| Aims: A health economics evaluation of stroke intervention strategies in a hypertensive population in China,using Shanghai J district as an example.Follow-up and visits to hypertensive patients were obtained through the Shanghai Chronic Disease Management System,and a Markov model was applied to simulate a cohort of 100,000 hypertensive people over 34 years.To explore the economic benefits and impact of interventions on the occurrence of stroke in hypertensive populations and to select the best intervention strategy for stroke prevention and treatment in China.Methods: Firstly,a field study was conducted to understand the health utility of hypertensive patients in Shanghai J.The number of follow-up visits and consultations for hypertensive patients was obtained through the chronic disease management system and the consultation system in Shanghai J.The OR(odds ratio)and HR(hazard ratio)values for the effect of follow-up visits and standardised consultations on stroke were obtained.Secondly,the epidemiological,utility and cost parameters for the modelling were obtained through a literature study;next,a Markov model was used to simulate the cohort of patients with hypertension aged 66 years as a baseline for the no intervention group,the follow-up group,the standardised attendance group and the follow-up combined with standardised attendance group;finally,the results were evaluated in terms of health economics,and a one-way sensitivity analysis was conducted for each indicator,followed by a probabilistic sensitivity analysis of the model to explore The results were evaluated for health economics,with one-way sensitivity analyses for each indicator,and then probabilistic sensitivity analyses for the model to explore the veracity and stability of the findings.Results: After modelling a 34-year stroke intervention cycle in 100,000 hypertensive people aged 66 years,it was concluded that the follow-up combined with a standardised clinic visit strategy consumed the most cost(686,069,200),with the remaining intervention strategies costing no intervention(12,319,9 QALYs),follow-up(1,281,19 QALYs)and standardised clinic visit(65,156,72 QALYs);the follow-up combined with a standardised clinic visit strategy gained the highest total utility(10.47QALYs).The highest total utility was obtained for the no intervention strategy(10.47QALYs),followed by no intervention(10.39 QALYs),follow-up(10.43 QALYs)and standardised visit(10.44 QALYs);in terms of ICER results,the ICERs compared to the no intervention strategy were,in descending order,follow-up($12,139.51),follow-up combined with standardised visits($92,948.22)and standardised visits($10,606,593.51),with standardised visits being much larger than 3 times China’s GDP per capita in 2021,making it an absolutely inferior intervention strategy;as shown by the univariate sensitivity analysis,follow-up post-onset mortality HR,follow-up costs and stroke mortality had a greater impact on ICER values,but all factors varied within their adjusted ranges in incremental cost-The probabilistic sensitivity analysis showed that the probability that the follow-up strategy was economic at 1 times GDP per capita was 97.08%,the probability that the no-intervention strategy was economic was 2.92%and the probability that the combined standardised visit and follow-up standardised visit was economic was 0.When willingness to pay was 3 times GDP per capita,the probability that the follow-up strategy was economic was 99.67%,the probability that the no-intervention strategy was economic was 0.21%,and the probability that the combined standardised and follow-up standardised visits were economic was 0.Conclusion: Follow-up in combination with standardised consultation is a poor intervention strategy;follow-up is the best stroke intervention strategy;follow-up has the highest probability of being economic.Recommendations: strengthen hypertension management to reduce the incidence of stroke events;promote forward movement to strengthen primary stroke prevention;and conduct regular economic evaluations using real-world data. |