Efficiency of fever clinics in Shanghai’s primary healthcare centers: a DEA-Tobit analysis

The fever clinic system, initially developed in China in 2003, was expanded to include primary healthcare centers (PHCs) at the grassroots level in 2020. Preliminary interviews indicated occurrences of periodic resource depletion and wastage. Despite substantial investments aimed at improving the infrastructure, staffing, and equipment of clinics across Shanghai, a systematic evaluation of the daily operational efficiency of these clinics has not yet been conducted. This study seeks to address this gap in the literature. This study utilized a multi-stage stratified sampling approach and developed a bespoke questionnaire for data collection. A cross-sectional survey was administered across the fever clinics of 177 PHCs in Shanghai. The comprehensive technical efficiency (CTE), pure technical efficiency (PTE), and scale efficiency (SE) of these clinics were assessed using the CCR and BCC models. A Tobit regression analysis was conducted to assess the impact of eight external factors on the efficiency of fever clinics. Data were collected through online surveys, yielding 177 valid responses from 16 districts in Shanghai. The study identified four output indicators: the total number of visits, the total number of confirmed SARS-CoV infections, the total number of confirmed Flu A/Flu B infections, and the total number of referrals. Additionally, seven input indicators were considered. The Data Envelopment Analysis (DEA) efficiency assessment revealed that the mean CTE of the 177 fever clinics was 0.34 ± 0.32, the mean PTE was 0.84 ± 0.21, and the mean SE was 0.38 ± 0.33. Of these clinics, 20 (11.30%) achieved full efficiency of CTE (CTE = 1). After eliminating the two factors of multicollinearity, a correlation analysis was performed between five input variables and the four output variables. The findings indicated positive correlations between the usable area of the fever clinic rooms, the cost of hardware configuration, and the four output indicators. Notably, the correlation with the total number of confirmed SARS-CoV infections was the strongest, with correlation coefficients of r = 0.276 and r = 0.346. The inefficiency observed in the PHC fever clinics in Shanghai is attributed not to a lack of technical capabilities, but rather to a mismatch between operational scale and demand. The investment in the usable area of the fever clinic rooms and the cost of hardware configuration was associated with efficiency; however, extending operation days did not correlate with increased efficiency. The service efficiency of fever clinics was influenced by objective factors such as the permanent population and the available building area of PHC, but it was not related to geographical location, the presence of X-ray machines, or the accessibility of CT machines. It is recommended that in regions with limited resources or during high-incidence influenza seasons, point-of-care testing (POCT) for antigen monitoring should be prioritized. A hierarchical configuration standard should be established, allowing for differentiated and flexible arrangements of hardware and clinic operating times based on community population density and historical risk of infectious diseases, in order to avoid resource wastage.