Evaluation of Willingness to Accept COVID-19 Vaccine and Willingness to Pay among Pakistani Parents for Their Children Aged 5 to 11 Years: Findings and Implications.

Vaccines are the most efficient and cost-effective tool to halt the transmission and prevention of COVID-19. The current study examined the willingness of parents to vaccinate their children against COVID-19. This was a cross-sectional study that used a questionnaire based on the Health Belief Model, previous history of COVID-19, willingness to accept, and willingness to pay for the COVID-19 vaccine. The questionnaire was administered among parents of children aged 5 to 11 years. Descriptive statistics, χ2 tests, and regression analysis were carried out for data analysis. A total of 474 respondents participated in this survey with a response rate of 67.7%. In our study, a majority of the respondents exhibited a willingness to accept the COVID-19 vaccine for their children (Definitely yes/Probably yes = 252, 53.2%); nevertheless, 229 (48.3%) respondents were unwilling to pay for it. More than three-quarters of the respondents were worried about the probability of COVID-19 infection in their children (n = 361, 76.2%) and were afraid of COVID-19-associated complications (n = 391, 82.5%). Likewise, most respondents showed their concerns regarding the effectiveness of the vaccine (n = 351, 74.1%), vaccine safety (n = 351, 74.1%), and the halal nature of the vaccine (n = 309, 65.2%). Respondents who were aged 40 to 50 years (odds ratio [OR]: 0.101, 95% CI: 0.38-0.268; P < 0.001), family income > 50,000 PKR (OR: 0.680, 95% CI: 0.321-1.442; P = 0.012), and location (OR: 0.324, 95% CI: 0.167-0.628; P = 0.001) were the factors that were likely to impact vaccine acceptance among parents. Education-based interventions are urgently required to improve COVID-19 vaccination acceptance among parents for their children.

[Exploration and example interpretation of real-world herbal prescription classification based on similarity matching algorithm].

In observational studies, herbal prescriptions are usually studied in the form of "similar prescriptions". At present, the classification of prescriptions is mainly based on clinical experience judgment, but there are some problems in manual judgment, such as lack of unified criteria, labor consumption, and difficulty in verification. In the construction of a database of integrated traditional Chinese and western medicine for the treatment of coronavirus disease 2019(COVID-19), our research group tried to classify real-world herbal prescriptions using a similarity matching algorithm. The main steps include 78 target prescriptions are determined in advance; four levels of importance labeling shall be carried out for the drugs of each target prescription; the combination, format conversion, and standardization of drug names of the prescriptions to be identified in the herbal medicine database; calculate the similarity between the prescriptions to be identified and each target prescription one by one; prescription discrimination is performed based on the preset criteria; remove the name of the prescriptions with "large prescriptions cover the small". Through the similarity matching algorithm, 87.49% of the real prescriptions in the herbal medicine database of this study can be identified, which preliminarily proves that this method can complete the classification of herbal prescriptions. However, this method does not consider the influence of herbal dosage on the results, and there is no recognized standard for the weight of drug importance and criteria, so there are some limitations, which need to be further explored and improved in future research.

Seroprevalence of COVID-19 in Taiwan revealed by testing anti-SARS-CoV-2 serological antibodies on 14,765 hospital patients.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) has become a worldwide pandemic and affected more than 227 countries or territories, resulting in more than 25 million cases with over 0•85 million deaths, as of September 2, 2020. Taiwan has been successful in countering the COVID-19 outbreak, however, the potential risk for asymptomatic infections and the prevalence rates remain unknown. We aimed to estimate the seroprevalence of COVID-19 in Taiwan via serologically testing hospital patients with neither symptoms indicative of nor positive nucleic acid test for SARS-CoV-2 infection. METHODS: Residual specimens from laboratory blood tests for outpatient and emergency department patients visiting a medical centre in Taipei, Taiwan, within one week in May and another in July, 2020, were collected. We used Elecsys Anti-SARS-CoV-2 Assay to screen and further validated cases with high cutoff index by a confirmatory ELISA assay. We also analysed antibody responses against SARS-CoV-2 along disease progression in four nucleic acid test confirmed COVID-19 patients. FINDINGS: Blood samples from a total of 14,765 patients were tested. The unweighted seroprevalence of anti-SARS-CoV-2 antibodies was 0•07% [95% CI, 0•04%-0•13%]; after weighting with the population demographics of Taiwan, the estimated overall seroprevalence was 0•05% [95% CI, 0•02%-0•10%]. Furthermore, based on data of the four COVID-19 cases, the seroconversion dates for IgM were as early as 9 days and that for IgG 11 days after symptoms onset. INTERPRETATION: We screened the anti-SARS-CoV-2 antibodies in a small-scale population-based study and observed an approximately 0•05% seroprevalence of COVID-19, indicating that the current containment protocols emphasising mask wearing, hand washing, social distancing and mandatory quarantine for all incomers are effective in Taiwan. FUNDING: Taipei Veterans General Hospital, Taipei, Taiwan.

