessons.

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network's Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7® FHIR®), to represent clinical genomics results. These new standards improve the utility of HL7® FHIR® as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study.

BACKGROUND: Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. OBJECTIVE: This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. METHODS: This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. RESULTS: A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. CONCLUSIONS: Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Molecular identification of the Trypanosoma (Herpetosoma) lewisi clade in black rats (Rattus rattus) from Australia.

Invasive rodent species are known hosts for a diverse range of infectious microorganisms and have long been associated with the spread of disease globally. The present study describes molecular evidence for the presence of a Trypanosoma sp. from black rats (Rattus rattus) in northern Sydney, Australia. Sequences of the 18S ribosomal RNA (rRNA) locus were obtained in two out of eleven (18%) blood samples with subsequent phylogenetic analysis confirming the identity within the Trypanosoma lewisi clade.

Insulin regulates GLUT4 in the ventromedial hypothalamus to restore the sympathoadrenal response to hypoglycemia in diabetic rats.

It is proposed that the impaired counterregulatory response (CRR) to hypoglycemia in insulin-deficient diabetes may be due to chronic brain insulin deficiency. To test this hypothesis, streptozotocin-induced diabetic Sprague-Dawley rats were infused with insulin (3 mU/day) or artificial cerebrospinal fluid (aCSF) bilaterally into the ventromedial hypothalamus (VMH) for 2 wk and compared with nondiabetic rats. Rats underwent hyperinsulinemic (50 mU·kg-1·min-1)-hypoglycemic (~45 mg/dl) clamps. Diabetic rats demonstrated an impaired CRR to hypoglycemia, noted by a high glucose infusion rate and blunted epinephrine and glucagon responses. The defective sympathoadrenal response was restored by chronic infusion of insulin into the VMH. Diabetic rats had decreased VMH Akt phosphorylation and decreased VMH glucose transporter 4 (GLUT4) content, which was also restored by chronic infusion of insulin into the VMH. Separate experiments in nondiabetic rats in which GLUT4 translocation into the VMH was inhibited with an infusion of indinavir were notable for an impaired CRR to hypoglycemia, indicated by increased glucose infusion rate and diminished epinephrine and glucagon responses. Results suggest that, in this model of diabetes, VMH insulin deficiency impairs the sympathoadrenal response to hypoglycemia and that chronic infusion of insulin into the VMH is sufficient to normalize the sympathoadrenal response to hypoglycemia via restoration of GLUT4 expression in the VMH.

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.

BACKGROUND: Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being. OBJECTIVES: This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use. METHODS: The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use. RESULTS: The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes. CONCLUSION: SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

Objectives: Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time. Materials and Methods: Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication. Results: The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up. Discussion and Conclusion: Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Risk for Poor Post-Operative Quality of Life Among Wearable Use Subgroups in an All of Us Research Cohort.

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.

An Automated Strategy to Calculate Medication Regimen Complexity.

Understanding medication regimen complexity is important to understand what patients may benefit from pharmacist interventions. Medication Regimen Complexity Index (MRCI), a 65-item tool to quantify the complexity by incorporating the count, dosage form, frequency, and additional administration instructions of prescription medicines, provides a more nuanced way of assessing complexity. The goal of this study was to construct and validate a computational strategy to automate the calculation of MRCI. The performance of our strategy was evaluated by comparing our calculated MRCI values with gold-standard values, using correlation coefficients and population distributions. The results revealed satisfactory performance to calculate the sub-score of MRCI that includes dosage form and frequency (76 to 80% match with gold standard), and fair performance for sub-score related to additional direction (52% match with gold standard). Our automated strategy shows potential to help reduce the effort for manually calculating MRCI and highlights areas for future development efforts.

Will the Doctor "See" You Now? The Development and Implementation of a Targeted Telemedicine System for Primary Care.

