Quality evaluation of tuberculosis prevention and control work in Guangxi based on Entropy Weight-TOPSIS and RSR method

doi:10.19982/j.issn.1000-6621.20260166

Abstract

Abstract:

Objective: To explore the key indicators influencing the comprehensive evaluation through the comprehensive quality assessment of tuberculosis prevention and control work in cities of Guangxi Zhuang Autonomous Region from 2021 to 2024, so as to provide a reference for cities to improve the quality of tuberculosis prevention and control. Methods: A comprehensive evaluation of tuberculosis prevention and control performance across various cities was carried out by integrating the entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with the rank-sum ratio (RSR) method. Results: There were significant regional differences in the comprehensive evaluation of tuberculosis control efforts across 14 cities in Guangxi Zhuang Autonomous Region. Laibin and Hezhou consistently ranked in the higher tier (Probit >6). Baise, Yulin, Wuzhou, Chongzuo, Beihai, and Qinzhou remained in the middle tier over time (4≤Probit≤6). Nanning, Fangchenggang, Guigang, Hechi, Liuzhou, and Guilin showed certain fluctuations in their evaluations: Nanning was in the lower tier (Probit <4) from 2022 to 2023 but jumped to the upper tier (Probit >6) in 2024; Fangchenggang was in the middle tier (4≤Probit≤6) from 2021 to 2023 but dropped to the lower tier (Probit <4) in 2024; Liuzhou was in the lower tier (Probit <4) from 2021 to 2022 but rose to the middle tier (4≤Probit≤6) from 2023 to 2024; Guigang was in the middle tier (4≤Probit≤6) from 2021 to 2022 but declined to the lower tier (Probit <4) for two consecutive years from 2023 to 2024. Cluster analysis revealed marked regional differentiation in pulmonary tuberculosis prevention and control quality across Guangxi. The northern region exhibited stable growth; the central region showed polarization, with Nanning characterized as high-start and high-growth, while Guigang and Laibin were classified as slow decline and fluctuating adjustment types, respectively; the eastern region and southern coastal areas (except Fangchenggang) were fluctuating adjustment types, whereas Fangchenggang was a rapid decline type; the western and border regions displayed the strongest spatial heterogeneity, with Chongzuo showing stable growth, Hechi exhibiting slow decline, and Baise being fluctuating adjustment. Lag-one-year correlation analysis did not reveal a significant relationship between the comprehensive score of tuberculosis control performance and changes in reported incidence rate (r=-0.019, P=0.950) or mortality rate (ρ=-0.167, P=0.568). Conclusion: The quality of tuberculosis prevention and control efforts across Guangxi demonstrates considerable instability. Certain areas in the western and central regions—potentially influenced by a high epidemic burden and socioeconomic constraints—have persistently performed at suboptimal levels. The current performance evaluation indicators do not directly capture the impact on epidemic trends.

Key words: Entropy weight-TOPSIS, Ranksum ratio method, Tuberculosis, Quality of health care, Outcome assessment (health care)

CLC Number:

R521

Huang Quanjiang, Huang Minying, Cui Zhezhe. Quality evaluation of tuberculosis prevention and control work in Guangxi based on Entropy Weight-TOPSIS and RSR method[J]. Chinese Journal of Antituberculosis, 2026, 48(9): 1375-1385. doi: 10.19982/j.issn.1000-6621.20260166

Figures/Tables 10

References 15

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地区	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
南宁市	45.66	59.55	97.58	96.86	83.24	88.94	55.56	90.22	83.21	57.99	91.91	83.86
柳州市	48.54	60.59	99.32	99.23	74.52	83.10	57.28	69.42	83.15	66.59	92.31	85.26
桂林市	46.88	63.53	97.47	94.72	80.92	86.20	43.01	75.66	84.78	65.76	92.89	85.54
梧州市	47.78	63.73	98.46	93.96	84.14	94.79	53.95	85.89	89.76	85.45	95.45	91.70
北海市	43.28	61.46	98.02	97.62	77.63	93.72	94.91	78.05	79.25	67.00	93.31	82.14
防城港市	36.87	64.50	98.48	97.96	76.34	94.42	61.85	77.07	84.18	69.92	94.40	86.22
钦州市	33.46	62.68	96.82	97.19	88.81	93.50	37.80	89.34	90.22	75.96	93.93	91.32
贵港市	35.45	58.25	95.28	95.14	81.35	98.30	77.93	73.57	62.59	46.59	93.08	88.67
玉林市	30.12	64.77	97.64	97.80	88.06	93.41	41.43	85.75	86.97	71.79	92.42	85.79
百色市	59.86	60.84	98.99	97.73	87.95	97.46	52.07	84.50	88.46	76.95	95.21	90.62
贺州市	45.67	61.19	98.76	98.14	87.92	98.83	44.88	90.83	80.78	62.77	94.29	94.68
河池市	32.89	65.86	97.05	96.30	83.06	86.88	49.75	84.61	86.43	79.08	93.45	87.80
来宾市	36.45	61.76	98.74	96.81	80.97	92.74	81.32	75.82	88.52	80.57	95.46	87.46
崇左市	61.30	63.32	97.51	97.42	88.06	94.55	66.24	89.79	85.11	68.75	95.20	89.71
合计	44.58	60.93	97.83	97.06	83.72	90.91	54.41	80.26	80.14	67.70	93.32	86.91

