The purpose of this page is to create publicly available analytic tools (e.g., programming script) that can 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. Therefore, we will develop and disseminate code (and “finder files”) designed to identify AD/ADRD cohorts and relevant outcomes using a variety of current, validated algorithms and post them here.
National Hospital Ambulatory Medical Care Survey (NHAMCS):
Six annual data files from 2014 to 2019 using the National Hospital Ambulatory Medical Care Survey (NHAMCS) to identify patient visits to hospital emergency departments where the patient was either diagnosed with Alzheimer’s Disease and Related Dementias (ADRD) during the visit using ICD-9-CM and ICD-10-CM codes or was otherwise identified as having ADRD. The datasets also include information on the reason for visit and sociodemographic characteristics.
DOI LINK: https://doi.org/10.3886/E170841V1
Six annual data files from 2014 to 2019 using the National Hospital Ambulatory Medical Care Survey (NHAMCS) to identify patient visits to hospital emergency departments where the patient was either diagnosed with Alzheimer’s Disease and Related Dementias (ADRD) during the visit using ICD-9-CM and ICD-10-CM codes or was otherwise identified as having ADRD. The datasets also include information on the reason for visit and sociodemographic characteristics.
DOI LINK: https://doi.org/10.3886/E170841V1
National Health and Nutrition Examination Survey (NHANES) for Dementia Researchers
Three datasets derived from the 2011-12 and 2013-14 NHANES panels (survey years 2011-12, 2013-14, and 2-year appended data file). Datasets include several sociodemographic characteristics merged onto cognition variables. Cognition variables include raw variables and a series of standardized (based on education, race, and age) measures from performance on cognitive tests – both on individual tests and global performance. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (SAS and Stata).
Three datasets derived from the 2011-12 and 2013-14 NHANES panels (survey years 2011-12, 2013-14, and 2-year appended data file). Datasets include several sociodemographic characteristics merged onto cognition variables. Cognition variables include raw variables and a series of standardized (based on education, race, and age) measures from performance on cognitive tests – both on individual tests and global performance. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (SAS and Stata).
National Health Interview Survey (NHIS) for Dementia Researchers
Twelve national annual data files from 2007 to 2018 to identity NHIS respondents with self-reported memory issues. Datasets include several sociodemographic characteristics and cognition variables. In applicable years, datasets include additional merged cognition variables from the Adult Functioning and Disability supplement. NHIS utilizes complex survey design measures to make national annual estimates. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (Stata).
DOI link: https://doi.org/10.3886/E154401V1
Twelve national annual data files from 2007 to 2018 to identity NHIS respondents with self-reported memory issues. Datasets include several sociodemographic characteristics and cognition variables. In applicable years, datasets include additional merged cognition variables from the Adult Functioning and Disability supplement. NHIS utilizes complex survey design measures to make national annual estimates. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (Stata).
DOI link: https://doi.org/10.3886/E154401V1
Behavioral Risk Factor Surveillance System (BRFSS) for Dementia Researchers
Six annual data files from 2013 to 2018 using the full-year BRFSS data on cognitive functioning. Datasets use a series of self-reported measures on cognitive limitations to identify respondents with potential cognitive issues. Datasets also include BRFSS module on caregiving to identify individuals reporting offering care to individuals living with dementia. Data merged with sociodemographic characteristics and state FIPS codes (to make state level estimates). Visit the repository for this project to find lists of included states for each year and module, detailed descriptions of the datasets, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (Stata).
DOI Link: https://doi.org/10.3886/E154421V1
Six annual data files from 2013 to 2018 using the full-year BRFSS data on cognitive functioning. Datasets use a series of self-reported measures on cognitive limitations to identify respondents with potential cognitive issues. Datasets also include BRFSS module on caregiving to identify individuals reporting offering care to individuals living with dementia. Data merged with sociodemographic characteristics and state FIPS codes (to make state level estimates). Visit the repository for this project to find lists of included states for each year and module, detailed descriptions of the datasets, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (Stata).
DOI Link: https://doi.org/10.3886/E154421V1
Medical Expenditure Panel Survey (MEPS) for Dementia Researchers
Five annual data files using data from 2015 to 2019 to identify MEPS respondents with a diagnosis of dementia using truncated ICD (9 and 10) codes in the medical conditions files. Diagnosis data merged on annual healthcare spending estimates, sociodemographic characteristics, and other health utilization measures. Complex survey design allows users to make national estimates of dementia diagnosis. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (SAS and Stata).
DOI Link: https://doi.org/10.3886/E154381V1
Five annual data files using data from 2015 to 2019 to identify MEPS respondents with a diagnosis of dementia using truncated ICD (9 and 10) codes in the medical conditions files. Diagnosis data merged on annual healthcare spending estimates, sociodemographic characteristics, and other health utilization measures. Complex survey design allows users to make national estimates of dementia diagnosis. Visit the repository for this project to find detailed descriptions, data files (in SAS, Stata, and CSV formats), codebooks, and programming scripts (SAS and Stata).
DOI Link: https://doi.org/10.3886/E154381V1