This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
Kawasaki disease (muco-cutaneous lymph node syndrome, MCLS) was first reported by Kawasaki in 1967. It was characteried by nonvascular polymorphous rash, fever, ulcer in oral mucosa, edema of hands of feet, cervix lym...Kawasaki disease (muco-cutaneous lymph node syndrome, MCLS) was first reported by Kawasaki in 1967. It was characteried by nonvascular polymorphous rash, fever, ulcer in oral mucosa, edema of hands of feet, cervix lymphadenopathy and desquamations in peripheral extremities. Because of unknown pathogens, no lab examination was available as a single easily recognized diagnostic marker; the diagnosis is展开更多
This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and trainin...This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and training.A total of 208 laboratory staff from seven provincial CDCs were chosen by stratified random sampling to undergo a self-administered questionnaire survey.The collected data were analyzed using SPSS 22.0.The overall average biosafety awareness of the CDC laboratory staff involved in pathogen detection was 82.5 points.The average biosafety awareness score was the highest in health monitoring(92.63 points)and the lowest in risk assessment and control(41.6 points).Among the seven provincial CDCs,the Guizhou Provincial Center for Disease Control and Prevention laboratory staff showed the lowest average biosafety awareness score(74.3 points).The team who worked for 5–14 years were more likely to score above the passing score(≥88 points;corrected OR 0.400,95%CI:0.168–0.951)than the staff with less than five years of work experience.In addition,the mid-level and junior-level staff,as well as the lower position ones were more likely to score below the passing score(<88 points;junior level and lower:corrected OR 3.473,95%CI:1.028–11.737;mid-level:corrected OR 2.797,95%CI:1.027–7.618)compared with the senior-level staff.Among the surveyed team,66.3%identified a lack of specific funds related to work,61.5%identified a lack of designated personnel for the laboratory biosafety management.The biosafety awareness in CDC laboratory staff involved in pathogen detection is low,especially regarding risk assessment and control.The biosafety awareness in Guizhou and Guangxi Provincial CDC laboratory staff is also low.Laboratory funding,job title,and years of experience in a laboratory influence biosafety awareness in CDC laboratory staff.The biosafety knowledge,education,and training of CDC laboratory staff involved in pathogen detection need to improve by paying attention to the content and coverage of biosafety training,exploring new training modalities,and increasing funding for activities related to biosafety in CDC.展开更多
Objective:To explore the role of the Centers for Disease Control and Prevention(CDC)in public health emergencies.Methods:The details of 12 public health events that occurred between January 2021 to December 2022 were ...Objective:To explore the role of the Centers for Disease Control and Prevention(CDC)in public health emergencies.Methods:The details of 12 public health events that occurred between January 2021 to December 2022 were analyzed to explore the roles of the CDC.Results:There were 160 patients involved in 10 public health events in 2021 and 48 patients involved in 2 public health events in 2022.Besides,the proportion of school public health events in 2022 was 0%,which was lower than in 2021,which was 80%(P<0.05).99.38%of patients during public health events were sent to the hospital promptly in 2022,which was higher than that in 2021,which was 81.25%(P<0.05).Furthermore,the average time taken for the CDC to control public health events in 2022 was 20.11±1.62 hours,and the average time taken to send inspection reports was shorter than that in 2021.The public satisfaction score was also higher in 2022 compared to 2021(P<0.05).Conclusion:The role of the CDC is to control infectious diseases.Therefore,it is important to pinpoint the existing problems in the strategies implemented by the CDC so that more improvements can be made to better prevent infectious diseases.展开更多
In 2019, an outbreak of Mycoplasma pneumoniae(M. pneumoniae) occurred at a military academy in China. The attack rate(10.08%, 60/595) was significantly different among the units. High-intensity training and crowded en...In 2019, an outbreak of Mycoplasma pneumoniae(M. pneumoniae) occurred at a military academy in China. The attack rate(10.08%, 60/595) was significantly different among the units. High-intensity training and crowded environments to which cadets are exposed are the high risk factors for the outbreak of M. pneumoniae. In-time prevention and control measures effectively controlled the spread of the epidemic.展开更多
