Receipt of Diabetes Specialty Care and Management Services by Older Adults With Diabetes in the U.S., 2015-2019: An Analysis of Medicare Fee-for-Service Claims.

OBJECTIVE: We characterized the receipt of diabetes specialty care and management services among older adults with diabetes. RESEARCH DESIGN AND METHODS: Using a 20% random sample of fee-for-service Medicare beneficiaries aged ≥65 years, we analyzed cohorts of type 1 diabetes (T1D) or type 2 diabetes (T2D) with history of severe hypoglycemia (HoH), and all other T2D annually from 2015 to 2019. Outcomes were receipt of office-based endocrinology care, diabetes education, outpatient diabetes health services, excluding those provided in primary care, and any of the aforementioned services. RESULTS: In the T1D cohort, receipt of endocrinology care and any service increased from 25.9% and 29.2% in 2015 to 32.7% and 37.4% in 2019, respectively. In the T2D with HoH cohort, receipt of endocrinology care and any service was 13.9% and 16.4% in 2015, with minimal increases. Age, race/ethnicity, residential setting, and income were associated with receiving care. CONCLUSIONS: These findings suggest that many older adults may not receive specialty diabetes care and underscore health disparities.

Disparities in Maternal Health Visits Between Rural and Urban Communities in the United States, 2016-2018.

OBJECTIVE: The objective was to estimate the rural-urban differences in the receipt of prepregnancy, prenatal, and postpartum services. METHODS: The authors conducted a cross-sectional data analysis using data from the Pregnancy Risk Assessment and Monitoring System from 2016 to 2018 to analyze rural-urban differences in the receipt of medical visits and care content delivery during the prepregnancy year, as well as the prenatal and postpartum periods among birthing people in the US, using survey-weighted multivariable logistic regression models. RESULTS: Rural-dwelling birthing people were significantly less likely to attend a medical visit in the prepregnancy year or postpartum period, even when controlled for sociodemographic and clinical characteristics. Compared to their urban counterparts, they were also less likely to receive comprehensive screening and counseling in the prepregnancy and postpartum maternity phases. CONCLUSION: Efforts to ameliorate rural-urban differences in maternal care access and quality should explicitly adopt multilevel, systemic approaches to policy and program implementation and evaluation. Policymakers and practitioners should consider telehealth as a potential complementary tool to minimize gaps in quality of care which disproportionately impact rural-dwelling birthing people.

Mild Traumatic Brain Injury and Career Stage Associate with Visible Perivascular Spaces in Special Operations Forces Soldiers.

Mild traumatic brain injury (mTBI) and occupational blast exposure in military Service Members may lead to impaired brain waste clearance which increases neurological disease risk. Perivascular spaces (PVS) are a key part of the glymphatic system which supports brain waste clearance, preferentially during sleep. Visible PVS on clinical magnetic resonance imaging have been previously observed in patients with neurodegenerative diseases and animal neurotrauma models. The purpose of this study was to determine associations between PVS morphological characteristics, military career stage, and mTBI history in Special Operations Forces (SOF) Soldiers. Participants underwent T2-weighed neuroimaging to capture three-dimensional whole brain volumes. Segmentation was performed using a previously validated, multi-scale deep convolutional encoder-decoder neural network. Only PVS clusters within the white matter mask were quantified for analyses. Due to non-normal PVS metric distribution, non-parametric Mann-Whitney U tests were used to determine group differences in PVS outcomes. In total, 223 healthy SOF combat Soldiers (age = 33.1 ± 4.3yrs) were included, 217 reported career stage. Soldiers with mTBI history had greater PVS number (z = 2.51, P = 0.013) and PVS volume (z = 2.42, P = 0.016). In-career SOF combat Soldiers had greater PVS number (z = 2.56, P = 0.01) and PVS volume (z = 2.28, P = 0.02) compared to a baseline cohort. Mild TBI history is associated with increased PVS burden in SOF combat Soldiers that are clinically recovered from mTBI. This may indicate ongoing physiological changes that could lead to impaired waste clearance via the glymphatic system. Future studies should determine if PVS number and volume are meaningful neurobiological outcomes for neurodegenerative disease risk and if clinical interventions such as improving sleep can reduce PVS burden.

Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population.

INTRODUCTION: There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM). METHODS: Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information. RESULTS: Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources. CONCLUSIONS: Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.

Breast Imaging Fellowship Training in the United States: A National Survey of Fellowship Program Directors.

