早期筛查老年人群糖尿病视网膜病变的卫生经济学分析

doi:10.3877/cma.j.issn.2095-2007.2022.04.005

目的

从社会角度和卫生系统角度探讨中国50岁以上人群行糖尿病视网膜病变(DR)筛查的经济学效益。

方法

检索中国知网、万方数据库、PubMed及Medline等数据库，检索日期为自建库之始至2020年12月，搜集中国50岁以上人群DR的患病率、转归率、筛查依从性、治疗依从性、筛查敏感性、筛查特异性、DR不同分期的效用值及死亡率等指标。按照国际分级系统将DR分为不威胁视力DR、威胁视力DR，糖尿病性黄斑水肿及DR致盲等4个阶段。筛查项目、转诊检查及治疗费用来源于邯郸眼病研究和2020年首都医科大学附属北京同仁医院住院系统。使用TreeAge Pro软件构建DR筛查的马尔可夫模型，以社区筛查、远程筛查及人工智能(AI)辅助筛查为研究策略，以无筛查为对照策略，以我国农村和城市50岁以上人群为队列人群，比较不同策略的成本效益，并比较1~5年筛查间隔的成本效益。2020年中国农村和城市人均生产总值(GDP)分别为49 000元和84 000元。经济学效益以增量成本－效用比(ICUR)和增量成本－效果比(ICER)表示。成本效益阈值按照世界卫生组织规定的ICER或ICUR<3倍人均GDP为具有成本效益，<1倍人均GDP为具有高成本效益，>3倍人均GDP则不具备经济性。通过敏感性分析评价模型的稳健性。

结果

农村和城市人群的成本效益阈值分别为147 000元和252 000元。从社会角度看，对农村50岁以上人群进行社区、远程及AI辅助筛查的ICUR分别为4214元、2772元及3206元；每避免1个致盲年导致的ICER分别为216 524元、152 726元及164 745元；对城市人群筛查的ICUR分别为7133元、5061元及5894元；每避免1个致盲年导致的ICER分别为57 792元、37 632元及55 839元。与无筛查人群相比，农村和城市的ICUR以及城市人群的ICER均<1倍人均GDP；3种筛查方式对农村50岁以上人群的ICER均略高于成本效益阈值。从卫生系统角度看，3种筛查方式对农村和城市老年人群的ICUR和ICER均低于成本效益阈值。社区或AI辅助筛查对城市老年人群间隔2年以上具有成本效益，远程筛查间隔1年以上已具有成本效益；而对农村人群各筛查间隔均不具有成本效益。经概率敏感性分析，当成本效益阈值为3倍人均GDP时，农村老年人群社区、远程及AI辅助筛查成本效益概率分别为87.34%、87.56%及87.54%；城市老年人群筛查的成本效益概率分别为89.19%、89.48%及89.61%。当成本效益阈值为1倍人均GDP时，3种筛查方式对农村老年人群筛查有成本效益的概率分别为86.03%、86.84%及86.55%；城市老年人群的成本效益概率分别为87.83%、88.55%及88.62%。说明构建马尔可夫模型的设计和结果稳健，对参数的浮动不敏感。

结论

对我国50岁以上老年人群进行DR筛查具有成本效益，因农村和城市的经济发展水平和疾病负担不同，应制定适宜当地的DR筛查模式。

关键词: 糖尿病视网膜病变, 筛查, 人工智能, 远程筛查, 成本效益, 马尔可夫模型

Objective

To explore the cost-effectiveness of screening for diabetic retinopathy (DR) among people over 50 years old in China from social and health system perspectives.

