contact screening
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Author(s):  
Chunjiao Dong ◽  
Yixian Qiao ◽  
Chunheng Shang ◽  
Xiwen Liao ◽  
Xiaoning Yuan ◽  
...  

2021 ◽  
Vol 6 (3) ◽  
pp. 167
Author(s):  
Kiran Chawla ◽  
Sharath Burugina Nagaraja ◽  
Nayana Siddalingaiah ◽  
Chidananda Sanju ◽  
Vishnu Prasad Shenoy ◽  
...  

Background: In India, challenges in pediatric TB contact screening and chemoprophylaxis initiation are still underexplored. Elucidating these challenges will help in better implementation of the programme at the grass-roots level thereby helping in early detection of pediatric cases and timely initiation of preventive therapy. This study aimed at exploring the challenges faced by the health care provider in contact screening and chemoprophylaxis initiation implementation of the pediatric household contacts. Methods: A qualitative study was conducted in the districts of Bengaluru and Udupi and in-depth interviews of key participants were adopted to explore the challenges. Qualitative data analysis was done after developing transcripts by generating themes and codes. Results: The key challenges were identified as stigma towards the disease, migrant patients with changing address, difficulty in sample collection, anxiety among parents due to long duration of the prophylactic treatment and adherence to IPT is not well documented, inadequate transportation from rural areas, and the ongoing COVID-19 pandemic. Conclusions: It is important for the National TB programme to address these challenges efficiently and effectively. Innovative solutions, feasible engagements, and massive efforts are to be taken by the programme to improve contact screening and isoniazid chemoprophylaxis implementation.


2021 ◽  
Vol 15 (9) ◽  
pp. e0009640
Author(s):  
Kedir Urgesa ◽  
Kidist Bobosha ◽  
Berhanu Seyoum ◽  
Fitsum Weldegebreal ◽  
Adane Mihret ◽  
...  

Leprosy or Hansen’s disease is a disabling infectious disease caused by Mycobacterium leprae. Reliance on the self-presentation of patients to the health services results in many numbers of leprosy cases remaining hidden in the community, which in turn results in a longer delay of presentation and therefore leading to more patients with disabilities. Although studies in Ethiopia show pockets of endemic leprosy, the extent of hidden leprosy in such pockets remains unexplored. This study determined the magnitude of hidden leprosy among the general population in Fedis District, eastern Ethiopia. A community-based cross-sectional study was conducted in six randomly selected leprosy-endemic villages in 2019. Health extension workers identified study participants from the selected villages through active case findings and household contact screening. All consenting individuals were enrolled and underwent a standardized physical examination for diagnosis of leprosy. Overall, 262 individuals (214 with skin lesions suspected for leprosy and 48 household contacts of newly diagnosed leprosy cases) were identified for confirmatory investigation. The slit skin smear technique was employed to perform a bacteriological examination. Data on socio-demographic characteristics and clinical profiles were obtained through a structured questionnaire. Descriptive statistics and binary logistic regression were used to assess the association between the outcome variable and predictor variables, and the P-value was set at 0.05. From the 268 individuals identified in the survey, 6 declined consent and 262 (97.8%) were investigated for leprosy. Fifteen cases were confirmed as leprosy, giving a detection rate of 5.7% (95%, CI: 3%, 9%). The prevalence of hidden leprosy cases was 9.3 per 10,000 of the population (15/16107). The majority (93.3%) of the cases were of the multi-bacillary type, and three cases were under 15 years of age. Three cases presented with grade II disability at initial diagnosis. The extent of hidden leprosy was not statistically different based on their sex and contact history difference (p > 0.05). High numbers of leprosy cases were hidden in the community. Active cases findings, and contact screening strategies, play an important role in discovering hidden leprosy. Therefore, targeting all populations living in leprosy pocket areas is required for achieving the leprosy elimination target.


2021 ◽  
Vol 104 ◽  
pp. 634-640
Author(s):  
Mahboob Ul Haq ◽  
Sven G. Hinderaker ◽  
Razia Fatima ◽  
Hemant Deepak Shewade ◽  
Einar Heldal ◽  
...  

2021 ◽  
Author(s):  
Chunheng Shang ◽  
Yixian Qiao ◽  
Xiwen Liao ◽  
Xiaoning Yuan ◽  
Qin Cheng ◽  
...  

BACKGROUND COVID-19 is a new infectious disease with high infectivity. At present, body temperature detection is the main method for primary screening, but this single detection method has poor accuracy and is easy to miss detection. OBJECTIVE The objective of our study was to propose a non-contact, high-precision COVID-19 screening system. METHODS We used impulse-radio ultra-wideband (IR-UWB) radar to detect the respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital, and compared them with 144 radar monitoring data of healthy controls. Then XGBoost and logistic regression(XGBoost+LR) algorithm was used to classify the data of patients and healthy people; feature selection was performed by SHAP value; using ten-fold cross-validation, XGBoost+LR algorithm was compared with five other classic classification algorithms, and the classification performance was evaluated by precision, recall, and the area under the ROC curve( AUC ). RESULTS The XGBoost+LR algorithm demonstrate excellent discrimination (precision=99.1 %, recall rate = 94.1 %, AUC=98.7 %), which is superior to several other single machine learning algorithms. In addition, the SHAP value indicate that number of apnea during REM(‘ REMSATims’) and mean heart rate(‘meanHR’) are important features for classification. CONCLUSIONS The COVID-19 non-contact screening system based on XGBoost+LR algorithm can accurately predict COVID-19 patients and can be applied in isolation wards to effectively help medical staff.


PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0240031 ◽  
Author(s):  
Said Mirza Sayedi ◽  
Mohammad Khaled Seddiq ◽  
Mohammad K. Rashidi ◽  
Ghulam Qader ◽  
Naser Ikram ◽  
...  

2020 ◽  
Vol 41 (S1) ◽  
pp. s147-s148
Author(s):  
Alice Kanyua ◽  
Rose Ngugi ◽  
Loice Ombajo ◽  
Joyce Mwangi ◽  
Bolivya Olasya ◽  
...  

Background:Candida auris is an emerging pathogen associated with nosocomial outbreaks. During January to May 2019, 11 invasive cases of C. auris were identified in the intensive care unit (ICU) and high-dependency unit (HDU) at a hospital in Nairobi, Kenya. We report on the interventions implemented to control the outbreak. Methods: Intensified infection prevention and control (IPC) interventions were implemented. All patients infected or colonized with C. auris were placed in single-patient rooms with strict contact precautions. Cleaning of the patient care environment was enhanced by instituting a 3-step procedure of cleaning with soap and water, disinfecting with 0.5% chlorine, and rinsing with water. Glo-Germ gel was used to evaluate the cleaning processes, and percentage of missed surfaces was calculated. Hand hygiene training and compliance observations were conducted to enforce adherence to hand hygiene. The IPC team provided training and observational feedback of IPC to staff, patients, and their families. The IPC interventions were guided by screening activities. To monitor ongoing transmission, a biweekly point-prevalence survey (PPS) was performed to screen all previously negative ICU and HDU patients for C. auris. Furthermore, admission and contact screening were added to guide patient placement. Screening was conducted by collecting a composite swab from the bilateral axilla and groin. Samples were incubated in salt dulcitol broth for 5 days at 40°C then subcultured onto Sabouraud dextrose agar. Colony identification was performed using a Vitek 2 system (bioMérieux). Results: In total, 177 patients were placed in single-patient rooms under contact precautions during May–August 2019. We conducted 123 environmental cleaning observations, and the percentage of missed surfaces decreased from 71% (10 of 14) in June to 7% (1 of 16) in August. Hand hygiene compliance among ICU and HDU staff was 79% (204 of 257) in May, 71% (159 of 223) in June, 73% (170 of 233) in July, and 81% (534 of 657) in August. In total, 283 screening swabs from 234 patients were processed during May–August 2019. Overall, 18 of 88 PPS swabs (20%), 13 of 180 admission screening swabs (7%), and 0 of 15 contact screening swabs (0%) were positive for C. auris. The PPS results showed a rapid decrease in colonization: 6 of 14 (43%) in May, 12 of 54 (22%) in June, 9 of 98 (9%) in July, and 1 of 70 (2%) in August. No new C. auris infections were identified from June to October 2019. Conclusions: The control of C. auris in a hospital outbreak requires multimodal interventions, including enhanced IPC interventions, PPS, admission and contact screening for colonization, rigorous monitoring, and team effort.Funding: NoneDisclosures: None


Author(s):  
Qiong Jia ◽  
Yue Guo ◽  
Guanlin Wang ◽  
Stuart J. Barnes

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.


Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 236
Author(s):  
Vijay Kumar Chattu ◽  
Sateesh Sakhamuri ◽  
Shastri Motilal ◽  
Liam J. Pounder ◽  
Vasishma Kanita Persad ◽  
...  

Globally, a quarter of the population is infected with tuberculosis (TB), caused by Mycobacterium tuberculosis. About 5–10% of latent TB infections (LTBI) progress to active disease during the lifetime. Prevention of TB and treating LTBI is a critical component of the World Health Organization’s (WHO) End TB Strategy. This study aims to examine the screening practices for prevention and treatment employed by the National Tuberculosis Program of Trinidad and Tobago in comparison to the WHO’s standard guidelines. A cross-sectional retrospective study was conducted from the TB registers (2018–2019) for persons aged 18 years and above with recorded tuberculin skin test reactions (TST). Bivariate comparisons for categorical variables were made using Chi-square or Fisher’s exact test. Binary logistic regression was used for exploring predictors of TST positivity with adjustment for demographic confounders in multivariable models. Of the total 1972 eligible entries studied, 384 (19.4%) individuals were tested positive with TST. TB contact screening (aOR 2.49; 95% CI 1.65, 3.75) and Bacillus Calmette–Guerin (BCG) vaccination status (aOR 1.66; 95% CI, 1.24 to 2.22) were associated with a positive TST reaction, whereas, preplacement screening failed to show such association when compared to those screened as suspect cases. The findings suggest that TB contact screening and positive BCG vaccination status are associated with TST positivity independent of age and gender.


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