OBJECTIVE: To provide an updated characterization of breast imaging fellowship programs in the United States to identify opportunities for improvement and standardization. METHODS: An anonymous survey was e-mailed to program directors of breast imaging fellowship programs listed on the Society of Breast Imaging website. The survey was open from April 23, 2021, through May 27, 2021. The survey was deemed exempt by the IRB. RESULTS: Forty-seven of 80 (59%) program directors responded, of which 36/47 (77%) represented programs dedicated 100% to breast imaging, and 11/47 (23%) represented programs dedicated 50%-75% to breast imaging. Common elements to most programs include tumor boards (47/47, 100%), journal clubs (39/47, 83%), case-based teaching sessions (35/47, 74%), didactic lectures (40/47, 85%), and participation in radiology-pathology conferences (29/47, 62%). Mammography Quality and Standards Act audit training (22/47, 47%), mammography quality control training (22/47, 47%), and formal communication training (19/47, 40%) were less common. Most programs provide exposure to wire (42/47, 89%) and wire-free localization procedures (45/47, 96%), but exposure to contrast-enhanced mammography (13/47, 28%) and molecular breast imaging (4/47, 9%) was limited. A small majority of programs (25/47, 53%) do not require weekday call; however, more (31/47, 66%) have weekend call responsibilities. Many programs (29/47, 62%) offer at least 3 weeks of elective time, which may be clinical or nonclinical. CONCLUSION: Breast imaging fellowship programs vary in curricula, modality exposure, and academic policies. The results of this survey can help guide further efforts to standardize and optimize fellowship training.

Volumetric imaging of an intact organism by a distributed molecular network.

Lymphatic, nervous, and tumoral tissues, among others, exhibit physiology that emerges from three-dimensional interactions between genetically unique cells. A technology capable of volumetrically imaging transcriptomes, genotypes, and morphologies in a single de novo measurement would therefore provide a critical view into the biological complexity of living systems. Here we achieve this by extending DNA microscopy, an imaging modality that encodes a spatio-genetic map of a specimen via a massive distributed network of DNA molecules inside it, to three dimensions and multiple length scales in developing zebrafish embryos.

Statistically Adjusting for Wear Time in Randomized Trials of Continuous Glucose Monitors as a Complement to Intent-to-Treat and As-Treated Analyses: Application and Evaluation in Two Trials.

Background: Randomized trials of continuous glucose monitoring (CGM) often estimate treatment effects using standard intent-to-treat (ITT) analyses. We explored how adjusting for CGM-measured wear time could complement existing analyses by estimating the effect of receiving and using CGM 100% of the time. Methods: We analyzed data from two 6-month CGM trials spanning diverse ages, the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) and CGM Intervention in Teens and Young Adults with Type 1 Diabetes (CITY) Studies. To adjust the ITT estimates for CGM use, as measured by wear time, we used an instrumental variable (IV) approach with the treatment assignment as an instrument. Outcomes included (1) time in range ([TIR] 70-180 mg/dL), time below range ([TBR] ≤70 mg/dL), and time above range ([TAR] ≥250 mg/dL). We estimated outcomes based on CGM use in the last 28 days of the trial and the full trial. Findings: In the WISDM study, the wear time rates over the 28-day window and full trial period were 93.1% (standard deviation [SD]: 20.4) and 94.5% (SD: 11.9), respectively. In the CITY study, the wear time rates over the 28-day window and full trial period were 82.2% (SD: 26.5) and 83.1% (SD: 21.5), respectively. IV-based estimates for the effect of CGM on TIR, TBR, and TAR suggested greater improvements in glycemic management than the ITT counterparts. The magnitude of the differences was proportional to the level of wear time observed in the trials. Interpretation: In trials of CGM use, the effect of variable wear time is non-negligible. By providing adherence-adjusted estimates, the IV approach may have additional utility for individual clinical decision-making.

"Nothing is linear": Characterizing the determinants and dynamics of CGM use in older adults with type 1 diabetes.

AIMS: Continuous glucose monitoring (CGM) can reduce hypoglycemia in older adults with type 1 diabetes (T1D). We aimed to characterize factors that influence effective use in this age group. METHODS: Older adults with type T1D (age ≥ 65) and their caregivers participated in one of a series of parallel group model building workshops, a participatory approach to system dynamics involving drawing and scripted group activities. Data were synthesized in a qualitative model of the hypothesized system of factors producing distinct patterns of CGM use in older adults. The model was validated through virtual follow-up interviews. RESULTS: Data were collected from 33 participants (four patient-caregiver dyads, mean age 73.8 ± 4.4 years [range 66-85 years]; 16 % non-CGM users, 79 % pump users). The system model delineates drivers of CGM uptake, drivers of ongoing CGM use, and feedback loops that either reinforce or counteract future CGM use. Participants emphasized the importance of different sets of feedback loops at different points in the duration of CGM use. CONCLUSIONS: The holistic system model underscores that factors and feedback loops driving effective CGM use in older adults are both individualized and dynamic (e.g., changing over time), suggesting opportunities for staged and tailored age-specific education and support.

