The Bynum-Standard 1-Year Algorithm
The Bynum-Standard 1-Year Algorithm is a decision-making framework designed to evaluate and optimize short-term strategies or actions over a one-year timeframe, prioritizing efficiency and measurable outcomes.
Explore our growing list of publicly available ADRD datasets and analytic tools.
These tools decrease time to functional accessibility of AD/ADRD healthcare administrative and survey data. There is no single “gold standard” approach to the identification of AD/ADRD (and relevant outcomes) using administrative and/or cognitive function measures.
The choice depends on tradeoffs in performance dependent on a variety of factors. The code and “finder files” you will find on our site are designed to help researchers identify AD/ADRD cohorts and relevant outcomes using a variety of current, validated algorithms.
Current datasets contain relevant cognitive and dementia-related outcomes for the National Health and Nutrition Examination Survey (NHANES), the National Hospital Ambulatory Care Survey (NHAMCS), the National Health Interview Survey (NHIS), Behavioral Risk Factor Surveillance System (BRFSS), and the Medical Expenditure Panel Survey (MEPS). The resources core is currently developing programming script for use on ResDAC (Medicare claims) data that can be used to identify ADRD.
The Bynum-Standard 1-Year Algorithm is a decision-making framework designed to evaluate and optimize short-term strategies or actions over a one-year timeframe, prioritizing efficiency and measurable outcomes.
This dataset reports 2019 Medicare claims, events, and beneficiary counts in hospitals, EDs, and SNFs, comparing those with and without ADRD. Data are available at the facility level in SAS, Stata, and CSV formats.
Using 2014–2019 NHAMCS data, this study identifies emergency visits where patients were diagnosed with or had ADRD. The datasets also include information on the reason for visit and sociodemographic characteristics.
Code and datasets for cognitive decline and Alzheimer’s research using NHANES data (2011–12, 2013–14). Includes standardized cognition variables from CERAD, Animal Fluency, and DSST tests, along with sociodemographic data, available in SAS, Stata, and CSV formats.
This project compiles twelve annual NHIS datasets (2007–2018) identifying respondents with self-reported memory issues, including sociodemographic and cognition variables. The repository provides detailed descriptions, data files, codebooks, and programming scripts.
This MEPS dataset (2015–2019) links dementia diagnoses to healthcare spending, demographics, and utilization, enabling national estimates with supporting files and scripts.
BRFSS data (2013–2018) on cognitive functioning and dementia caregiving, merged with demographics and state FIPS codes for state-level estimates. The repository includes datasets, codebooks, and programming scripts.
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