An Algorithm to Characterize a Dementia Population by Disease Subtype

Abstract

Purpose: Identification of Alzheimer's disease and related dementias (ADRD) subtypes is important for pharmacologic treatment and care planning, yet inaccuracies in dementia diagnoses make ADRD subtypes hard to identify and characterize.The objectives of this study were to: 1) develop a method to categorize ADRD cases by subtype and 2) characterize and compare the ADRD subtype populations by demographic and other characteristics. Methods:We identified cases of ADRD occurring during 2008-2014 from the OptumLabs Database using diagnosis codes and anti-dementia medication fills. We developed a categorization algorithm that made use of temporal sequencing of diagnoses and provider type. Results:We identified 36,838 individuals with ADRD. After application of our algorithm, the largest proportion of cases were non-specific dementia (41.2%), followed by individuals with antidementia medication but no ADRD diagnosis (15.6%). Individuals with Alzheimer's disease formed 10.2% of cases. Individuals with vascular dementia had the greatest burden of comorbid disease. Initial documentation of dementia occurred primarily in the office setting (35.1%).Discussion: Our algorithm identified six dementia subtypes and three additional categories representing unique diagnostic patterns in the data. Differences and similarities between groups provided support for the approach and offered unique insight into ADRD subtype characteristics.

Dementia in health claims data: The influence of different case definitions on incidence and prevalence estimates

“…For example, the AD proportion in the German outpatient specialist sector was estimated to be 25%-40% in a survey study (Lohmann et al, 2017). Even lower proportions of around 10% were previously found in health claims data (Albrecht et al, 2018(Albrecht et al, , 2019Kaduszkiewicz et al, 2014). Cho et al (2014), who also examined proportions of dementia diagnoses in healthcare data and distinguished between three dementia categories similarly to ours, found 25% AD in incident cases and 31.9% after 1 year of follow-up.…”

“…For example, the AD proportion in the German outpatient specialist sector was estimated to be 25%–40% in a survey study (Lohmann et al., 2017 ). Even lower proportions of around 10% were previously found in health claims data (Albrecht et al., 2018 , 2019 ; Kaduszkiewicz et al., 2014 ). Cho et al.…”

Risk of Alzheimer’s Disease Following Influenza Vaccination: A Claims-Based Cohort Study Using Propensity Score Matching

“…The specific ICD codes used to define incident AD are provided in Supplementary Table 1. Although AD empirically accounts for nearly 80% of all dementia cases among persons 65 and older [1], studies of administrative datasets have found that the proportion of dementia cases with AD-specific diagnostic codes is far lower than expected and that nonspecific dementia codes (e.g., for unspecified or "senile" dementia) are the most common dementia-related diagnostic codes in these datasets [24][25][26][27]. For example, a study of the entire Medicare fee-for-service beneficiary population from 2011 to 2013 found that, among beneficiaries with ≥ 1 ICD code for dementia, 46.1% had ≥ 1 code for nonspecific dementia but no codes for a specific dementia subtype (e.g., AD), 29.0% had a code for nonspecific dementia and a code for AD, and only 4.5% had ≥ 1 AD code and zero nonspecific dementia codes [27].…”