Food Insecurity, Missed Workdays, And Hospitalizations Among Working-Age US Adults With Diabetes.

Food insecurity is associated with poor clinical outcomes among adults with diabetes, but associations with nonclinical outcomes, such as missed work, have not been well characterized. Our objective was to assess the associations between food insecurity, health-related missed workdays, and overnight hospitalizations. We pooled National Health Interview Survey data from the period 2011-18 to analyze food insecurity among 13,116 US adults ages 18-65 who had diabetes. Experiencing food insecurity, compared with being food secure, was associated with increased odds of reporting any health-related missed workdays, more than twice the rate of health-related missed workdays, and increased odds of overnight hospitalization within the prior twelve months. There was no significant association between food insecurity and the number of nights spent hospitalized. These findings underscore the broad impacts of food insecurity on health and wellness for working-age adults with diabetes. When weighing the costs and benefits of proposed interventions to address food insecurity, policy makers should consider potential benefits related to productivity in addition to implications for health care use.

Modeling the impact of digital readiness in recruiting and sustaining underrepresented groups: Data from the All of Us research program.

The All of Us Research Program (All of Us or Program) is an ongoing longitudinal data collection operated by the National Institutes of Health (NIH). The Program aims to improve healthcare for all through the development of a biomedical research resource reflective of the diversity of the United States that includes Underrepresented in Biomedical Research (UBR) groups. Federally Qualified Health Centers (FQHCs) are a key recruitment stream of UBR participants, which are community based and provide primary care and preventive services in medically underserved areas. Over 90% of FQHC patients enrolled in All of Us to date are UBR. The COVID-19 pandemic caused a pause in All of Us activities. Re-starting the activities was a challenge, especially due to the digital divide faced by FQHC participants, and that most Program activities are primarily completed via web-based portal from a computer or a mobile device. This paper investigates the extent to which digital readiness impacted recruitment and sustainment of a pre-pandemic sample of 2,791 FQHC participants to the Program. Digital readiness was defined by access to home-based or other internet-accessing devices, and participants' comfort level using such devices. Results from multivariable logistic regression models showed that lower age, more education, female gender identity, and higher income were associated with higher digital readiness (p ≤ 0.01). Race, rurality, and sexual orientation status were not significant factors associated with digital readiness. Older participants had higher odds of completing Program activities, even though less digitally ready than their younger peers, as they often completed the activities during their in-person clinical visits. A subsequent weighted model demonstrated that FQHC participants who were digitally ready had 27% higher odds of completing Program activities than those not digitally ready. The data highlight the need for improved connectivity and sustainment between longitudinal data collection, research programs, and UBR participants, particularly among those facing the digital divide. Quantifying digital challenges provide operational insights for longitudinal data collection (All of Us, or others), and broadly, other aspects of digital medicine such as telehealth or patient portals by recognizing digital readiness of participants and patients, and the level of support required for success.

Spatial Associations Between Land Use and Infectious Disease: Zika Virus in Colombia.

Land use boundaries represent human-physical interfaces where risk of vector-borne disease transmission is elevated. Land development practices, coupled with rural and urban land fragmentation, increases the likelihood that immunologically naïve humans will encounter infectious vectors at land use interfaces. This research consolidated land use classes from the GLC-SHARE dataset; calculated landscape metrics in linear (edge) density, proportion abundance, and patch density; and derived the incidence rate ratios of the Zika virus occurrence in Colombia, South America during 2016. Negative binomial regression was used to evaluate vector-borne disease occurrence counts in relation to Population Density, Average Elevation, Per Capita Gross Domestic Product, and each of three landscape metrics. Each kilometer of border length per square kilometer of area increase in the linear density of the Cropland and Grassland classes is associated with an increase in Zika virus risk. These spatial associations inform a risk reduction approach to rural and urban morphology and land development that emphasizes simple and compact land use geometry that decreases habitat availability for mosquito vectors of Zika virus.

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.

DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction.

