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BackgroundWith the spread of COVID-19, an infodemic is also emerging. In public health emergencies, the use of information to enable disease prevention and treatment is incredibly important. Although both the information adoption model (IAM) and health belief model (HBM) have their own merits, they only focus on information or public influence factors, respectively, to explain the public’s intention to adopt online prevention and treatment information. ObjectiveThe aim of this study was to fill this gap by using a combination of the IAM and the HBM as the framework for exploring the influencing factors and paths in public health events that affect the public’s adoption of online health information and health behaviors, focusing on both objective and subjective factors. MethodsWe carried out an online survey to collect responses from participants in China (N=501). Structural equation modeling was used to evaluate items, and confirmatory factor analysis was used to calculate construct reliability and validity. The goodness of fit of the model and mediation effects were analyzed. ResultsThe overall fitness indices for the model developed in this study indicated an acceptable fit. Adoption intention was predicted by information characteristics (β=.266, P

BackgroundThe COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies. ObjectiveThis study aims to use online search data (Baidu Index) to monitor the public’s attention and verify internet search engines’ function in public attention monitoring of public health emergencies. MethodsWe collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention’s temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis. ResultsThe Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases. ConclusionsThe national public’s demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public’s attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic.

BackgroundThe internet hospital is an innovative organizational form and service mode under the tide of internet plus in the Chinese medical industry. It is the product of the interaction between consumer health needs and supply-side reform. However, there has still been no systematic summary of its establishment and definition, nor has there been an analysis of its service content. ObjectiveThe primary purpose of this study was to understand the definition, establishment, and development status of internet hospitals. MethodsData on internet hospitals were obtained via the Baidu search engine for results up until January 1, 2019. Based on the results of the search, we obtained more detailed information from the official websites and apps of 130 online hospitals and formed a database for descriptive analysis. ResultsBy January 2019, the number of registered internet hospitals had expanded to approximately 130 in 25 provinces, accounting for 73.5% of all provinces or province-level municipalities in China. Internet hospitals, as a new telehealth model, are distinct but overlap with online health, telemedicine, and mobile medical. They offer four kinds of services—convenience services, online medical services, telemedicine, and related industries. In general, there is an underlying common treatment flowchart of care in ordinary and internet hospitals. There are three different sponsors—government-led integration, hospital-led, and enterprise-led internet hospitals—for which stakeholders have different supporting content and responsibilities. ConclusionsInternet hospitals are booming in China, and it is the joint effort of the government and the market to alleviate the coexistence of shortages of medical resources and wasted medical supplies. The origin of internet hospitals in the eastern and western regions, the purpose of the establishment initiator, and the content of online and offline services are different. Only further standardized management and reasonable industry freedom can realize the original intention of the internet hospital of meeting various health needs.

Objective To explore the relationship between monocyte/high-density lipoprotein cholesterol ratio （MHR） and the progression and prognosis of type 2 diabetic kidney disease （DKD）. Methods From January 1， 2017 to December 31， 2022， 269 type 2 DKD patients were selected as DKD group while 269 healthy medical check-ups during the same period as healthy group. And the differences in MHR levels of two groups were compared. According to median MHR， DKD group were assigned into low-level MHR and high-level MHR sub-groups. General profiles， clinical data， the incidence rate of endpoint events and cumulative renal survival were compared two groups. Cox regression analysis was performed for exploring the independent risk factors for poor renal prognosis in DKD patients and drawing receiver operator characteristic curve （ROC） for exploring the diagnostic efficacy of MHR for poor prognosis of DKD. Results MHR level was higher in DKD group than that in healthy group [0.4918（0.3788，0.6818）×109/mmol vs 0.2984（0.1867，0.4112）×109/mmol] （P＜0.05）； high-level MHR group had higher levels of white blood cells （WBC） [7.70（6.40， 8.70）×109/L vs 6.50（5.40， 8.00）×109/L]， neutrophils （Ne） [4.60（3.60， 5.53）×109/L vs 3.99（3.18， 5.19）×109/L] and monocyte （Mono） [0.69（0.60， 0.70）×109/L vs 0.50（0.40， 0.60）×109/L]， urinary albumin to creatinine ratio （UACR） [1214.59（373.48， 3410.02）mg/g vs 1050.96（180.26， 3341.06） mg/g]， 24 h urine protein （24 hUP） [3.21（1.42， 5.51）g vs 2.66 （0.58， 4.56） g]， low-density lipoprotein cholesterol （LDL-C） [2.72（2.06， 3.40）mmol/L vs 2.23（1.63， 2.80）mmol/L]， serum creatinine （Scr） [152.10（95.20， 221.60）μmol/L vs 126.00（92.48， 186.55）μmol/L] than those in low-level MHR group； lymphocyte （Lym） [1.60（1.27， 2.20）×109/L vs 1.82（1.30， 2.40）×109/L]， high-density lipoprotein cholesterol （HDL-C） [0.94（0.83， 1.07）mmol/L vs 1.39（1.15， 1.65）mmol/L] and estimated glomerular filtration rate （eGFR） [39.69（25.19， 65.10）mL·min−1·（1.73 m²）−1 vs 47.12（28.86， 73.60）mL·min−1·（1.73 m²）−1] were lower than those in low-level MHR group； high-level MHR group had a cumulative kidney survival time and it was shorter than that in low level MHR group [63（39， 72）month vs 72（46， 72）month] （P＜0.05）； MHR was correlated positively with WBC， Ne， Mono， UACR， 24h UP， Scr and LDL-C （P＜0.05） and negatively with Lym， HDL-C， eGFR and cumulative renal survival time （P＜0.05）； the incidence of endpoint events was higher in high-level MHR group than that in low-level MHR group （52.59% vs 38.06%）（P＜0.05）； baseline MHR [0.5492（0.4030， 0.7235）×109/mmol vs 0.4255（0.3117， 0.5134）×109/mmol]， UACR [2062.65（752.80， 4234.80）mg/g vs 608.56（88.63. 1912.44）mg/g]， 24 hUP [3.79（2.54， 5.53）g vs 1.58（0.39， 4.85）g] and Scr [178.40（134.00， 234.23）μmol/L vs 100.95（74.25， 152.10）μmol/L] were higher than those in DKD patients without endpoint events； eGFR was lower than that in DKD patients without endpoint events [33.45（23.33， 46.41）mL·min−1·（1.73 m²）−1 vs 61.59（38.57， 95.98）mL·min−1·（1.73 m²）−1]（P＜0.05）. The results of Cox regression analysis indicated that MHR was an independent risk factor for a poor prognosis of DKD； The results of ROC curve showed that the area under the curve of MHR was 0.747 with a sensitivity of 0.820 and a specificity of 0.605. Conclusion DKD patients tend to have higher levels of MHR as compared with healthy individuals. As an independent risk for the progression of renal function in DKD patients， MHR has some diagnostic value for a poor prognosis of DKD. However， its specificity is not high.