评价指标	2021年		2022年		2023年		2024年
评价指标	Z⁺	Z^-	Z⁺	Z^-	Z⁺	Z^-	Z⁺	Z^-
X₁	0.0392	0.0000	0.0394	0.0000	0.0399	0.0000	0.0413	0.0000
X₂	0.0360	0.0000	0.0417	0.0000	0.0495	0.0000	0.0339	0.0000
X₃	0.0393	0.0000	0.0447	0.0000	0.0358	0.0000	0.0378	0.0000
X₄	0.0464	0.0000	0.0356	0.0000	0.0516	0.0000	0.0443	0.0000
X₅	0.0380	0.0000	0.0327	0.0000	0.0587	0.0000	0.0466	0.0000
X₆	0.0383	0.0000	0.0356	0.0000	0.0350	0.0000	0.0405	0.0000
X₇	0.0614	0.0000	0.0644	0.0000	0.0585	0.0000	0.0704	0.0000
X₈	0.0430	0.0000	0.0290	0.0000	0.0355	0.0000	0.0351	0.0000
X₉	0.0377	0.0000	0.0368	0.0000	0.0423	0.0000	0.0445	0.0000
X₁₀	0.0369	0.0000	0.0372	0.0000	0.0350	0.0000	0.0455	0.0000
X₁₁	0.0341	0.0000	0.0350	0.0000	0.0330	0.0000	0.0565	0.0000
X₁₂	0.0389	0.0000	0.0628	0.0000	0.0413	0.0000	0.0363	0.0000

年份	南宁市	柳州市	桂林市	梧州市	北海市	防城港市	钦州市	贵港市	玉林市	百色市	贺州市	河池市	来宾市	崇左市
2021年
D⁺	0.0862	0.1031	0.1007	0.0765	0.0702	0.0814	0.0843	0.1001	0.0767	0.0839	0.0663	0.0893	0.0696	0.0825
D^-	0.0736	0.0647	0.0636	0.0859	0.0938	0.0780	0.0875	0.0769	0.0851	0.0851	0.0913	0.0660	0.0945	0.0708
C_i	0.4607	0.3856	0.387	0.5290	0.5719	0.4894	0.5094	0.4343	0.5260	0.5037	0.5794	0.4251	0.5757	0.4618
排名	10	14	13	4	3	8	6	11	5	7	1	12	2	9
2022年
D⁺	0.1199	0.1068	0.097	0.0959	0.0888	0.0897	0.0795	0.1004	0.0933	0.0867	0.0803	0.0930	0.0668	0.0877
D^-	0.0563	0.0653	0.0651	0.0811	0.0904	0.0771	0.1001	0.0727	0.0833	0.0789	0.0879	0.0726	0.1016	0.0793
C_i	0.3195	0.3794	0.4016	0.4582	0.5044	0.4621	0.5572	0.4200	0.4717	0.4766	0.5226	0.4383	0.6035	0.4749
排名	14	13	12	9	4	8	2	11	7	5	3	10	1	6
2023年
D⁺	0.1114	0.1024	0.0927	0.0777	0.0988	0.1076	0.0927	0.1162	0.0851	0.0859	0.0896	0.0956	0.0822	0.0885
D^-	0.0527	0.0757	0.0788	0.0880	0.0869	0.0737	0.0823	0.0671	0.0870	0.0870	0.0947	0.0703	0.0957	0.0765
C_i	0.3212	0.4249	0.4595	0.5312	0.4678	0.4066	0.4704	0.3663	0.5054	0.5032	0.5138	0.4237	0.5380	0.4638
排名	14	10	9	2	7	12	6	13	4	5	3	11	1	8
2024年
D⁺	0.0971	0.1042	0.1104	0.1000	0.1028	0.1182	0.0940	0.1167	0.1070	0.1001	0.0934	0.1097	0.0837	0.0973
D^-	0.0966	0.0726	0.0695	0.0842	0.0959	0.0510	0.0892	0.0691	0.0863	0.0752	0.0931	0.0655	0.0950	0.0768
C_i	0.4986	0.4104	0.3862	0.4571	0.4828	0.3016	0.4871	0.3718	0.4465	0.4289	0.4990	0.3737	0.5316	0.4411
排名	3	10	11	6	5	14	4	13	7	9	2	12	1	8

年份	等级	Probit值	城市分档结果
2021	下	0~3	柳州市、桂林市
	中	4~5	河池市、贵港市、南宁市、崇左市、防城港市、百色市、钦州市、玉林市、梧州市
	上	6~9	北海市、来宾市、贺州市
2022	下	0~3	南宁市、柳州市
	中	4~5	桂林市、贵港市、河池市、梧州市、防城港市、玉林市、崇左市、百色市、北海市
	上	6~9	贺州市、钦州市、来宾市
2023	下	0~3	南宁市、贵港市
	中	4~5	防城港市、河池市、柳州市、桂林市、崇左市、北海市、钦州市、百色市、玉林市
	上	6~9	贺州市、梧州市、来宾市
2024	下	0~3	防城港市、贵港市
	中	4~5	河池市、桂林市、柳州市、百色市、崇左市、玉林市、梧州市、北海市、钦州市
	上	6~9	南宁市、贺州市、来宾市