Objectives: The coronavirus disease 2019 (COVID-19) pandemic led to a rapid adoption of telehealth. For underserved populations lacking internet access, telemedicine was accomplished by phone rather than an audio-video connection. The latter is presumed a more effective form and better approximation of an in-person visit. We sought to provide a telehealth platform to overcome barriers for underserved groups to hold video visits with their health care providers and evaluate differences between the two telehealth modalities as assessed by physicians and patients. Methods: We designed a simplified tablet solution for video visits and piloted its use among patients who otherwise would have been completing audio-only visits. Patients consented to participation and were randomized in a 1:1 fashion to continue with their scheduled phone visit (control) versus being shipped a tablet to facilitate a video visit (intervention). Participants and providers completed communication and satisfaction surveys. Results: Tablet and connectivity design features included removal of all functions but for the telemedicine program, LTE always-on wireless internet connectivity, absence of external equipment (cords chargers and keyboard), and no registration with a digital portal. In total, 18 patients were enrolled. Intervention patients with video-enabled devices compared to control patients agreed more strongly that they were satisfied with their visits (4.75/5 vs. 3.75/5, p = 0.02). Conclusion: The delivered simplified tablet solution for video visits holds promise to improve access to video visits for underserved groups. Strategies to facilitate patient acceptance of devices are needed to expand the scope and potential impact of this effort.

Feasibility of the Genetic Information Assistant Chatbot to Provide Genetic Education and Study Genetic Test Adoption Among Pancreatic Cancer Patients at Johns Hopkins Hospital.

Genetic testing is a valuable tool to guide care of pancreatic cancer patients, yet personal and family uncertainty about the benefits of genetic testing (i.e., decisional conflict) may lead to low adoption. Enabling patients to learn more about genetic testing before their scheduled appointments may help to address this decisional conflict problem. We completed a feasibility assessment of a chatbot to provide genetic education (GEd) with 60 pancreatic cancer patients and using the chatbot to deliver surveys to assess: (a) opinions about the GEd, and (b) decisional conflict about genetic testing. Findings demonstrate intervention and study feasibility with about 80% of participants engaging with the GEd chatbot, 71% of which completed at least one survey. Overall, participants appear to have favorable opinions of the chatbot-delivered education and thought it was helpful to decide about genetic testing. Furthermore, patients who chose to get genetic testing spent more time interacting with the chatbot. Findings will be used to improve chatbot design and to facilitate a well-powered future trial.

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

A pilot study to predict cardiac arrest in the pediatric intensive care unit.

BACKGROUND: Cardiac arrest is a leading cause of mortality prior to discharge for children admitted to the pediatric intensive care unit. To address this problem, we used machine learning to predict cardiac arrest up to three hours in advance. METHODS: Our data consists of 240 Hz ECG waveform data, 0.5 Hz physiological time series data, medications, and demographics from 1,145 patients in the pediatric intensive care unit at the Johns Hopkins Hospital, 15 of whom experienced a cardiac arrest. The data were divided into training, validating, and testing sets, and features were generated every five minutes. 23 heart rate variability (HRV) metrics were determined from ECG waveforms. 96 summary statistics were calculated for 12 vital signs, such as respiratory rate and blood pressure. Medications were classified into 42 therapeutic drug classes. Binary features were generated to indicate the administration of these different drugs. Next, six machine learning models were evaluated: logistic regression, support vector machine, random forest, XGBoost, LightGBM, and a soft voting ensemble. RESULTS: XGBoost performed the best, with 0.971 auROC, 0.797 auPRC, 99.5% sensitivity, and 69.6% specificity on an independent test set. CONCLUSION: We have created high-performing models that identify signatures of in-hospital cardiac arrest (IHCA) that may not be evident to clinicians. These signatures include a combination of heart rate variability metrics, vital signs data, and therapeutic drug classes. These machine learning models can predict IHCA up to three hours prior to onset with high performance, allowing clinicians to intervene earlier, improving patient outcomes.

Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images.

Purpose: The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion versus extorsion) and amount (physiologic versus pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for classifying vertical ocular misalignment; however, current methods are time-intensive with steep learning curves for frontline providers. Methods: We used a dataset (n = 276) of right eye fundus images. The disc-foveal angle was calculated using ImageJ to generate synthetic images via image rotation. Using synthetic datasets (n = 12,740 images per model) and transfer learning (the reuse of a pretrained deep learning model on a new task), we developed a binary classifier (intorsion versus extorsion) and a multiclass classifier (physiologic versus pathologic intorsion and extorsion). Model performance was evaluated on unseen synthetic and nonsynthetic data. Results: On the synthetic dataset, the binary classifier had an accuracy and area under the receiver operating characteristic curve (AUROC) of 0.92 and 0.98, respectively, whereas the multiclass classifier had an accuracy and AUROC of 0.77 and 0.94, respectively. The binary classifier generalized well on the nonsynthetic data (accuracy = 0.94; AUROC = 1.00). Conclusions: The direction of static ocular torsion can be detected from synthetic fundus images using deep learning methods, which is key to differentiate between vestibular misalignment (skew deviation) and ocular muscle misalignment (superior oblique palsies). Translational Relevance: Given the robust performance of our models on real fundus images, similar strategies can be adopted for deep learning research in rare neuro-ophthalmologic diseases with limited datasets.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study.