Methods

CNKI, Wanfang Database, PubMed and Medline from the beginning of self-built database to December 2020 were searched, and parameters such as prevalence, transition rate, screening and treatment compliance, screening sensitivity and specificity, utility value and mortality rate of DR among people over 50 years old in China were collected. According to the international classification system, DR was be divided into four stages: non-sight-threatening DR, sight-threatening DR, diabetic macular edema and blindness caused by DR. The cost of screening program, referral examination and treatment was based on the Handan Eye Study and inpatients system of Beijing Tongren Hospital affiliated with Capital Medical University in 2020. TreeAgePro software was used to build a Markov model of DR screening, in which the research strategies were community screening, telescreening and artificial intelligence (AI)-based screening, the control strategy was no-screening, and the cohort population were people over 50 years old in rural and urban areas of China. The cost-effectiveness of different strategies was compared, and the cost-effectiveness of 1 to 5 years screening interval was compared. In 2020, China′s rural and urban per capita GDP was ￥49 000 and ￥84 000, respectively. Economic benefits were expressed in terms of incremental cost-effectiveness ratio (ICUR) and incremental cost-effectiveness ratio (ICER). The WHO defined interventions that cost between one to three times the per capita gross domestic product (GDP) was defined as cost-effective, and interventions that cost less than the per capita GDP as highly cost-effective, and interventions that cost more than three times the per capita GDP as not cost-effective. The sensitivity analysis was used to evaluate the robustness of the model.

Results

From the societal perspective, the ICUR of community, telemedicine and AI-based screening for people over 50 years old for rural elderly were ￥4217, ￥2772 and ￥3206, respectively. The ICER was ￥216 524, ￥152 726 and ￥164 745, respectively. The ICUR for urban elderly was ￥7133, ￥5061 and ￥5894, respectively. The ICER was ￥57 792, ￥37 632 and ￥55 839, respectively. Com-pared with elderly without screening , ICUR for rural and urban elderly and ICER for urban elderly were less than the per capita GDP. However, the ICER of 3 screening methods for over 50 years rural elderly was slightly higher than the cost-effectiveness threshold. From health system perspective, ICUR and ICER of 3 screening methods for rural and urban elderly were lower than the cost-effectiveness threshold. Community and AI-assisted screening were cost-effective for urban elderly at intervals of more than 2 years, while telescreening at intervals of more than 1 year was cost-effective; however, it was not cost-effective to screen rural residents frequently. The sensitivity analysis showed that when the cost-effectiveness threshold was 3 times per capita GDP, the cost-effectiveness probabilities of community, telemedicine and AI-assisted screening for rural population were 87.34%, 87.56% and 87.54%, and the probability of urban population 89.19%, 89.48% and 89.61%, respectively. When the cost-effectiveness threshold was 1 time per capita GDP, the cost-effectiveness probability of 3 screening methods for rural eldly was 86.03%, 86.84% and 86.55%, and the cost-effectiveness probability of urban elderly population 87.83%, 88.55% and 88.62%, respectively. It showed that the design and results of Markov model were robust and insensitive to the fluctuation of parameters.

Conclusions

DR screening for the elderly over 50 years old in China is cost-effective. Due to the different levels of economic development and disease burdens in rural and urban areas, appropriate DR screening models should be developed according to local conditions.

Key words: Diabetic retinopathy, Screening, Artificial intelligence, Telemedicine screening, Cost-effectiveness, Markov model

