BACKGROUND: Antimicrobial resistance poses a significant threat to public health globally. We studied the prevalence of colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE), carbapenem-resistant Enterobacterales (CRE), and colistin-resistant Enterobacterales (Col-RE) in hospitals and the surrounding community in South India. METHODS: Adults from 2 hospitals and the catchment community who consented to provide stool specimens were enrolled. Stools were plated on CHROMagar selective for ESCrE, CRE, and Col-RE. Bacterial identification and antibiotic susceptibility testing were done using Vitek 2 Compact and disc diffusion testing. Colistin broth microdilution was performed for a subset of isolates. Prevalence estimates were calculated with 95% confidence intervals (CIs), and differences were compared across populations using the Pearson χ(2) or Fisher exact test. RESULTS: Between November 2020 and March 2022, 757 adults in the community and 556 hospitalized adults were enrolled. ESCrE colonization prevalence was 71.5% (95% CI, 68.1%–74.6%) in the community and 81.8% (95% CI, 78.4%–84.8%) in the hospital, whereas CRE colonization prevalence was 15.1% (95% CI, 12.7%–17.8%) in the community and 22.7% (95% CI, 19.4%–26.3%) in the hospital. Col-RE colonization prevalence was estimated to be 1.1% (95% CI, .5%–2.1%) in the community and 0.5% (95% CI, .2%–1.6%) in the hospital. ESCrE and CRE colonization in hospital participants was significantly higher compared with community participants (P < .001 for both). CONCLUSIONS: High levels of colonization with antibiotic-resistant Enterobacterales were found in both community and hospital settings. This study highlights the importance of surveillance of colonization in these settings for understanding the burden of antimicrobial resistance.

Objective Although contact tracing is generally not used to control influenza pandemics, China and several countries in the Western Pacific Region employed contact tracing as part of COVID-19 response activities. To improve understanding on the use of contact tracing for COVID-19 emergency public health response activities, we describe reported COVID-19 contacts traced and quarantined in China and a proxy for number of reported contacts traced per reported case.Methods We abstracted publicly available online aggregate data reported from China’s National Health Commission and provincial health commissions’ COVID-19 daily situational reports for January 20–February 29, 2020. The number of new contacts traced by report date was computed as the difference between total contacts traced on consecutive reports. A proxy for the number of contacts traced per case was computed as the number of new contacts traced divided by the number of new cases.Results During January 20–February 29, 2020, China reported 80,968 new COVID-19 cases (Hubei Province = 67,608 [83%]), and 659,899 contacts traced (Hubei Province = 265,617 [40%]). Non-Hubei provinces reported more contacts traced per case than Hubei Province; this difference increased over time.Discussion Along with other NPI used in China, contact tracing likely contributed to reducing SARS-CoV-2 transmission by quarantining a large number of potentially infected contacts. Despite reporting only 15% of total cases, non-Hubei provinces had 1.5 times more reported contacts traced compared to Hubei Province. Contract tracing may have been more complete in areas and periods with lower case counts.Competing Interest StatementThe authors have declared no competing interest.Funding StatementData collection and analysis were conducted as part of COVID-19 emergency response. No external funds were used.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This activity was reviewed by CDC and was determined to be non-research, public health emergency response, consistent with applicable U.S. federal law and CDC policy.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData were compiled from Provincial-Level Health Commission Websites Containing Publicly Available Reported Data on COVID-19 National Health Commission http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Anhui http://wjw.ah.gov.cn/ Beijing http://wjw.beijing.gov.cn/xwzx_20031/xwfb/ Chongqing http://wsjkw.cq.gov.cn/ Fujian http://wjw.fujian.gov.cn/ Gansu http://wsjk.gansu.gov.cn/ Guangdong http://wsjkw.gd.gov.cn/zwyw_yqxx/index.html Guangxi http://wsjkw.gxzf.gov.cn/gzdt/bt/ Guizhou http://www.gzhfpc.gov.cn/ Hainan http://wst.hainan.gov.cn/swjw/index.html Hebei http://wsjkw.hebei.gov.cn/ Heilongjiang http://wsjkw.hlj.gov.cn/ Henan http://www.hnwsjsw.gov.cn/ Hubei http://wjw.hubei.gov.cn/fbjd/dtyw/ Hunan http://wjw.hunan.gov.cn/ Inner Mongolia http://wjw.nmg.gov.cn/ Jiangsu http://wjw.jiangsu.gov.cn/ Jiangxi http://hc.jiangxi.gov.cn/ Jilin http://wsjkw.jl.gov.cn/ Liaoning http://wsjk.ln.gov.cn/ Ningxia http://wsjkw.nx.gov.cn/ Qinghai https://wsjkw.qinghai.gov.cn/ Shaanxi http://sx jw.shaanxi.gov.cn/ Shandong http://wsjkw.shandong.gov.cn Shanghai http://wsjkw.sh.gov.cn/xwfb/index.html Shanxi http://wjw.shanxi.gov.cn/ Sichuan http://wsjkw.sc.gov.cn/scwsjkw/szyw/tygl.shtml Tianjin http://wsjs.tj.gov.cn/ Tibet http://wjw.xizang.gov.cn Xinjiang http://xjhfpc.gov.cn Yunnan http://ynswsjkw.yn.gov.cn/wjwWebsite/web/index Zhejiang http://www.zjwjw.gov.cn/col/col1202101/index.html

