Abstract Background: Prior work has indicated racial, ethnic and geographic disparities in treatment access and outcomes for acute myeloid leukemia (AML). However, the relationship between social vulnerability and AML outcomes remains understudied. The study examines the association between overall Social Vulnerability Index (SVI) and its domains with crude mortality rate (CMR) in AML patients from 2010 to 2020 and describes temporal trends. Methods: County-level SVI data aggregating 15 census variables into four domains-(1) socioeconomic status, (2) household composition and disability, (3) minority status and language, and (4) housing type and transportation, assign each county a percentile score from 0 (least vulnerable) to 1 (most vulnerable). Counties were categorized into four quartiles based on SVI scores: Q1:0-0.25 (least vulnerable) to Q4:0.75-1 (most vulnerable). CMR per 100,000 patients were abstracted from CDC WONDER database for adult patients (≥25 years) with AML and reported for patients 65 and ≥65 years of age in Q1 and Q4 counties. Counties with 10 deaths (1% deaths) were excluded. Poisson regression models, using Q1 as reference, assessed the relationship of CMR and overall SVI in each age group, and reported rate ratios (RR) and 95% confidence intervals (CI). Each SVI domain's independent association with CMR was examined in each age group. Temporal trends were assessed by comparing CMR in 2010-2014 and 2016-2020 cohorts. Results: From 2010 to 2020, 103,411 deaths in AML patients occurred (overall CMR: 5.61 per 100,000). Unexpectedly, higher overall SVI was associated with lower mortality: CMR 5.96 in Q1 vs. 5.01 in Q4. As expected, adults aged65 had a lower mean CMR (1.74 in Q1 vs. 1.77 in Q4) compared to those ≥65 (18.81 in Q1 vs. 15.60 in Q4). In adults aged 65, higher overall social vulnerability was not significantly associated with mortality (Q2 RR 1.01, 95% CI 0.96-1.06, p = 0.75; Q3 RR 1.02, 95% CI 0.98-1.07, p = 0.33; Q4 RR 0.96, 95% CI 0.91-1.00, p=0.054). However, there were significant associations for each SVI domain. A higher SVI in the Minority Status and Language was surprisingly associated with a significantly lower CMR (RR 0.39, 95% CI 0.36-0.43, p0.001); for the other 3 domains, a higher SVI was associated with higher mortality: Socioeconomic status (RR 1.14, 95% CI 1.04-1.25, p=0.004), Household Composition 0.001) and Housing 65, higher overall SVI quartiles were linked to lower CMR (Q2 RR 0.96, 95% CI 0.94-0.98, p .001; Q3 RR 0.91, 95% CI 0.89-0.94, p .001; Q4 RR 0.84, 95% CI 0.82-0.86, p .001). As for the domains, Socioeconomic (RR 0.81, 95% CI 0.78-0.85, p.001) and Minority status and Language (RR 0.77, 95% CI 0.75-0.80, p.001) were associated with a lower CMR rate. Housing .001) and Household Composition 65, CMR in Q1 counties improved from 1.94 in 2010-2014 to 1.71 in 2016-2020 and from 1.72 in 2010-2014 to 1.66 in 2016-2020 in Q4 counties. In adults aged ≥65, CMR in Q1 counties improved from 19.62 in the earlier cohort to 18.70 in later cohort but worsened from 16.32 in 2010-2014 to 16.48 in Q4 counties from 2016 to 2020. Conclusions: 1. Mortality for AML has improved over time for most of the population, except among adults in high vulnerability counties. 2. As expected, mortality is higher for older patients. 3. For adults aged 65, a surprising finding was lower mortality in counties with a higher social vulnerability in the minority and language domain, whereas a higher SVI in the socioeconomic, household, and housing domains was associated with higher mortality. 4. For adults aged ≥65, higher SVI in both the socioeconomic and minority domains was associated with a lower mortality rate, and higher SVI in the housing domain with a higher mortality rate. The authors hypothesize that urban areas with a more diverse population, and therefore a higher SVI in Minority Status and Language Domain, may offer more comprehensive care to patients with AML. In addition, the SVI does not capture important clinical and treatment variables that may impact outcomes. Further research is needed to explore more specific, population based indicators to identify at-risk patients with AML.
Karen K. Ballen (Mon,) studied this question.