表1 马尔可夫模型中所用的参数

参数名称		农村		城市
参数名称		数值	来源文献	数值	来源文献
患病率(%)
	非STDR	0.56	[10]	2.92	[11]
	STDR	0.22	[10]	0.98	[11]
	DME	0.34	[10]	0.86	[11]
	DR致盲	0.05	[12]	0.05	[12]
依从性(%)
	社区筛查	90.7	[10，13]	82.5	[14]
	远程筛查	95.0	[15]	96.8	[10，14]
	AI辅助筛查	95.0	[15]	96.8	[10，14]
	全面医院检查	19.0	[6]	57.0	[6]
	治疗	50.0	[16]	60.0	[17]
	机会性筛查	18.9	[18]	45.1	[18]
社区筛查敏感性(%)
	非STDR	76	[5]	76	[5]
	STDR	95	[5]	95	[5]
	DME	82	[19]	82	[19]
远程筛查敏感性(%)
	非STDR	91	[20]	91	[20]
	STDR	91	[20]	91	[20]
	DME	59	[19]	59	[19]
AI辅助筛查敏感性(%)
	非STDR	91	[5，20，21，22]	91	[5，20，21，22]
	STDR	100	[5，20，21，22]	100	[5，20，21，22]
	DME	59	[5，20，21，22]	59	[5，20，21，22]
特异性(%)
	社区筛查	100	[5，20，21，22]	100	[5，20，21，22]
	远程筛查	97	[5，20，21，22]	97	[5，20，21，22]
	AI辅助筛查	91	[5，20，21，22]	91	[5，20，21，22]
效用值(%)
	正常	1.00	[6]	1.00	[6]
	非STDR	0.87	[5，6，16]	0.87	[5，6，16]
	STDR	0.70	[5，6，16]	0.70	[5，6，16]
	DME	0.83	[5，6，16]	0.83	[5，6，16]
	DR致盲	0.26	[5，6，16]	0.26	[5，6，16]
死亡率(%)
	50~54岁	0.364	[22]	0.364	[22]
	55~59岁	0.518	[22]	0.518	[22]
	60~64岁	0.854	[22]	0.854	[22]
	65~69岁	1.421	[22]	1.421	[22]
	70~74岁	3.149	[22]	3.149	[22]
	75~79岁	4.861	[22]	4.861	[22]
	80~84岁	8.932	[22]	8.932	[22]

表1 马尔可夫模型中所用的参数

参数名称		农村		城市
参数名称		数值	来源文献	数值	来源文献
患病率(%)
	非STDR	0.56	[10]	2.92	[11]
	STDR	0.22	[10]	0.98	[11]
	DME	0.34	[10]	0.86	[11]
	DR致盲	0.05	[12]	0.05	[12]
依从性(%)
	社区筛查	90.7	[10，13]	82.5	[14]
	远程筛查	95.0	[15]	96.8	[10，14]
	AI辅助筛查	95.0	[15]	96.8	[10，14]
	全面医院检查	19.0	[6]	57.0	[6]
	治疗	50.0	[16]	60.0	[17]
	机会性筛查	18.9	[18]	45.1	[18]
社区筛查敏感性(%)
	非STDR	76	[5]	76	[5]
	STDR	95	[5]	95	[5]
	DME	82	[19]	82	[19]
远程筛查敏感性(%)
	非STDR	91	[20]	91	[20]
	STDR	91	[20]	91	[20]
	DME	59	[19]	59	[19]
AI辅助筛查敏感性(%)
	非STDR	91	[5，20，21，22]	91	[5，20，21，22]
	STDR	100	[5，20，21，22]	100	[5，20，21，22]
	DME	59	[5，20，21，22]	59	[5，20，21，22]
特异性(%)
	社区筛查	100	[5，20，21，22]	100	[5，20，21，22]
	远程筛查	97	[5，20，21，22]	97	[5，20，21，22]
	AI辅助筛查	91	[5，20，21，22]	91	[5，20，21，22]
效用值(%)
	正常	1.00	[6]	1.00	[6]
	非STDR	0.87	[5，6，16]	0.87	[5，6，16]
	STDR	0.70	[5，6，16]	0.70	[5，6，16]
	DME	0.83	[5，6，16]	0.83	[5，6，16]
	DR致盲	0.26	[5，6，16]	0.26	[5，6，16]
死亡率(%)
	50~54岁	0.364	[22]	0.364	[22]
	55~59岁	0.518	[22]	0.518	[22]
	60~64岁	0.854	[22]	0.854	[22]
	65~69岁	1.421	[22]	1.421	[22]
	70~74岁	3.149	[22]	3.149	[22]
	75~79岁	4.861	[22]	4.861	[22]
	80~84岁	8.932	[22]	8.932	[22]

表2 马尔可夫模型中所用的成本(元)

项目		农村			城市
项目		直接医疗	直接非医疗	间接医疗	直接医疗	直接非医疗	间接医疗
筛查成本
	社区筛查	30	8	98	30	8	168
	远程筛查	17	8	49	17	8	84
	AI辅助筛查	11	8	49	11	8	84
确诊成本		798	861	196	798	8	168
治疗成本
	STDR	14 014	3444	784	14 014	32	672
	DME	12 943	3444	784	12 943	32	672
	致盲	33 219	3983	25 145	33 219	3983	25 145
随诊成本
	STDR和DME	4975	861	196	4975	8	168
	致盲	0	3983	25 145	0	3983	25 145

