This dataset consists of comparisons of mortality and hospital discharge rates/diagnoses between Old Order Amish (OOA) living in Lancaster County, Pennsylvania, and non-Amish Caucasians. The Anabaptist Genealogy Database Version 5 (AGDB5) and the Framingham Heart Study (FHS) were utilized for the mortality statistics for each cohort, respectively. Additionally, OOA health-related data were collected from hospital discharge records from 4 Lancaster County hospitals while the National Hospital Discharge Survey (NHDS) was used for non-Amish Caucasians. For each discharge the principal diagnosis and up to 6 additional diagnoses were recorded using ICD-9-CM codes. The dataset consists of demographics, mortality statistics, hospital discharge and diagnoses data, as well as longevity and health-related comparison analyses between the two groups.

The COVID-19 pandemic severely affected blood collection activities due to social distancing requirements, deferral of blood drives, and other measures to prevent spread of infection. Medical facilities also experienced changes in blood usage patterns with the cancellation of elective surgeries. To evaluate fluctuating blood inventory levels, the University of Maryland Medical Center (UMMC) built an R-based workflow to facilitate rapid and repeatable analysis and visualization of blood usage data extracted from the Cerner Laboratory Information System (LIS). The generation of daily reports by blood product and hospital unit promoted informed decision-making with regard to changing ordering practices to avoid wastage. This dataset consists of 5 R Markdown workflow files and one README file. The workflows will continue to be updated in the GitHub repository as additional R Markdown documents are written to assist in identifying large users, optimize data cleaning and product identification, establish safe inventory levels based on historical and recent usage, and establish web‐based functionality via the shiny pack.

An age-stratified agent-based model of COVID-19 was used to simulate outbreaks in states within two U. S. regions. The northeastern region consisted of Connecticut, Massachusetts, Maine, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island and Vermont. The southern region consisted of Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia and West Virginia. The model was calibrated using reported incidence of COVID-19 in each state from October 1, 2020 to August 31, 2021. It then projected the number of infections, hospitalizations, and deaths that would be averted between September 2021 and the end of March 2022, if states increased their daily vaccination rate.

The Nationwide Readmissions Database is part of the Healthcare Cost and Utilization Project (HCUP) family of databases. The NRD is derived from the HCUP State Inpatient Databases (SID), and aims to provide nationally represenative data to support hospital readmission analyses. The NRD includes all-payer inpatient discharges from HCUP partner community hospitals in the SID which have verifiable patient linkage numbers. These synthetic linkage numbers allow analysts to track patients across hospital stays, while maintaining patient privacy. The NRD contains over 14 million discharge records per data year from about 85% of SID discharges from participating states. The 122 data elements in the NRD include diagnostic and procedure codes, and hospital characteristics. The data cannot be used to track readmissions across states or across data years or used for state-, facility-, or physician-level analyses.