P354 Laboratory capacity assessment for serological detection of syphilis in Guangdong, China

ABSTRACT U.S. gonorrhea rates are rising, and antibiotic-resistant Neisseria gonorrhoeae (AR-Ng) is an urgent public health threat. Since implementation of nucleic acid amplification tests for N. gonorrhoeae identification, the capacity for culturing N. gonorrhoeae in the United States has declined, along with the ability to perform culture-based antimicrobial susceptibility testing (AST). Yet AST is critical for detecting and monitoring AR-Ng. In 2016, the CDC established the Antibiotic Resistance Laboratory Network (AR Lab Network) to shore up the national capacity for detecting several resistance threats including N. gonorrhoeae. AR-Ng testing, a subactivity of the CDC’s AR Lab Network, is performed in a tiered network of approximately 35 local laboratories, four regional laboratories (state public health laboratories in Maryland, Tennessee, Texas, and Washington), and the CDC’s national reference laboratory. Local laboratories receive specimens from approximately 60 clinics associated with the Gonococcal Isolate Surveillance Project (GISP), enhanced GISP (eGISP), and the program Strengthening the U.S. Response to Resistant Gonorrhea (SURRG). They isolate and ship up to 20,000 isolates to regional laboratories for culture-based agar dilution AST with seven antibiotics and for whole-genome sequencing of up to 5,000 isolates. The CDC further examines concerning isolates and monitors genetic AR markers. During 2017 and 2018, the network tested 8,214 and 8,628 N. gonorrhoeae isolates, respectively, and the CDC received 531 and 646 concerning isolates and 605 and 3,159 sequences, respectively. In summary, the AR Lab Network supported the laboratory capacity for N. gonorrhoeae AST and associated genetic marker detection, expanding preexisting notification and analysis systems for resistance detection. Continued, robust AST and genomic capacity can help inform national public health monitoring and intervention.

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Serological detection of Toxoplasma gondii and associated risk factors among pregnant women in Lahore Pakistan

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2020.09.1099 ◽

2020 ◽

Vol 101 ◽

pp. 419

Author(s):

T. Ijaz ◽

S. Ijaz ◽

M. Afzal ◽

N. Ijaz

Keyword(s):

Risk Factors ◽

Toxoplasma Gondii ◽

Pregnant Women ◽

Serological Detection ◽

Associated Risk Factors

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Using novel methodologies to support burden of disease estimates

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.951 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

T Hald

Keyword(s):

International Development ◽

Traditional Approach ◽

Source Population ◽

Surveillance Systems ◽

Data Generation ◽

Time Data ◽

African Countries ◽

Indirect Measure ◽

True Incidence ◽

Laboratory Capacity

Abstract A challenge to estimating burden of diarrheal diseases, particularly in LMICs, where laboratory capacity and surveillance systems are limited, is obtaining valid estimates of etiology proportions of cases. A commonly used method is systematic review of studies reporting pathogen isolation in diarrhea cases. However, studies often differ in design, source population, timeframe, and pathogens included, hampering extrapolation to the target population. In a study co-funded by the Bill and Melinda Gates Foundation and the UK Department for International Development, we explore a novel approach for estimating diarrhea etiology proportions in urban and rural populations in four African countries. We analyse sewage samples using short-read next-generation sequencing (NGS) to determine abundance of genes that can be mapped to specific bacterial genera, providing an estimate of the relative abundance of specific pathogens in each sample. In parallel to collecting sewage samples, a questionnaire-based population survey will estimate diarrheal incidence. By combining results, pathogen-specific incidence will be estimated and compared with incidence estimates from the traditional approach. The application NGS to human sewage has great potential for surveillance of foodborne infections, particularly in resource-poor settings where laboratory capacity for bacterial isolation is limited. First, NGS is a one method takes all approach, as it is based on detection of RNA/DNA, a language common across pathogens. Second, it is culture independent, allowing for real-time data generation and standardized sharing. Finally, few samples are needed to survey large populations for several pathogens at the same time. Thus, surveillance based on NGS of sewage may prove to be an indirect measure of incidence. Although it will not provide an estimate for the true incidence in the population, it will increase our understanding of the burden and as such be a proxy and novel way of ranking diseases.

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