表2 马尔可夫模型中所用的成本(元)

项目		农村			城市
项目		直接医疗	直接非医疗	间接医疗	直接医疗	直接非医疗	间接医疗
筛查成本
	社区筛查	30	8	98	30	8	168
	远程筛查	17	8	49	17	8	84
	AI辅助筛查	11	8	49	11	8	84
确诊成本		798	861	196	798	8	168
治疗成本
	STDR	14 014	3444	784	14 014	32	672
	DME	12 943	3444	784	12 943	32	672
	致盲	33 219	3983	25 145	33 219	3983	25 145
随诊成本
	STDR和DME	4975	861	196	4975	8	168
	致盲	0	3983	25 145	0	3983	25 145

表3 糖尿病视网膜病变筛查项目成本效用分析和成本效益分析的结果

项目		农村成本效用			农村成本效果		城市成本效用			城市成本效果
项目		成本(元/人)	QALYs/人	ICURs(元/人)	致盲年/人	ICERs(元/人)	成本(元/人)	QALYs/人	ICURs(元/人)	致盲年/人	ICERs(元/人)
社会角度
	无筛查项目	5432	16.236 82		0.143 886		9072	16.064 29		0.197 916
	社区筛查	5558	16.267 12	4214^*	0.143 302	216 524	9240	16.088 68	7133^*	0.194 908	57 792^*
	远程筛查	5516	16.267 10	2772^*	0.143 339	152 726	9191	16.088 84	5061^*	0.194 616	37 632^*
	AI辅助筛查	5530	16.267 12	3206^*	0.143 296	164 745	9212	16.089 01	5894^*	0.194 313	55 839^*
卫生系统角度
	无筛查项目	1428	16.236 82		0.143 886		3906	16.064 29		0.197 916
	社区筛查	1470	16.267 12	1379^*	0.143 302	71 757^*	4011	16.088 68	4130^*	0.194 908	33 516^*
	远程筛查	1463	16.267 10	1120^*	0.143 339	61 768^*	4018	16.088 84	4487^*	0.194 616	33 376^*
	AI辅助筛查	1470	16.267 12	1204^*	0.143 296	61 873^*	4046	16.089 01	5404^*	0.194 313	37 079^*

表3 糖尿病视网膜病变筛查项目成本效用分析和成本效益分析的结果

项目		农村成本效用			农村成本效果		城市成本效用			城市成本效果
项目		成本(元/人)	QALYs/人	ICURs(元/人)	致盲年/人	ICERs(元/人)	成本(元/人)	QALYs/人	ICURs(元/人)	致盲年/人	ICERs(元/人)
社会角度
	无筛查项目	5432	16.236 82		0.143 886		9072	16.064 29		0.197 916
	社区筛查	5558	16.267 12	4214^*	0.143 302	216 524	9240	16.088 68	7133^*	0.194 908	57 792^*
	远程筛查	5516	16.267 10	2772^*	0.143 339	152 726	9191	16.088 84	5061^*	0.194 616	37 632^*
	AI辅助筛查	5530	16.267 12	3206^*	0.143 296	164 745	9212	16.089 01	5894^*	0.194 313	55 839^*
卫生系统角度
	无筛查项目	1428	16.236 82		0.143 886		3906	16.064 29		0.197 916
	社区筛查	1470	16.267 12	1379^*	0.143 302	71 757^*	4011	16.088 68	4130^*	0.194 908	33 516^*
	远程筛查	1463	16.267 10	1120^*	0.143 339	61 768^*	4018	16.088 84	4487^*	0.194 616	33 376^*
	AI辅助筛查	1470	16.267 12	1204^*	0.143 296	61 873^*	4046	16.089 01	5404^*	0.194 313	37 079^*

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Health economic analysis of early screening for diabetic retinopathy in the elderly

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Health economic analysis of early screening for diabetic retinopathy in the elderly