OBJECTIVES/GOALS: Missed appointments (MAs) negatively impact the health outcomes of adults living with type 2 diabetes mellitus (T2DM), causing disruptions in clinic operation and added financial cost to healthcare providers and systems. This study aimed to identify risk factors for MAs in both in-person and telehealth settings among adults living with T2DM. METHODS/STUDY POPULATION: Using a sequential multi-method design guided by the modified Quality-Caring Model, the quantitative phase of this study used electronic health records (EHR) data in Calendar Years 2019 and 2020 with 7,276 encounters made by 2,235 patients with T2DM from four diabetes clinics within a tertiary academic medical center in Baltimore, MD. Multivariable random effect logistic regression were used to examine the association between MAs and included predictors (i.e., patient characteristics [e.g., age, race, health status], health provider factors [e.g., types of provider], and health system factors [e.g., scheduling lag]). Based on the results of the quantitative phase, a purposive sample of 23 adults with T2DM and 10 providers were then interviewed individually via phone or zoom. RESULTS/ANTICIPATED RESULTS: The EHR data found that the following variables decreased the odds of MAs: having an activated patient portal account, patients with age over 46 or with white race. Telehealth was associated with 50% decreased odds of MAs during COVID (after 3/23/2020). On the other hand, longer scheduling lag increased the odds of MAs. Qualitative interviews revealed that MAs were often related to social needs, such as lack of/limited health-related transportation and its associated financial burden. Telehealth helped break these barriers for some adults with T2DM, but technical challenges in telehealth persisted for those with low digital health literacy and people who did not have a digital device and/or with unstable internet connection. Providers worried that these challenges might undermine the quality of diabetes care. DISCUSSION/SIGNIFICANCE: Disparities in MAs by age and race were noted, which might reflect the impact of unmeasured social needs in EHR. Perceived convenient telehealth may reduce MAs in T2DM care. However, the persistent technical challenges of telehealth should be addressed to optimize the quality of diabetes care and to promote care continuity for underserved populations.

This paper presents a compilation of atmospheric radiocarbon for the period 1950–2019, derived from atmospheric CO2 sampling and tree rings from clean-air sites. Following the approach taken by Hua et al. (2013), our revised and extended compilation consists of zonal, hemispheric and global radiocarbon (14C) data sets, with monthly data sets for 5 zones (Northern Hemisphere zones 1, 2, and 3, and Southern Hemisphere zones 3 and 1–2). Our new compilation includes smooth curves for zonal data sets that are more suitable for dating applications than the previous approach based on simple averaging. Our new radiocarbon dataset is intended to help facilitate the use of atmospheric bomb 14C in carbon cycle studies and to accommodate increasing demand for accurate dating of recent (post-1950) terrestrial samples.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

In this article, we present the most significant results of the Monte Albán Geophysical Archaeology Project. Using ground-penetrating radar, gradiometry, and electrical resistance, we carried out a systematic survey of the site's Main Plaza to identify buried prehispanic features that might shed light on Monte Albán's early history. The most important discoveries include three buried structures dating between the Danibaan (500–300 BC) and Nisa phases (100 BC–AD 100). We argue that the largest structure, measuring 18 × 18 m, was probably a temple platform and that all three of the structures were razed and buried by the end of the Nisa phase at the latest. Furthermore, we contend that these events were part of a major renovation and expansion of the site's Main Plaza that occurred during a pivotal period of dramatic sociopolitical transformation in the Zapotec capital.

Background: Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.

Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).

Disclosures: Aaron Milstone, BD (consulting)

Making predictions about aliens is not an easy task. Most previous work has focused on extrapolating from empirical observations and mechanistic understanding of physics, chemistry and biology. Another approach is to utilize theory to make predictions that are not tied to details of Earth. Here we show how evolutionary theory can be used to make predictions about aliens. We argue that aliens will undergo natural selection – something that should not be taken for granted but that rests on firm theoretical grounds. Given aliens undergo natural selection we can say something about their evolution. In particular, we can say something about how complexity will arise in space. Complexity has increased on the Earth as a result of a handful of events, known as the major transitions in individuality. Major transitions occur when groups of individuals come together to form a new higher level of the individual, such as when single-celled organisms evolved into multicellular organisms. Both theory and empirical data suggest that extreme conditions are required for major transitions to occur. We suggest that major transitions are likely to be the route to complexity on other planets, and that we should expect them to have been favoured by similarly restrictive conditions. Thus, we can make specific predictions about the biological makeup of complex aliens.

The National Center for the Study of Preparedness and Catastrophic Event Response (PACER) has created a publicly available simulation tool called Surge (accessible at http://www.pacerapps.org) to estimate surge capacity for user-defined hospitals. Based on user input, a Monte Carlo simulation algorithm forecasts available hospital bed capacity over a 7-day period and iteratively assesses the ability to accommodate disaster patients. Currently, the tool can simulate bed capacity for acute mass casualty events (such as explosions) only and does not specifically simulate staff and supply inventory. Strategies to expand hospital capacity, such as (1) opening unlicensed beds, (2) canceling elective admissions, and (3) implementing reverse triage, can be interactively evaluated. In the present application of the tool, various response strategies were systematically investigated for 3 nationally representative hospital settings (large urban, midsize community, small rural). The simulation experiments estimated baseline surge capacity between 7% (large hospitals) and 22% (small hospitals) of staffed beds. Combining all response strategies simulated surge capacity between 30% and 40% of staffed beds. Response strategies were more impactful in the large urban hospital simulation owing to higher baseline occupancy and greater proportion of elective admissions. The publicly available Surge tool enables proactive assessment of hospital surge capacity to support improved decision-making for disaster response. (Disaster Med Public Health Preparedness. 2018;12:513–522)

Systems linking people and nature, known as social-ecological systems, are increasingly understood as complex adaptive systems. Essential features of these complex adaptive systems – such as nonlinear feedbacks, strategic interactions, individual and spatial heterogeneity, and varying time scales – pose substantial challenges for modeling. However, ignoring these characteristics can distort our picture of how these systems work, causing policies to be less effective or even counterproductive. In this paper we present recent developments in modeling social-ecological systems, illustrate some of these challenges with examples related to coral reefs and grasslands, and identify the implications for economic and policy analysis.