Developing a novel evaluation model to assess the performance of base hospitals in China National Stroke Screening Surveys - Xuedong Liu; Jian Cao; Ou Jiang (PLoS ONE, 2025)
Background The China National Stroke Screening Surveys (CNSSS), a decade-long national public health initiative, aims to reduce stroke morbidity and mortality through early detection and intervention. This study develops a novel evaluation model to systematically assess the performance of base hospitals (BHS) participating in the CNSSS program in Sichuan Province, China. Methods Sixteen BHS in Sichuan Province participated in the CNSSS program in 2024. We utilized eleven performance indicators to develop the evaluation model. Subjective weights were derived from scores assigned to indicators, while objective weights were calculated using the Entropy Weight Method (EWM). A Multiplicative Synthesis with Normalization (MSN) method was adopted to generate combined weights. Based on the subjective, objective, and combined weights, we generated weighted data matrices, determined the corresponding BHS rankings separately, and compared BHS performance in CNSSS implementation across these three ranking systems. Results Among the evaluation indicators, Task Completion Rate received the highest subjective weight (0.3125), whereas Intervention Rate dominated both objective (0.1594) and combined (0.2303) weights. Notable weight changes were observed: Task Completion Rate exhibited the largest reduction (−92.45%) from subjective to objective weights, followed by Age Deviation Degree (−76.11%), Follow-up Completion Rate (−73.31%), and Hypertension Awareness Rate (−3.31%). Conversely, Diabetes Detection Rate displayed the most significant increase (+313.65%), followed by Dyslipidemia Detection Rate (+249.44%), Hypertension Detection Rate (+227.79%), Stroke High-Risk Detection Rate (+119.52%), Stroke High-Risk Intervention Rate (+107.90%), Intervention Rate (+48.77%), and Risk Factor Control Rate (+42.52%). BHS A and B ranked top 3 across all weighting methodologies. BHS A ranked first under original, subjective and combined weights, while BHS D led in the objective ranking. Compared to the original methodology, the combined weighting methodology has the highest discrimination degree (0.1166). Conclusions Weighting methodologies significantly influence BHS performance evaluations. Subjective approaches emphasize expert expertise, whereas objective methods prioritize data variability. Compared to single weighting method, combined weighting effectively balances discrepancies between expert subjective priorities and data-driven objectivity, thereby addressing limitations of single-method designs. For the CNSSS program, our model underscores the need to shift quality focus from high-risk screening to targeted management, including timely post-screening interventions and effective risk factor control. These targeted interventions are expected to significantly reduce stroke incidence, recurrence, and mortality among screened populations, while enhancing the overall quality of the CNSSS program in the region.