ABSTRACT
Medical waste deserves particular attention due to its potential for causing serious damage to people and the environment. Although the factors influencing the generation of medical waste are critical for designing policies aimed at effectively reducing medical waste and improving medical waste management, they have not been extensively studied. The rapid development of China's medical and health services and the sudden outbreak of Covid-19 have brought significant challenges to managing medical waste in China. Therefore, based on panel data from eight cities in China from 2013 to 2019, this study used a fixed-effects model to investigate the influencing factors of medical waste generation (MWG) in China, and tested the environmental Kuznets curve (EKC) hypothesis. The results show that there is a non-linear N-shaped curve relationship between MWG and per capita gross domestic product (GDP);MWG will continue to increase with economic growth, but the growth rate will slow down from fast to slow, and then from slow to fast with economic growth. The analysis also reveals that implementing a tiered diagnosis and treatment policy may negatively affect MWG by reducing the waste of medical resources and thus reducing the generation of medical waste. The positive effect of population size on MWG is also highly significant, so when the aging of the population increases, the generation of medical waste also increases. The three policy suggestions are provided: 1) improve the disposal capacity and efficiency of medical waste;2) give full play to the advantages of hierarchical diagnosis and treatment policy;3) improve the management level of medical waste in primary medical institutions. © 2023
ABSTRACT
Medical waste deserves particular attention due to its potential for causing serious damage to people and the environment. Although the factors influencing the generation of medical waste are critical for designing policies aimed at effectively reducing medical waste and improving medical waste management, they have not been extensively studied. The rapid development of China's medical and health services and the sudden outbreak of Covid-19 have brought significant challenges to managing medical waste in China. Therefore, based on panel data from eight cities in China from 2013 to 2019, this study used a fixed-effects model to investigate the influencing factors of medical waste generation (MWG) in China, and tested the environmental Kuznets curve (EKC) hypothesis. The results show that there is a non-linear N-shaped curve relationship between MWG and per capita gross domestic product (GDP);MWG will continue to increase with economic growth, but the growth rate will slow down from fast to slow, and then from slow to fast with economic growth. The analysis also reveals that implementing a tiered diagnosis and treatment policy may negatively affect MWG by reducing the waste of medical resources and thus reducing the generation of medical waste. The positive effect of population size on MWG is also highly significant, so when the aging of the population increases, the generation of medical waste also increases. The three policy suggestions are provided: 1) improve the disposal capacity and efficiency of medical waste;2) give full play to the advantages of hierarchical diagnosis and treatment policy;3) improve the management level of medical waste in primary medical institutions.
ABSTRACT
Background: During the prevention and control of the COVID-19, the setting of fever clinic in primary healthcare institutions is an important experience summary and pilot practice. At present, the pilot sentinel surveillance has been carried out for more than one year, and it is necessary to analyze its operation situation. Objective: To investigate the setting and utilization of fever clinic in primary healthcare institutions, summarize experience and put forward corresponding suggestions. Methods: From 2021-02-22 to 2021-03-02, a multi-stage sampling method was adopted to select primary healthcare institutions(community healthcare centers/township health centers) who participated in the prevention and control of the COVID-19 epidemic to conduct a questionnaire survey(including fever sentinel surveillance clinics set up during epidemic prevention and fever clinics set up before epidemic prevention), cumulative use time, cumulative number of visits, cumulative referral rate, and cumulative number of diagnoses. Results: 718 valid responses from 408 primary healthcare institutions were totally obtained. Among them, 208 institutions(51.0%) set up fever sentinel surveillance clinic during the prevention and control of the COVID-19 epidemic, 91 institutions(22.3%) had set up before the prevention and control of the COVID-19 epidemic, and 109 institutions(26.7%) had not set up. 271 subjects participated in the work of the fever sentinel surveillance clinic. According to the 271 questionnaires: as the end of January 2021, the median cumulative use time of the fever sentinel surveillance clinic is 12(10) months;188 cases(69.4%) of the respondents indicated that the cumulative number of visits was <300, and 45 cases(16.6%) indicated that the cumulative number of visits was >1 000;184(67.9%) respondents indicated that the cumulative referral rate was <3%, and 35(12.9%) respondents indicated that the cumulative referral rate was >70%;238(87.8%) of the respondents indicated that the number of patients who were finally diagnosed with novel coronavirus pneumonia was 0 out of all the patients admitted. The test results of χ2 showed that the cumulative number of visits between the setting of the fever clinic during the prevention and control of the novel coronavirus pneumonia epidemic and the setting of the fever clinic before the epidemic prevention and control is statistically significant(P<0.05);However, there is no statistically significant difference in the cumulative number of visits, the cumulative referral rate, and the cumulative number of diagnoses in fever clinic in different regions(including economic zone, geographical location within the city, and the highest risk level during the epidemic period of the region). The results of Spearman rank correlation analysis showed that the setting time of fever clinics was significantly positively correlated with cumulative use time and cumulative visits(rs values were 0.37, 0.18, P<0.05), and the region(east, central, and western) was significantly positively correlated with cumulative diagnoses(rs=0.13, P<0.05). Conclusion: According to the survey, more than two thirds of primary healthcare institutions in China have set up fever sentinel surveillance clinics/fever clinics, and of which no difference were indicated in the cumulative visit number of febrile patients, cumulative referral rate and cumulative number of confirmed COVID-19 infections based on the different economic zones, geographical locations and regions with different highest risk level. The higher cumulative number of visits to fever clinics than fever sentinel surveillance clinics demonstrated the potential capacity of managing patients with fever during the pandemic of infectious diseases. Copyright © 2021 by the Chinese General Practice.