The Future of Public Health Informatics: Alternative Scenarios and Recommended Strategies

Background: In October 2013, the Public Health Informatics Institute (PHII) and Institute for Alternative Futures (IAF) convened a multidisciplinary group of experts to evaluate forces shaping public health informatics (PHI) in the United States, with the aim of identifying upcoming challenges and opportunities. The PHI workshop was funded by the Robert Wood Johnson Foundation as part of its larger strategic planning process for public health and primary care. Workshop Context: During the two-day workshop, nine experts from the public and private sectors analyzed and discussed the implications of four scenarios regarding the United States economy, health care system, information technology (IT) sector, and their potential impacts on public health in the next 10 years, by 2023. Workshop participants considered the potential role of the public health sector in addressing population health challenges in each scenario, and then identified specific informatics goals and strategies needed for the sector to succeed in this role. Recommendations and Conclusion: Participants developed recommendations for the public health informatics field and for public health overall in the coming decade. These included the need to rely more heavily on intersectoral collaborations across public and private sectors, to improve data infrastructure and workforce capacity at all levels of the public health enterprise, to expand the evidence base regarding effectiveness of informatics-based public health initiatives, and to communicate strategically with elected officials and other key stakeholders regarding the potential for informatics-based solutions to have an impact on population health

Influence of Jail Incarceration and Homelessness Patterns on Engagement in HIV Care and HIV Viral Suppression among New York City Adults Living with HIV/AIDS

Objectives Both homelessness and incarceration are associated with housing instability, which in turn can disrupt continuity of HIV medical care. Yet, their impacts have not been systematically assessed among people living with HIV/AIDS (PLWHA). Methods We studied a retrospective cohort of 1,698 New York City PLWHA with both jail incarceration and homelessness during 2001–05 to evaluate whether frequent transitions between jail incarceration and homelessness were associated with a lower likelihood of continuity of HIV care during a subsequent one-year follow-up period. Using matched jail, single-adult homeless shelter, and HIV registry data, we performed sequence analysis to identify trajectories of these events and assessed their influence on engagement in HIV care and HIV viral suppression via marginal structural modeling. Results Sequence analysis identified four trajectories; 72% of the cohort had sporadic experiences of both brief incarceration and homelessness, whereas others experienced more consistent incarceration or homelessness during early or late months. Trajectories were not associated with differential engagement in HIV care during follow-up. However, compared with PLWHA experiencing early bouts of homelessness and later minimal incarceration/homelessness events, we observed a lower prevalence of viral suppression among PLWHA with two other trajectories: those with sporadic, brief occurrences of incarceration/homelessness (0.67, 95% CI = 0.50,0.90) and those with extensive incarceration experiences (0.62, 95% CI = 0.43,0.88). Conclusions Housing instability due to frequent jail incarceration and homelessness or extensive incarceration may exert negative influences on viral suppression. Policies and services that support housing stability should be strengthened among incarcerated and sheltered PLWHA to reduce risk of adverse health conditions

Analyzing historical diagnosis code data from NIH N3C and RECOVER Programs using deep learning to determine risk factors for Long Covid

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C) to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48\%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis

Tumour-associated antigenic peptides are present in the HLA class I ligandome of cancer cell line derived extracellular vesicles

Funding: Breast Cancer Now (Grant Number(s): 2018JulPR1086), Wellcome Trust (GrantNumber(s): 105621/Z/14/Z), Melville Charitable Trust.The recent success of monoclonal antibody checkpoint inhibitor therapies that enhance the ability of CD8+ T cells to detect cancer-related antigenic peptides has refocused the need to fully understand the repertoire of peptides being presented to the immune system. Whilst the peptide ligandome presented by cell surface human leucocyte antigen class I (HLA-I) molecules on cancer cells has been studied extensively, the ligandome of extracellular vesicles (EVs) remains poorly defined. Here we report the HLA-I ligandome of both the cell surface and EVs from eight breast cancer cell lines (MCF7, MDA-MB-231, MDA-MB-361, MDA-MB-415, MDA-MB-453, HCC 1806, HCC 1395, and HCC 1954), and additionally the melanoma cell line ESTDAB-056 and the multiple myeloma line RPMI 8226. Utilising HLA-I immunoisolation and mass spectrometry, we detected a total of 6574 peptides from the cell surface and 2461 peptides from the EVs of the cell lines studied. Within the EV HLA-I ligandome, we identified 150 peptides derived from tumour associated antigenic proteins, of which 19 peptides have been shown to elicit T cell responses in previous studies. Our data thus shows the prevalence of clinically relevant tumour-associated antigenic peptides in the HLA-I ligandome presented on EV.Publisher PDFPeer reviewe