{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:00:04Z","timestamp":1785214804758,"version":"3.55.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To examine the spatiotemporal evolution and equity of traditional Chinese medicine (TCM) service capacity across China from 2012 to 2023 and to provide empirical evidence for improving regional resource allocation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using data on TCM services from the China Health Statistical Yearbook (2013\u20132024 editions), we constructed a panel dataset for 31 provinces in China covering 2012\u20132023. Six core indicators were included: outpatient and emergency visits to TCM institutions, number of discharged patients, number of TCM medical and health institutions, number of beds, number of TCM practitioners, and number of staff in TCM hospitals. To improve interprovincial comparability, four contextual indicators were additionally incorporated: gross domestic product per capita, year-end resident population, administrative area, and population density. An entropy-weighting method was used to calculate the composite score of TCM service capacity for each province and year. Spatial dependence was assessed using global Moran's\n                      <jats:italic>I<\/jats:italic>\n                      based on an economic-geographic weight matrix and further examined through local hot spot analysis. Overall disparities, intra-regional disparities, inter-regional disparities, and the contribution of transvariation density were evaluated using the Dagum Gini coefficient and its decomposition.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      From 2012 to 2023, TCM service capacity in China improved steadily. Outpatient and emergency visits, number of discharged patients, number of TCM medical and health institutions, number of beds, number of TCM practitioners, and number of staff in TCM hospitals increased by 105.5%, 99.0%, 135.0%, 145.4%, 114.0%, and 121.2%, respectively. Composite scores rose in all 31 provinces, with Beijing and Shanghai consistently ranking among the top-performing regions, while Sichuan, Chongqing, Guangdong, and Shandong showed particularly marked improvement. Spatial analysis indicated that global Moran's\n                      <jats:italic>I<\/jats:italic>\n                      was positive and statistically significant in every year, ranging from 0.140 to 0.224, suggesting a stable positive spatial autocorrelation in provincial TCM service capacity. Local spatial patterns were characterized by strengthening hot spots in northern China and relatively stable cold spots in the south. The Dagum Gini coefficient showed that overall national disparities declined from 0.2107 in 2012 to 0.1459 in 2023, with a mean value of 0.1767, indicating an overall convergence trend. Intra-regional disparities were relatively higher in the eastern and western regions and lower in the central and northeastern regions. Inter-regional disparities accounted for the largest share of overall inequality, with a mean contribution rate of 42.57%, exceeding that of intra-regional disparities (28.15%) and transvariation density (29.28%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>From 2012 to 2023, TCM service capacity improved overall across provinces in China, although interprovincial disparities persisted. Overall inequality showed a converging trend and was accompanied by significant spatial clustering. In terms of the sources of disparity, inter-regional differences remained the primary driver of overall inequality, with particularly pronounced gradients between the eastern region and other parts of the country, whereas within-region disparities were relatively small in the central and northeastern regions. Future efforts should therefore focus not only on continued capacity expansion, but also on strengthening cross-regional coordination, optimizing the targeted allocation of resources, and promoting a more spatially balanced distribution of TCM services to improve equity in service capacity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1884758","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T04:26:26Z","timestamp":1785212786000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-index spatio-temporal joint evaluation of traditional Chinese medicine service capacity: empirical analysis of china provincial panel data from 2012 to 2023"],"prefix":"10.3389","volume":"14","author":[{"given":"Rui","family":"Quan","sequence":"first","affiliation":[{"name":"Beijing University of Chinese Medicine","place":["Beijing, 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