Assessing Geographic Barriers to Access Long-Term Services and Supports in Chengdu, China: A Spatial Accessibility Analysis

China’s rapidly aging population has intensified demand for long-term services and supports (LTSSs), yet geographic disparities in accessibility persist despite policy reforms like long-term care insurance (LTCI). This study evaluates spatial inequities in Chengdu, a megacity piloting LTCI, using an enhanced two-step floating catchment area (2SFCA) method with demand intensity coefficients and a spatial mismatch index (SMI). Results reveal critically low average accessibility: 0.126 LTSS beds and 0.019 formal caregivers per thousand recipients within a 60 min travel threshold. Accessibility declines sharply along urbanization gradients, with urban cores (“first loop”) exceeding suburban “second” and “third loop” by ratios of 1.5–2.1 and 2.0–8.0, respectively. Strong correlations with impervious surface ratios (R2 = 0.513–0.643) highlight systemic urban bias in resource allocation. The SMI analysis uncovers divergent spatial mismatches: home care accessibility predominates in western suburbs due to decentralized small-scale providers, while institutional care clusters in eastern suburbs, reflecting government prioritization of facility-based services. Despite LTCI’s broad coverage (67% of Chengdu’s population), rural and peri-urban older adults face compounded barriers, including sparse LTSS facilities, inadequate transportation infrastructure, and reimbursement policies favoring urban institutional care. To address these inequities, this study proposes a multi-stakeholder framework: (1) strategic expansion of LTSS facilities in underserved suburban zones, prioritizing institutional care in the “third loop”; (2) road network optimization to reduce travel barriers in mountainous regions; (3) financial incentives (e.g., subsidies, tax breaks) to attract formal caregivers to suburban areas; (4) cross-regional LTCI coverage to enable access to adjacent district facilities; and (5) integration of informal caregivers into reimbursement systems through training and telehealth support. These interventions aim to reconcile spatial mismatches, align resource distribution with Chengdu’s urban–rural integration goals, and provide scalable insights for aging megacities in developing contexts. By bridging geospatial analytics with policy design, this study underscores the imperative of data-driven governance to ensure equitable aging-in-place for vulnerable populations.