Geographic patterns of platform-reported pediatric major-disease cases and healthcare inequality in China: spatial associations and cross-regional healthcare seeking

Geographic inequality in healthcare systems has become a major challenge to health equity, particularly in rapidly transforming countries marked by pronounced socioeconomic and demographic disparities. However, the geographic relationship between pediatric major-disease patterns and the distribution of healthcare resources remains insufficiently understood, particularly in the context of regional inequalities in healthcare provision and cross-regional healthcare seeking. This study examines the geographic distribution of platform-reported pediatric major-disease cases and inequalities in healthcare provision in China and explores their associations with environmental, socioeconomic, and healthcare-related factors. This study covered 31 provincial-level administrative units in mainland China and included 13,112 publicly disclosed cases among individuals aged ≤ 18 years, classified into six major disease categories: solid tumors (ST), hematological diseases (HemD), metabolic diseases (MetD), rare and genetic diseases (RGD), trauma and surgical conditions (TrauS), and organ failure and sequelae (OFS). Cumulative platform-reported case rates were calculated per 100,000 resident minors for spatial comparison. Spatial heterogeneity was assessed at the provincial and seven-region scales, while age-specific variation was examined across five developmental stages. Global Moran’s I and Getis–Ord Gi* were used to assess global and local spatial autocorrelation, respectively. GeoDetector was applied as an exploratory analysis using four strata, permutation tests, and sensitivity analyses. Cross-provincial patient flows and population-weighted Theil indices were used to characterize healthcare utilization and resource inequality. Pronounced multi-scale spatial variation was observed, with higher population-normalized platform-reported pediatric case measures observed in northeastern and eastern China and comparatively lower values in western regions. Global Moran’s I indicated significant positive spatial autocorrelation across all six disease categories, while Getis–Ord Gi* analysis further identified statistically significant spatial hot and cold spots. Age-specific patterns were also evident among the reported cases: solid tumors and rare and genetic diseases accounted for relatively larger shares in early life, whereas hematological and metabolic diseases were more concentrated in childhood age groups. Population size, environmental pollution, and healthcare resources showed comparatively high spatial explanatory power for several disease categories, while several factor pairs exhibited enhanced joint spatial explanatory power in the GeoDetector analysis. Healthcare utilization was highly centralized: 33.16% of the platform-reported pediatric cases ( n = 4,348; 95% CI: 32.36–33.97%) involved treatment outside the province of registered residence, and 67.89% of these cross-provincial flows were directed to Beijing, Shanghai, and Guangdong. Theil decomposition showed that the geographic composition of healthcare-resource inequality differed across resource types, with the relative contributions of between-region and within-region inequality varying among healthcare personnel, healthcare institutions, and hospital beds. The geographic distribution of platform-reported pediatric major-disease cases in China coexists with substantial geographic inequalities in healthcare-resource allocation and a marked concentration of cross-provincial healthcare seeking. The observed spatial associations and inequality patterns provide descriptive evidence for geographically differentiated and equity-oriented pediatric healthcare planning.