Analyzing the spatial organization of molecules in cells and tissues is a cornerstone of biological research and clinical practice. However, despite enormous progress in molecular profiling of cellular constituents, spatially mapping them remains a disjointed and specialized machinery-intensive process, relying on either light microscopy or direct physical registration. Here, we demonstrate DNA microscopy, a distinct imaging modality for scalable, optics-free mapping of relative biomolecule positions. In DNA microscopy of transcripts, transcript molecules are tagged in situ with randomized nucleotides, labeling each molecule uniquely. A second in situ reaction then amplifies the tagged molecules, concatenates the resulting copies, and adds new randomized nucleotides to uniquely label each concatenation event. An algorithm decodes molecular proximities from these concatenated sequences and infers physical images of the original transcripts at cellular resolution with precise sequence information. Because its imaging power derives entirely from diffusive molecular dynamics, DNA microscopy constitutes a chemically encoded microscopy system.

Genetic measurement of memory B-cell recall using antibody repertoire sequencing.

Annual influenza vaccinations aim to protect against seasonal infections, and vaccine strain compositions are updated every year. This protection is based on antibodies that are produced by either newly activated or memory B cells recalled from previous encounters with influenza vaccination or infection. The extent to which the B-cell repertoire responds to vaccination and recalls antibodies has so far not been analyzed at a genetic level--which is to say, at the level of antibody sequences. Here, we developed a consensus read sequencing approach that incorporates unique barcode labels on each starting RNA molecule. These labels allow one to combine multiple sequencing reads covering the same RNA molecule to reduce the error rate to a desired level, and they also enable accurate quantification of RNA and isotype levels. We validated this approach and analyzed the differential response of the antibody repertoire to live-attenuated or trivalent-inactivated influenza vaccination. Additionally, we analyzed the antibody repertoire in response to repeated yearly vaccinations with trivalent-inactivated influenza vaccination. We found antibody sequences that were present in both years, providing a direct genetic measurement of B-cell recall.

DNA targeting specificity of RNA-guided Cas9 nucleases.

The Streptococcus pyogenes Cas9 (SpCas9) nuclease can be efficiently targeted to genomic loci by means of single-guide RNAs (sgRNAs) to enable genome editing. Here, we characterize SpCas9 targeting specificity in human cells to inform the selection of target sites and avoid off-target effects. Our study evaluates >700 guide RNA variants and SpCas9-induced indel mutation levels at >100 predicted genomic off-target loci in 293T and 293FT cells. We find that SpCas9 tolerates mismatches between guide RNA and target DNA at different positions in a sequence-dependent manner, sensitive to the number, position and distribution of mismatches. We also show that SpCas9-mediated cleavage is unaffected by DNA methylation and that the dosage of SpCas9 and sgRNA can be titrated to minimize off-target modification. To facilitate mammalian genome engineering applications, we provide a web-based software tool to guide the selection and validation of target sequences as well as off-target analyses.

Correlation of gene expression and genome mutation in single B-cells.

High-throughput measurement of gene-expression and immune receptor repertoires have recently become powerful tools in the study of adaptive immune response. However, despite their now-widespread use, both tend to discard cell identity by treating cell populations in bulk, and therefore lose the correlation between genetic variability and gene-expression at the single cell level. In order to recover this information, we developed a method to simultaneously measure gene expression profiles and genome mutations in single cells. We applied this method by quantifying the relationships between gene expression and antibody mutation in ensembles of individual B-cells from immunized mice. The results reveal correlations reflecting the manner in which information propagates between a B-cell's antigen receptors, its gene expression, and its mutagenic machinery, and demonstrate the power of this approach to illuminate both heterogeneity and physiology in cell populations.

Lineage structure of the human antibody repertoire in response to influenza vaccination.

The human antibody repertoire is one of the most important defenses against infectious disease, and the development of vaccines has enabled the conferral of targeted protection to specific pathogens. However, there are many challenges to measuring and analyzing the immunoglobulin sequence repertoire, including that each B cell's genome encodes a distinct antibody sequence, that the antibody repertoire changes over time, and the high similarity between antibody sequences. We have addressed these challenges by using high-throughput long read sequencing to perform immunogenomic characterization of expressed human antibody repertoires in the context of influenza vaccination. Informatic analysis of 5 million antibody heavy chain sequences from healthy individuals allowed us to perform global characterizations of isotype distributions, determine the lineage structure of the repertoire, and measure age- and antigen-related mutational activity. Our analysis of the clonal structure and mutational distribution of individuals' repertoires shows that elderly subjects have a decreased number of lineages but an increased prevaccination mutation load in their repertoire and that some of these subjects have an oligoclonal character to their repertoire in which the diversity of the lineages is greatly reduced relative to younger subjects. We have thus shown that global analysis of the immune system's clonal structure provides direct insight into the effects of vaccination and provides a detailed molecular portrait of age-related effects.