Background: In 2012, U.S. health care providers wrote more than 259 million opioid prescriptions, which is twice as many as in 1998. Approximately 1 in 10 women report the use of opioids for pain management during pre...Background: In 2012, U.S. health care providers wrote more than 259 million opioid prescriptions, which is twice as many as in 1998. Approximately 1 in 10 women report the use of opioids for pain management during pregnancy. The Centers for Disease Control and Prevention (CDC) estimated that between 2008 and 2012, 39% of reproductive-aged women on Medicaid had filled a prescription for opioid medication each year, as did 28% of women with private insurance. The opioid epidemic extends to the state of New Jersey (NJ);however, limited data is available regarding opioid prescriptions among Medicaid and private insurance patients within the state. Objective: Evaluate opioid prescriptions filled in reproductive-aged women presenting in labor at a community teaching hospital in suburban New Jersey. Methods: We performed a retrospective cohort study using data obtained from patient records and the New Jersey Prescription Monitoring Program (NJPMP) database. We enrolled 200 patients that were admitted in labor between May 2015 and May 2016. Data was collected from reproductive-aged women during the one year preceding labor admission. We compared our findings to national data reported by the CDC using Chi-square analysis. Maternal demographic data were extracted from patient records and included age, insurance status (private insurance, Medicaid, and no insurance), race, and ethnicity. The primary outcome was opioid prescriptions filled. Results: Of the 200 women admitted in labor, 129 had private insurance, 63 had Medicaid, and 8 had no insurance. We found that 5.4% (7/129) of patients with private insurance, 4.8% (3/63) of patients with Medicaid, and 12.5% (1/8) of patients with no insurance filled opioid prescriptions. Overall, 5.5% (11/200) of women filled opioid prescriptions during the study period. Opioid prescriptions confirmed via NJPMP were significantly lower than rates reported by the CDC in Medicaid (4.8% vs. 41.4%, p-value 0.001) and private insurance (5.4% vs. 29.1%, p-value < 0.001) patients, respectively. Conclusion: Rates of opioid prescriptions filled were lower among our suburban cohort of women in New Jersey than national rates reported by the CDC. We did not confirm that patients with Medicaid filled more prescriptions than patients with private insurance. These discrepancies raise the question of whether a federal prescription monitoring program would better capture data than state-wide programs. Further research is needed to ensure that prescription monitoring programs are actually capturing accurate data.展开更多
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
文摘Kawasaki disease (muco-cutaneous lymph node syndrome, MCLS) was first reported by Kawasaki in 1967. It was characteried by nonvascular polymorphous rash, fever, ulcer in oral mucosa, edema of hands of feet, cervix lymphadenopathy and desquamations in peripheral extremities. Because of unknown pathogens, no lab examination was available as a single easily recognized diagnostic marker; the diagnosis is
基金supported by grants from the Establishment of Technical Simulation Training Platform for High-Level Biosafety Laboratory(A3705011905-23-2).
文摘This study aims to evaluate the biosafety awareness of laboratory staff working on pathogens detection in seven Centers for Disease Control and Prevention and support these staff's biosafety management and training.A total of 208 laboratory staff from seven provincial CDCs were chosen by stratified random sampling to undergo a self-administered questionnaire survey.The collected data were analyzed using SPSS 22.0.The overall average biosafety awareness of the CDC laboratory staff involved in pathogen detection was 82.5 points.The average biosafety awareness score was the highest in health monitoring(92.63 points)and the lowest in risk assessment and control(41.6 points).Among the seven provincial CDCs,the Guizhou Provincial Center for Disease Control and Prevention laboratory staff showed the lowest average biosafety awareness score(74.3 points).The team who worked for 5–14 years were more likely to score above the passing score(≥88 points;corrected OR 0.400,95%CI:0.168–0.951)than the staff with less than five years of work experience.In addition,the mid-level and junior-level staff,as well as the lower position ones were more likely to score below the passing score(<88 points;junior level and lower:corrected OR 3.473,95%CI:1.028–11.737;mid-level:corrected OR 2.797,95%CI:1.027–7.618)compared with the senior-level staff.Among the surveyed team,66.3%identified a lack of specific funds related to work,61.5%identified a lack of designated personnel for the laboratory biosafety management.The biosafety awareness in CDC laboratory staff involved in pathogen detection is low,especially regarding risk assessment and control.The biosafety awareness in Guizhou and Guangxi Provincial CDC laboratory staff is also low.Laboratory funding,job title,and years of experience in a laboratory influence biosafety awareness in CDC laboratory staff.The biosafety knowledge,education,and training of CDC laboratory staff involved in pathogen detection need to improve by paying attention to the content and coverage of biosafety training,exploring new training modalities,and increasing funding for activities related to biosafety in CDC.