The Spatio-Temporal Evolution of Food Production and Self-Sufficiency in China from 1978 to 2020: From the Perspective of Calories.

Ensuring national food security is an eternal topic. We unified six categories of food with calorie content including grain, oil, sugar, fruits and vegetables, animal husbandry, and aquatic products on the basis of provincial-level data, and we dynamically evaluated caloric production capacity and the supply-demand equilibrium under the increase in feed-grain consumption as well as the food losses and waste in China from 1978 to 2020 at four different levels. The results show that: (1) From the perspective of food production, the total national calorie production showed a linear growth trend, with a growth rate of 31.7 × 1012 kcal/year, of which the proportion of grain crops has always exceeded 60%. Most provinces showed significant increasing trends in food calorific production, except for Beijing, Shanghai, and Zhejiang, which showed slightly decreasing trends. The distribution pattern of food calories and their growth rate were high in the east and low in the west. (2) From the perspective of the food supply-demand equilibrium, the national food calorie supply has been in surplus since 1992, but significant spatial heterogeneity is detected, with the Main Marketing Region changing from a tight balance to a short surplus, North China always remaining in calorie shortage, and 15 provinces still presenting supply and demand gaps up to 2020, necessitating the establishment of a more efficient and faster flow and trade system. (3) The national food caloric center has shifted 204.67 km to the northeast, and the population center has shifted to the southwest. The reverse migration of the centers of food supply and demand will further aggravate the pressure on water and soil resources and cause higher requirements for ensuring the circulation and trade system of food supply. The results are of great significance for the timely adjustment of agricultural development policies, making rational use of natural advantages and ensuring China's food security and sustainable agricultural development.

Sequential provocation of Ekbom's syndrome and acute mania following AstraZeneca COVID-19 vaccination.

ChAdOx1 nCoV-19 vaccine (so called AZ vaccine), is widely used to prevent the SARS-CoV-2 pandemic and shows powerful effectiveness to deter community transmission. There are common immunogenicity-related side effects such as fever, myalgia, lethargy, and headache; however, rare report on the neuropsychiatric problems (Ramasamy et al., 2021). In Taiwan, more than 15,200,000 doses of AZ vaccine were injected by the end of 2022. Here we presented a unique case with separated episode of Ekbom's syndrome, also called delusion of parasitosis, and mania following successive AZ vaccination in three-month interval.

A caveat to using wearable sensor data for COVID-19 detection: The role of behavioral change after receipt of test results.

BACKGROUND: Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results. METHODS: We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data. RESULTS: Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity. CONCLUSIONS: Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person's behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.

Clinical characteristics and risk factors for a prolonged length of stay of patients with asymptomatic and mild COVID-19 during the wave of Omicron from Shanghai, China.

BACKGROUND: This study aims to investigate the clinical characteristics and the length of hospital stay (LOS), as well as risk factors for prolonged LOS in a cohort of asymptomatic and mild COVID-19 patients infected with the Omicron variant. METHODS: A total of 1166 COVID-19 patients discharged from the inpatient ward of the largest makeshift hospital (May 8-10, 2022) in Shanghai, China, were included. The demographics, medical history, and the lowest and admission cycle threshold (Ct) values of the RT-PCR tests for SARS-CoV-2 genes of the open reading frame 1ab (Ct-ORF) and the nucleocapsid protein (Ct-N) during hospitalization were recorded. Patients with LOS > 7 days, or LOS ≤ 7 days were included in the Prolonged group or the Control group, separately. The clinical characteristics and LOS of the participants in the two groups were described and compared. Multivariate Logistic and linear regression analyses were applied to explore the risk factors for prolonged LOS. The diagnostic efficacy of the lowest and admission Ct values for the Prolonged group was tested via the receiver operating characteristic (ROC) curve analysis. RESULTS: The median LOS was 6 days in the total study population. The age was older (45.52 ± 14.78 vs. 42.54 ± 15.30, P = 0.001), while both the lowest and admission Ct-ORF (27.68 ± 3.88 vs. 37.00 ± 4.62, P < 0.001; 30.48 ± 5.03 vs. 37.79 ± 3.81, P < 0.001) and Ct-N (25.79 ± 3.60 vs. 36.06 ± 5.39, P < 0.001; 28.71 ± 4.95 vs. 36.95 ± 4.59, P < 0.001) values were significantly lower in the Prolonged group. There were more mild cases in the Prolonged group (23.8% vs. 11.5%, P < 0.001). The symptom spectrum differed between the two groups. In multivariate analyses, age, disease category, and the lowest Ct-N values were shown to be associated with prolonged LOS. Besides, both the lowest and admission Ct-ORF (AUC = 0.911 and 0.873) and Ct-N (AUC = 0.912 and 0.874) showed robust diagnostic efficacy for prolonged LOS. CONCLUSIONS: Our study firstly reports the clinical characteristics and risk factors for prolonged LOS during the wave of the Omicron epidemic in Shanghai, China. These findings provide evidence for the early identification of asymptomatic and mild COVID-19 patients at a high risk of prolonged hospitalization who may require early intervention, and long-term monitoring and management.