BACKGROUND: Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience. OBJECTIVE: The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19. METHODS: We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews. RESULTS: The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19-positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes. CONCLUSIONS: We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis.

OBJECTIVE: The study sought to develop and apply a framework that uses a clinical phenotyping tool to assess risk for recurrent preterm birth. MATERIALS AND METHODS: We extended an existing clinical phenotyping tool and applied a 4-step framework for our retrospective cohort study. The study was based on data collected in the Genomic and Proteomic Network for Preterm Birth Research Longitudinal Cohort Study (GPN-PBR LS). A total of 52 sociodemographic, clinical and obstetric history-related risk factors were selected for the analysis. Spontaneous and indicated delivery subtypes were analyzed both individually and in combination. Chi-square analysis and Kaplan-Meier estimate were used for univariate analysis. A Cox proportional hazards model was used for multivariable analysis. RESULTS: : A total of 428 women with a history of spontaneous preterm birth qualified for our analysis. The predictors of preterm delivery used in multivariable model were maternal age, maternal race, household income, marital status, previous caesarean section, number of previous deliveries, number of previous abortions, previous birth weight, cervical insufficiency, decidual hemorrhage, and placental dysfunction. The models stratified by delivery subtype performed better than the naïve model (concordance 0.76 for the spontaneous model, 0.87 for the indicated model, and 0.72 for the naïve model). DISCUSSION: The proposed 4-step framework is effective to analyze risk factors for recurrent preterm birth in a retrospective cohort and possesses practical features for future analyses with other data sources (eg, electronic health record data). CONCLUSIONS: We developed an analytical framework that utilizes a clinical phenotyping tool and performed a survival analysis to analyze risk for recurrent preterm birth.

Metatranscriptomic profiling reveals diverse tick-borne bacteria, protozoans and viruses in ticks and wildlife from Australia.

Tick-borne zoonoses are emerging globally due to changes in climate and land use. While the zoonotic threats associated with ticks are well studied elsewhere, in Australia, the diversity of potentially zoonotic agents carried by ticks and their significance to human and animal health is not sufficiently understood. To this end, we used untargeted metatranscriptomics to audit the prokaryotic, eukaryotic and viral biomes of questing ticks and wildlife blood samples from two urban and rural sites in New South Wales, Australia. Ixodes holocyclus and Haemaphysalis bancrofti were the main tick species collected, and blood samples from Rattus rattus, Rattus fuscipes, Perameles nasuta and Trichosurus vulpecula were also collected and screened for tick-borne microorganisms using metatranscriptomics followed by conventional targeted PCR to identify important microbial taxa to the species level. Our analyses identified 32 unique tick-borne taxa, including 10 novel putative species. Overall, a wide range of tick-borne microorganisms were found in questing ticks including haemoprotozoa such as Babesia, Theileria, Hepatozoon and Trypanosoma spp., bacteria such as Borrelia, Rickettsia, Ehrlichia, Neoehrlichia and Anaplasma spp., and numerous viral taxa including Reoviridiae (including two coltiviruses) and a novel Flaviviridae-like jingmenvirus. Of note, a novel hard tick-borne relapsing fever Borrelia sp. was identified in questing H. bancrofti ticks which is closely related to, but distinct from, cervid-associated Borrelia spp. found throughout Asia. Notably, all tick-borne microorganisms were phylogenetically unique compared to their relatives found outside Australia, and no foreign tick-borne human pathogens such as Borrelia burgdorferi s.l. or Babesia microti were found. This work adds to the growing literature demonstrating that Australian ticks harbour a unique and endemic microbial fauna, including potentially zoonotic agents which should be further studied to determine their relative risk to human and animal health.