Healthy People 2020 and the National Colorectal Cancer Roundtable established colorectal cancer (CRC) screening targets of 70.5% and 80%, respectively. While evidence-based interventions (EBIs) have increased CRC screening, the ability to achieve these targets at the population level remains uncertain. We simulated the impact of multicomponent interventions in North Carolina over 5years to assess the potential for meeting national screening targets. Each intervention scenario is described as a core EBI with additional components indicated by the "+" symbol: patient navigation for screening colonoscopy (PN-for-Col+), mailed fecal immunochemical testing (MailedFIT+), MailedFIT+ targeted to Medicaid enrollees (MailedFIT+forMd), and provider assessment and feedback (PAF+). Each intervention was simulated with and without Medicaid expansion and at different levels of exposure (i.e., reach) for targeted populations. Outcomes included the percent up-to-date overall and by sociodemographic subgroups and number of CRC cases and deaths averted. Each multicomponent intervention was associated with increased CRC screening and averted both CRC cases and deaths; three had the potential to reach screening targets. PN-for-Col+achieved the 70.5% target with 97% reach after 1year, and the 80% target with 78% reach after 5years. MailedFIT+ achieved the 70.5% target with 74% reach after 1year and 5years. In the Medicaid population, assuming Medicaid expansion, MailedFIT+forMd reached the 70.5% target after 5years with 97% reach. This study clarifies the potential for states to reach national CRC screening targets using multicomponent EBIs, but decision-makers also should consider tradeoffs in cost, reach, and ability to reduce disparities when selecting interventions.

An Escherichia coli strain (sequence type 636) was isolated from an adult residing in an urban informal settlement in Nairobi, Kenya, and was sequenced using the Illumina MiSeq platform. The draft genome was 5,075,726 bp, with a Col(BS512) plasmid plus aph(6)-Id, bla(TEM-1B), and dfrA7 genes, which encode kanamycin, ampicillin, and trimethoprim resistance proteins, respectively.

In today’s world, the term surveillance brings to mind | images of military intelligence gathering information to | guide military planning or security companies monitor- | ing homes or businesses. Public health surveillance | resembles these types of activities insofar as it also | involves the collection of data for decision making. As | defined by Thacker and Berkelman, public health sur- | veillance is “the ongoing systematic collection, analy- | sis, and interpretation of outcome-specific data for use | in the planning, implementation, and evaluation of | public health practice.”1 This supplement highlights the | various aspects of public health surveillance of physical | activity. | Two important components of the Thacker and | Berkelman definition merit further consideration. First, | surveillance requires ongoing and systematic data col- | lection. Ongoing refers to the periodic assessment of the | outcomes of interest over time. In this way surveillance | differs from the single 1-time survey. Because data col- | lection is done multiple times, it must be systematic; the | same information needs to be collected in the same way. | Even slight changes in survey questions or data collec- | tion methodology can alter the interpretation of the find- | ings both within a surveillance system and across sys- | tems. This is highlighted in the article by Carlson and | colleagues that illustrates how differences in the survey | questions and methodology for 3 U.S. surveillance sys- | tems for physical activity affect prevalence estimates | and trends.