Serinc2 deficiency causes susceptibility to sepsis-associated acute lung injury.

BACKGROUND: Severe sepsis and its subsequent complications cause high morbidity and mortality rates worldwide. The lung is one of the most vulnerable organs sensitive to the sepsis-associated inflammatory storm and usually develops into acute respiratory distress syndrome (ARDS)/acute lung injury (ALI). The pathogenesis of sepsis-associated ALI is accompanied by coordinated transmembrane signal transduction and subsequent programmed cell death; however, the underlying mechanism remains largely unclear. RESULTS: Here we find that the expression of serine incorporator 2 (Serinc2), a protein involved in phosphatidylserine synthesis and membrane incorporation, is upregulated in cecal ligation and puncture (CLP)-induced ALI. Furthermore, the Serinc2-knockout (KO) mouse line is generated by the CRISPR-cas9 approach. Compared with wild-type mice, the Serinc2-KO mice exhibit exacerbated ALI-related pathologies after CLP. The expressions of pro-inflammatory factors, including IL1ß, IL6, TNFα, and MCP1, are significantly enhanced by Serinc2 deficiency, concurrent with over-activation of STAT3, p38 and ERK pathways. Conversely, Serinc2 overexpression in RAW264.7 cells significantly suppresses the inflammatory responses induced by lipopolysaccharide (LPS). Serinc2 KO aggravates CLP-induced apoptosis as evidenced by increases in TUNEL-positive staining, Bax expression, and cleaved caspase-3 and decreases in BCL-2 expression and Akt phosphorylation, whereas these changes are suppressed by Serinc2 overexpression in LPS-treated RAW264.7 cells. Moreover, the administration of AKTin, an inhibitor of Akt, abolishes the protective effects of Serinc2 overexpression against inflammation and apoptosis. CONCLUSIONS: Our findings demonstrate a protective role of Serinc2 in the lung through activating the Akt pathway, and provide novel insight into the pathogenesis of sepsis-induced ALI.

Role of air pollutants in dengue fever incidence: evidence from two southern cities in Taiwan.

Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 µm (PM10) or 2.5 µm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.

Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech COVID-19 Vaccine (BNT162b2).

Various adverse events and complications have been attributed to COVID-19 (coronavirus disease 2019) vaccinations, which can affect the cardiovascular system, with conditions such as myocarditis, thrombosis, and ischemia. The aim of this study was to combine noninvasive pulse measurements and frequency domain analysis to determine if the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) vaccination and its accompanying cardiovascular side effects will induce changes in arterial pulse transmission and waveform. Radial blood pressure waveform and photoplethysmography signals were measured noninvasively for 1 min in 112 subjects who visited Shuang-Ho Hospital for a BNT162b2 vaccination. Based on side effects, each subject was assigned to Group N (no side effects), Group CV (cardiac or vascular side effects), Group C (cardiac side effects only), or Group V (vascular side effects only). Two classification methods were used: (1) machine-learning (ML) analysis using 40 harmonic pulse indices (amplitude proportions, phase angles, and their variability indices) as features, and (2) a pulse-variability score analysis developed in the present study. Significant effects on the pulse harmonic indices were noted in Group V following vaccination. ML and pulse-variability score analyses provided acceptable AUCs (0.67 and 0.80, respectively) and hence can aid discriminations among subjects with cardiovascular side effects. When excluding ambiguous data points, the AUC of the score analysis further improved to 0.94 (with an adopted proportion of around 64.1%) for vascular side effects. The present findings may help to facilitate a time-saving and easy-to-use method for detecting changes in the vascular properties associated with the cardiovascular side effects following BNT162b2 vaccination.

Lymphocyte count is a universal predictor of health outcomes in COVID-19 patients before mass vaccination: A meta-analytical study.

Background: Several laboratory data have been identified as predictors of disease severity or mortality in COVID-19 patients. However, the relative strength of laboratory data for the prediction of health outcomes in COVID-19 patients has not been fully explored. This meta-analytical study aimed to evaluate the prediction capabilities of laboratory data on the prognosis of COVID-19 patients during 2020 while mass vaccination has not started yet. Methods: Two electronic databases, MEDLINE and EMBASE, from inception to October 10, 2020 were searched. Observational studies of laboratory-confirmed COVID-19 patients with well-defined severity or survival status, and with the desired laboratory data at initial hospital administrations, were selected. Meta-regression analysis with the generalized estimating equations (GEE) method for clustered data was performed sequentially. Primary outcome measures were to compare the level of laboratory data and their impact on different health outcomes (severe vs non-severe, critically severe vs non-critically severe, and dead vs alive). Results: Meta-data of 13 clinical laboratory items at initial hospital presentations were extracted from 76 selected studies with a total of 26 627 COVID-19 patients in 16 countries. After adjusting for the effect of age, 1.03 0.87 (OR = 0.0576; 95% CI = 0.0043-0.4726; P = 0.0079) had a much lower risk of severity, critical severity, and mortality from COVID-19, respectively. Conclusions: Lymphocyte count was the most powerful predictor among the 13 common laboratory variables explored from COVID-19 patients to differentiate disease severity and to predict mortality. Lymphocyte count should be monitored for the prognoses of COVID-19 patients in clinical settings in particular for patients not fully vaccinated.

A Novel Convolutional Neural Network Model as an Alternative Approach to Bowel Preparation Evaluation Before Colonoscopy in the COVID-19 Era: A Multicenter, Single-Blinded, Randomized Study.

INTRODUCTION: Adequate bowel preparation is key to a successful colonoscopy, which is necessary for detecting adenomas and preventing colorectal cancer. We developed an artificial intelligence (AI) platform using a convolutional neural network (CNN) model (AI-CNN model) to evaluate the quality of bowel preparation before colonoscopy. METHODS: This was a colonoscopist-blinded, randomized study. Enrolled patients were randomized into an experimental group, in which our AI-CNN model was used to evaluate the quality of bowel preparation (AI-CNN group), or a control group, which performed self-evaluation per routine practice (control group). The primary outcome was the consistency (homogeneity) between the results of the 2 methods. The secondary outcomes included the quality of bowel preparation according to the Boston Bowel Preparation Scale (BBPS), polyp detection rate, and adenoma detection rate. RESULTS: A total of 1,434 patients were enrolled (AI-CNN, n = 730; control, n = 704). No significant difference was observed between the evaluation results ("pass" or "not pass") of the groups in the adequacy of bowel preparation as represented by BBPS scores. The mean BBPS scores, polyp detection rate, and adenoma detection rate were similar between the groups. These results indicated that the AI-CNN model and routine practice were generally consistent in the evaluation of bowel preparation quality. However, the mean BBPS score of patients with "pass" results were significantly higher in the AI-CNN group than in the control group, indicating that the AI-CNN model may further improve the quality of bowel preparation in patients exhibiting adequate bowel preparation. DISCUSSION: The novel AI-CNN model, which demonstrated comparable outcomes to the routine practice, may serve as an alternative approach for evaluating bowel preparation quality before colonoscopy.

Setting up a three-stage pre-endoscopy triage during the coronavirus disease 2019 pandemic: A multicenter observational study

Objectives Between May and July 2021, the coronavirus disease 2019 (COVID-19) pandemic led to a sharp surge in community transmission in Taiwan. We present a three-stage restructuring process of pre-endoscopy triage at the beginning of the pandemic, which can support urgent endoscopic procedures while protecting endoscopy staff. Methods The pre-endoscopy triage framework was set up with three checkpoints at the hospital entrance, outpatient department, and endoscopy unit, with a specific target patient population and screening methods. Relevant data included the number of endoscopic procedures performed, outpatient department visits, and performing screening methods such as temperature measurement, travel, occupation, contact, and clustering history checking, polymerase chain reaction assay, and rapid antigen test. Results Forehead temperature measurement and verification of travel, occupation, contact, and clustering history provided rapid, easy, and early mass screening of symptomatic patients at the hospital entrance. During the pandemic, outpatient department visits and endoscopic procedures decreased by 37% and 64%, respectively. The pre-endoscopy screening methods used displayed regional variations in COVID-19 prevalence. Among 16 endoscopy units with a community prevalence of ≥ 31.04 cases per 100,000 residents, 12 (75%) used polymerase chain reaction assay and four (25%) used rapid antigen test to identify asymptomatic patients before endoscopy. Of 6540 pre-endoscopy screening patients, 15 (0.23%) tested positive by laboratory testing. No endoscopy-related nosocomial COVID-19 infections were reported during the pandemic. Conclusions We present a three-stage pre-endoscopy triage based on the local laboratory capacity, medical resources, and community prevalence. These measures could be useful during the COVID-19 pandemic.

