DECOMPOSITION OF GEOGRAPHIC VARIATION IN HEALTHCARE SPENDING
Author(s)
Danqing Qian, MPA1, MIN HU, PhD2, Wen Chen, PhD3;
1Fudan University, Shanghai, China, 2Fudan University, Professor, Shanghai, China, 3School of Public Health, Fudan University, Shanghai, China
1Fudan University, Shanghai, China, 2Fudan University, Professor, Shanghai, China, 3School of Public Health, Fudan University, Shanghai, China
OBJECTIVES: Significant geographic variation in healthcare spending persists across China, reflecting disparities that extend beyond clinical need to include system-level factors. Decomposing these differences is critical for identifying policy-actionable drivers and advancing equity-oriented reforms in healthcare delivery. This study aims to quantify the contributions of various factors to geographic disparities in inpatient spending across different enrollment areas within an eastern Chinese province, with a focus on elements amenable to policy intervention.
METHODS: We conducted a cross-sectional analysis of provincial health insurance claims data from 2024, focusing on inpatient care. Five drivers were predefined, including care-location composition (local, within-province non-local, out-of-province), service intensity, insurance-scheme composition, disease mix, and age-sex composition. The relative importance of drivers was assessed using Shapley decomposition, while Das Gupta decomposition estimated the additive contribution of each driver to deviations of enrollment areas from the provincial mean.
RESULTS: Shapley decomposition attributed the largest shares of explained variation to care-location composition (33.53%) and service intensity (31.74%), followed by insurance-scheme composition (17.36%) and disease mix (14.44%); age-sex composition contributed minimally (2.92%). Das Gupta decomposition confirmed that deviations from the provincial mean were primarily driven by service intensity, with care-location composition and disease mix acting as significant modifiers.
CONCLUSIONS: Geographic variation in inpatient spending was driven largely by where patients obtained care and by service intensity, indicating that spending disparities are more closely related to system-level incentives and care pathways than to differences in clinical need. Reforming referral mechanisms and aligning payment incentives across regions are potential strategies to reduce unwarranted variation in healthcare spending and promote more equitable care delivery.
METHODS: We conducted a cross-sectional analysis of provincial health insurance claims data from 2024, focusing on inpatient care. Five drivers were predefined, including care-location composition (local, within-province non-local, out-of-province), service intensity, insurance-scheme composition, disease mix, and age-sex composition. The relative importance of drivers was assessed using Shapley decomposition, while Das Gupta decomposition estimated the additive contribution of each driver to deviations of enrollment areas from the provincial mean.
RESULTS: Shapley decomposition attributed the largest shares of explained variation to care-location composition (33.53%) and service intensity (31.74%), followed by insurance-scheme composition (17.36%) and disease mix (14.44%); age-sex composition contributed minimally (2.92%). Das Gupta decomposition confirmed that deviations from the provincial mean were primarily driven by service intensity, with care-location composition and disease mix acting as significant modifiers.
CONCLUSIONS: Geographic variation in inpatient spending was driven largely by where patients obtained care and by service intensity, indicating that spending disparities are more closely related to system-level incentives and care pathways than to differences in clinical need. Reforming referral mechanisms and aligning payment incentives across regions are potential strategies to reduce unwarranted variation in healthcare spending and promote more equitable care delivery.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
EE203
Topic
Economic Evaluation
Topic Subcategory
Cost/Cost of Illness/Resource Use Studies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas