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BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed a great challenge to the healthcare system. This study evaluated the impact of the pandemic on the utilization of primary healthcare (PHC). METHODS: The outpatient data from 158 PHC institutions in Yinchuan from May 1, 2017 to April 30, 2020 were used. The difference in difference (DID) model was used to analyze the difference in the number of outpatient visits per day, total outpatient expenditure per day, and outpatient expenditure per visit between December 2019 and February 2020 compared with the same periods in two previous years. The autoregressive integrated moving average (ARIMA) modelling was used to investigate the association between the outpatient volume and the number of the last week's new COVID-19 cases in Yinchuan, Ningxia, and China. RESULTS: From December 2019 to February 2020, the decline in the number of outpatient visits per day (DID: -367.21 times, P=.004) was larger than that in two previous years, and a similar trend can be seen in the outpatient expenditure per day. However, the rise in the outpatient expenditure per visit (DID: 19.06 thousand yuan, P=.003) was larger than that in two previous years. In 2020, the outpatient visits for most types of diseases decreased from week 3 and rebounded after week 5. The decline and rebound of outpatient visits in the population aged 45 years and older were steeper than in those younger. The outpatient volume was negatively associated with the number of the last week's new COVID-19 cases. CONCLUSION: This study indicated a significant impact of the pandemic on PHC service utilization. Since PHC service is the foundation of the healthcare system in most developing countries, measures should be taken to make PHC help cope with the crisis and relieve the burden of hospital care.
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The pandemic of coronavirus disease 2019 (COVID-19) has threatened the social and economic structure all around the world. Generally, COVID-19 has three possible transmission routes, including pre-symptomatic, symptomatic and asymptomatic transmission, among which the last one has brought a severe challenge for the containment of the disease. One core scientific question is to understand the influence of asymptomatic individuals and of the strength of control measures on the evolution of the disease, particularly on a second outbreak of the disease. To explore these issues, we proposed a novel compartmental model that takes the infection of asymptomatic individuals into account. We get the relationship between asymptomatic individuals and critical strength of control measures theoretically. Furthermore, we verify the reliability of our model and the accuracy of the theoretical analysis by using the real confirmed cases of COVID-19 contamination. Our results, showing the importance of the asymptomatic population on the control measures, would provide useful theoretical reference to the policymakers and fuel future studies of COVID-19.
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Background: The appropriateness of antibiotic prescriptions in primary care has not been well evaluated in China in recent years. Furthermore, the impact of coronavirus disease 2019 (COVID-19) on antibiotic prescriptions has not yet been investigated in China. We aimed to assess the appropriateness of antibiotic prescriptions and to evaluate the potential association between the COVID-19 pandemic and antibiotic prescriptions in primary care settings of Yinchuan, a city in China. Methods: This study included 155 primary care institutions and 10,192,713 outpatient visits. Outpatient prescriptions were classified as appropriate, potentially appropriate, inappropriate, or not linked to any diagnosis for antibiotic use following a validated evaluation scheme. Interrupted time-series analyses were performed to assess the effects of the COVID-19 pandemic on antibiotic prescriptions in Chinese primary care facilities. Results: During the study period, 1,287,678 (12.6%, 95% confidence interval [12.6-12.7]) of 10,192,713 outpatient visits in primary care resulted in antibiotic prescriptions. Among 1,287,678 antibiotic prescriptions, 653,335 (50.7% [50.6-50.9]) were inappropriate, 463,081 (36.0% [35.8-36.1]) were potentially appropriate, 171,056 (13.3% [13.1-13.5]) were appropriate, and 206 could not be linked to any diagnosis. Furthermore, patient, physician, and institutional factors were associated with inappropriate antibiotic prescriptions; there was an overall decreasing trend in the proportions of inappropriate antibiotic prescriptions, with the highest level in 2017 (67.1% [66.8-67.5]) and the lowest in 2021 (40.8% [40.3-41.3]). A total of 1,416,120 individual antibiotics were prescribed, of which 1,087,630 (76.8%) were broad-spectrum and 777,672 (54.9%) were classified in the World Health Organization's "Watch" category. In addition, the COVID-19 pandemic was associated with changes of -2.8% (-4.4 to -1.3) in the level and 0.3% (0.2-0.3) in the monthly trend of antibiotic prescription rates, as well as changes of -5.9% (-10.2 to -1.5) in the level and 1.3% (1.0-1.6) in the monthly trend of the proportions of inappropriate antibiotic prescriptions. Conclusion: More than half of the antibiotic prescriptions were inappropriate during the study period in primary care in Yinchuan. The COVID-19 pandemic may be associated with a decrease in the overall and inappropriate use of antibiotics in primary care settings in China.
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Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application. Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool). Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome. Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.
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Esophageal Neoplasms , HumansABSTRACT
• Proposing a coevolution model for resource allocation and epidemic spreading on metapopulation network. • Develop a mathematical framework to analyze the dynamical system and obtain the epidemic threshold concerning external factors. • The disease can be controlled effectively when resources are allocated unbiased. • There exists an appropriate small value of mobility rate that is propitious to control the disease through numerical analysis and simulations. A practical resource allocation strategy is the prerequisite for disease control during a pandemic affected by various external factors, such as the information about the epidemic state, the interregional population mobility, and the geographical factors. Understanding the influence of these factors on resource allocation and epidemic spreading is the premise for designing an optimal resource allocation strategy. To this end, we study the interaction of resource allocation and epidemic spreading in the scope of the metapopulation model by incorporating the factors of geographic proximity, the information of the epidemic state, the willingness of resource allocation, and the population mobility simultaneously. We develop a mathematical framework based on the Markovian chain approach to analyze the dynamical system and obtain the epidemic threshold concerning external factors. Combining extensive Monte Carlo simulations, we find that the disease can be controlled effectively when resources are allocated unbiased in terms of the geographical factor during a pandemic. Specifically, the spreading size is the lowest, and the epidemic threshold is the largest when resources are allocated unbiasedly between neighbor nodes and other nodes. In addition, when studying the effects of resource allocation on the epidemic threshold, we find the same results, i.e., information-aware resource allocation with unbiased in terms of the geographical factor will raise the epidemic threshold. At last, we study the effects of mobility rate on the dynamical property and find an appropriate small value of mobility rate that is propitious to control the disease through numerical analysis and simulations. Our findings will have a direct application in the development of strategies to suppress the spread of the disease and guide the behavior of individuals during a pandemic. [ABSTRACT FROM AUTHOR] Copyright of Applied Mathematics & Computation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic has lasted for nearly 4 months by this study was conducted. We aimed to describe drug utilization, disease progression, and adverse drug events of COVID-19. METHODS: A retrospective, single-center case series study enrolled 165 consecutive hospitalized COVID-19 patients who were followed up until March 25, 2020, from a designated hospital in Wuhan. Patients were grouped by a baseline degree of severity: non-severe and severe. An analytical study of drug utilization, disease progression, and adverse events (AEs) of COVID-19 was conducted. RESULTS: Of the 165 COVID-19 cases, antivirals, antibacterials, glucocorticoids, and traditional Chinese medicine (TCM) were administered to 92.7%, 98.8%, 68.5%, and 55.2% of patients, respectively. The total kinds of drugs administered to the severe subgroup [26, interquartile range (IQR) 18-39] were 11 more than the non-severe subgroup (15, IQR 10-24), regardless of comorbidities. The 2 most common combinations of medications in the 165 cases were 'antiviral therapy + glucocorticoids + TCM' (81, 49.1%) and 'antiviral therapy + glucocorticoids' (23, 13.9%). Compared with non-severe cases, severe cases received more glucocorticoids (88.5% vs. 66.2%, P=0.02), but less TCM (50.0% vs. 63.3%, P=0.20), and suffered a higher percentage of death (34.6% vs. 7.2%, P=0.001). At the end of the follow-up, 130 (78.8%) patients had been discharged, and 24 (14.5%) died. There were 13 patients (7.9%) who had elevated liver enzymes, and 49 patients (29.7%) presented with worsening kidney function during the follow-up. CONCLUSIONS: Of the 165 COVID-19 patients, the fatality rate remained high (14.5%). Drug utilization for COVID-19 was diverse and generally complied with the existing guidelines. Combination regimens containing antiviral drugs might be beneficial to assist COVID-19 recovery. Additionally, liver and kidney AEs should not be ignored.