Open Access

Population-Level Assessment of Sex and Age Disparities in Acute Myeloid Leukemia Hospitalization Burden in U.S. Adults Using a Retrospective Healthcare Utilization Framework (2009–2018)

4 Faculty of Education Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Abstract

Acute myeloid leukemia (AML) represents a clinically aggressive hematological malignancy with substantial hospitalization burden, particularly among older adults. Despite advances in molecular diagnostics and risk stratification, population-level evidence describing sex- and age-specific disparities in AML-related hospitalizations remains limited. This study presents a retrospective, population-based technical assessment of AML hospitalization patterns in U.S. adults from 2009 to 2018 using a healthcare utilization framework derived from national inpatient surveillance data structures. The objective is to evaluate differential hospitalization burden across sex and age strata and to interpret observed disparities within an epidemiological and molecular context.

Findings from prior population-level studies indicate persistent sex-based survival and incidence differences in AML, with males exhibiting consistently higher disease burden compared to females across multiple age groups (Hossain & Xie, 2015; Utuama et al., 2019). Age remains a dominant determinant of AML hospitalization intensity, with a marked concentration among older adults (Shallis et al., 2019). Molecular heterogeneity further complicates disease trajectory, as leukemogenic pathways and biomarker variability influence both progression and treatment response (rada-Arismendy et al., 2017). This technical synthesis integrates epidemiological patterns with healthcare utilization structures to conceptualize AML hospitalization disparities in a population-level framework.

The study emphasizes the need for stratified healthcare planning, improved risk prediction models, and integration of molecular epidemiology into population surveillance systems. Limitations include reliance on secondary data constructs and absence of individual-level clinical variables.

Keywords

References

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