文摘Objective:To explore the role of the Centers for Disease Control and Prevention(CDC)in public health emergencies.Methods:The details of 12 public health events that occurred between January 2021 to December 2022 were analyzed to explore the roles of the CDC.Results:There were 160 patients involved in 10 public health events in 2021 and 48 patients involved in 2 public health events in 2022.Besides,the proportion of school public health events in 2022 was 0%,which was lower than in 2021,which was 80%(P<0.05).99.38%of patients during public health events were sent to the hospital promptly in 2022,which was higher than that in 2021,which was 81.25%(P<0.05).Furthermore,the average time taken for the CDC to control public health events in 2022 was 20.11±1.62 hours,and the average time taken to send inspection reports was shorter than that in 2021.The public satisfaction score was also higher in 2022 compared to 2021(P<0.05).Conclusion:The role of the CDC is to control infectious diseases.Therefore,it is important to pinpoint the existing problems in the strategies implemented by the CDC so that more improvements can be made to better prevent infectious diseases.
基金supported by the military medical innovation research project of PLAGH (CX19015)program for military medical innovation (18CXZ038)。
文摘In 2019, an outbreak of Mycoplasma pneumoniae(M. pneumoniae) occurred at a military academy in China. The attack rate(10.08%, 60/595) was significantly different among the units. High-intensity training and crowded environments to which cadets are exposed are the high risk factors for the outbreak of M. pneumoniae. In-time prevention and control measures effectively controlled the spread of the epidemic.
文摘Background: In 2012, U.S. health care providers wrote more than 259 million opioid prescriptions, which is twice as many as in 1998. Approximately 1 in 10 women report the use of opioids for pain management during pregnancy. The Centers for Disease Control and Prevention (CDC) estimated that between 2008 and 2012, 39% of reproductive-aged women on Medicaid had filled a prescription for opioid medication each year, as did 28% of women with private insurance. The opioid epidemic extends to the state of New Jersey (NJ);however, limited data is available regarding opioid prescriptions among Medicaid and private insurance patients within the state. Objective: Evaluate opioid prescriptions filled in reproductive-aged women presenting in labor at a community teaching hospital in suburban New Jersey. Methods: We performed a retrospective cohort study using data obtained from patient records and the New Jersey Prescription Monitoring Program (NJPMP) database. We enrolled 200 patients that were admitted in labor between May 2015 and May 2016. Data was collected from reproductive-aged women during the one year preceding labor admission. We compared our findings to national data reported by the CDC using Chi-square analysis. Maternal demographic data were extracted from patient records and included age, insurance status (private insurance, Medicaid, and no insurance), race, and ethnicity. The primary outcome was opioid prescriptions filled. Results: Of the 200 women admitted in labor, 129 had private insurance, 63 had Medicaid, and 8 had no insurance. We found that 5.4% (7/129) of patients with private insurance, 4.8% (3/63) of patients with Medicaid, and 12.5% (1/8) of patients with no insurance filled opioid prescriptions. Overall, 5.5% (11/200) of women filled opioid prescriptions during the study period. Opioid prescriptions confirmed via NJPMP were significantly lower than rates reported by the CDC in Medicaid (4.8% vs. 41.4%, p-value 0.001) and private insurance (5.4% vs. 29.1%, p-value < 0.001) patients, respectively. Conclusion: Rates of opioid prescriptions filled were lower among our suburban cohort of women in New Jersey than national rates reported by the CDC. We did not confirm that patients with Medicaid filled more prescriptions than patients with private insurance. These discrepancies raise the question of whether a federal prescription monitoring program would better capture data than state-wide programs. Further research is needed to ensure that prescription monitoring programs are actually capturing accurate data.