CONFIDENCE LEVEL OF A MOTION FEEDBACK SYSTEM FOR ANALYZING EXERCISE TRAINING EFFICACY: APPLICATIONS, BASIS, AND COMMUNICATIONS

The increase in aged population is a global trend. Inculcating healthy behaviors such as regular exercises in the elderly has a significant impact on the financial and medical burden globally. Moreover, air pollution and the outbreak of the coronavirus disease 19 (COVID-19) pose a serious threat to public health. In order to improve the health conditions of the population, this study developed a motion feedback system named MoveV that can be used for several indoor training exercises. This system provides instant motion feedback by synchronizing exercise training videos on the website using a motion analysis algorithm that is applicable on smartphones, and a cloud database platform is used to record health behaviors. Feature extraction is performed based on force intensity, motion velocity, and exercise direction. The resultant accuracy of the motion feedback system was tested by a motion science expert and presented as the confidence level. For perfect movement, a confidence level of up to 90.5% was achieved, indicating that the MoveV system was able to record users’ exercise frequency and distinguish whether the user was performing well in the exercise movements. The proposed system is convenient and does not incur additional expenditure by purchasing any new device. Furthermore, it provides visual and voice feedback, companionship, and exercise motivation to the users, all of which are important factors when using online exercise platforms.

Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression.

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.

Real-world Evaluation of a Sample Pooling Strategy for Large-Scale Rapid COVID-19 Testing.

BACKGROUND: The worldwide outbreak of COVID-19 has become a public health crisis of unprecedented proportions. The fast spread of emerging variants increases the needs of rapid diagnostic and screening testing. Sample pooling efficiently expands the testing capacity under limited resources. OBJECTIVES: We evaluated the performance of sample pooling on the Point-of-Care (POC) Liat® and cobas® 6800 systems and provided real-world experiences for implementing these systems in large-scale screenings. METHODS: Positive nasopharyngeal (NP) specimens with Ct values < 25, 25∼30 or > 30 were tested individually and in pools to optimize the POC Liat® and cobas® 6800 systems, which were then implemented in community screenings. RESULTS: The 5-sample pooling strategy did not affect the positive detection rates on Liat® or cobas® 6800 in samples with Ct values <25 or 25∼30. However, in samples with low viral loads (Ct values >30), five-sample pooling has a higher positive detection rate on POC Liat® (20/20; 100%), compared to cobas® 6800 (9/20; 45%). Five-sample pooled on POC Liat® and two-sample pooled on cobas® 6800 appear to be appropriate for SARS-CoV-2 detection. By implementing the pooling strategies in two large-scale community screenings, 7,606 NP specimens was tested within 36 h; the average turn-around time was 4.8 h for cobas® 6800 and 1.3 h for POC Liat®. Eight positive specimens (0.11%; 8/7,606) were identified, with Ct values ranging from 18.85 to 37.68. CONCLUSION: The performance of sample pooling on POC Liat® was demonstrated to be an effective, accurate, and economical approach for large-scale community screenings for COVID-19.

Parental Preferences of Influenza Vaccination for Children in China: A National Survey with a Discrete Choice Experiment.

The influenza vaccination coverage among children is low in China. We aimed to conduct a nationwide survey to quantify parental preferences and willingness to pay (WTP) for influenza vaccination for their children. Parents with children aged six months to 18 years from six provinces in China were investigated by a discrete choice experiment regarding six influenza vaccination attributes. Mixed logit models were used to estimate the relative importance of vaccine attributes and parents' WTP. Interaction analysis and subgroup analysis were conducted to explore preference heterogeneity. A total of 1206 parents were included in the analysis. Parents reported vaccine effectiveness as the most important vaccine attribute. The mode of vaccine administration had no significant impact on parents' preferences. Parents aged over 30 years with higher education or income levels were more likely to prefer no influenza vaccination for their children. The largest marginal WTP (CNY 802.57) for vaccination and the largest increase in vaccine uptake (41.85%) occurred with improved vaccine effectiveness from 30% to 80%. Parents from central regions or mid-latitude areas had a relatively lower WTP than those from other regions. No significant difference in the relative importance of vaccine attributes were observed among parents from various regions of China.