IMPORTANCE Case-based surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection likely underestimates the true prevalence of infections. Large-scale seroprevalence surveys can better estimate infection across many geographic regions. OBJECTIVE To estimate the prevalence of persons with SARS-CoV-2 antibodies using residual sera from commercial laboratories across the US and assess changes over time. DESIGN, SETTING, AND PARTICIPANTS This repeated, cross-sectional study conducted across all 50 states, the District of Columbia, and Puerto Rico used a convenience sample of residual serum specimens provided by persons of all ages that were originally submitted for routine screening or clinical management from 2 private clinical commercial laboratories. Samples were obtained during 4 collection periods

, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr). Although non-Hispanic American Indian and Alaska Native (AI/AN) persons account for 0.7% of the U.S. population,* a recent analysis reported that 1.3% of coronavirus disease 2019 (COVID-19) cases reported to CDC with known race and ethnicity were among AI/AN persons (1). To assess the impact of COVID-19 among the AI/AN population, reports of laboratory-confirmed COVID-19 cases during January 22 †-July 3, 2020 were analyzed. The analysis was limited to 23 states § with >70% complete race/ethnicity information and five or more laboratory-confirmed COVID-19 cases among both AI/AN persons (alone or in combination with other races and ethnicities) and non-Hispanic white (white) persons. Among 424,899 COVID-19 cases reported by these states, 340,059 (80%) had complete race/ethnicity information; among these 340,059 cases, 9,072 (2.7%) occurred among AI/AN persons, and 138,960 (40.9%) among white persons. Among 340,059 cases with complete patient race/ethnicity data, the cumulative incidence among AI/AN persons in these 23 states was 594 per 100,000 AI/AN population (95% confidence interval [CI] = 203-1,740), compared with 169 per 100,000 white population (95% CI = 137-209) (rate ratio [RR] = 3.5; 95% CI = 1.2-10.1). AI/AN persons with COVID-19 were younger (median age = 40 years; interquartile range [IQR] = 26-56 years) than were white persons (median age = 51 years; IQR = 32-67 years). More complete case report data and timely, culturally responsive, and evidencebased public health efforts that leverage the strengths of AI/AN communities are needed to decrease COVID-19 transmission and improve patient outcomes.

BackgroundPublic health triangulation is a process for reviewing, synthesising and interpreting secondary data from multiple sources that bear on the same question to make public health decisions. It can be used to understand the dynamics of HIV transmission and to measure the impact of public health programs. While traditional intervention research and metaanalysis would be ideal sources of information for public health decision making, they are infrequently available, and often decisions can be based only on surveillance and survey data.MethodsThe process involves examination of a wide variety of data sources and both biological, behavioral and program data and seeks input from stakeholders to formulate meaningful public health questions. Finally and most importantly, it uses the results to inform public health decision-making. There are 12 discrete steps in the triangulation process, which included identification and assessment of key questions, identification of data sources, refining questions, gathering data and reports, assessing the quality of those data and reports, formulating hypotheses to explain trends in the data, corroborating or refining working hypotheses, drawing conclusions, communicating results and recommendations and taking public health action.ResultsTriangulation can be limited by the quality of the original data, the potentials for ecological fallacy and "data dredging" and reproducibility of results.ConclusionsNonetheless, we believe that public health triangulation allows for the interpretation of data sets that cannot be analyzed using meta-analysis and can be a helpful adjunct to surveillance, to formal public health intervention research and to monitoring and evaluation, which in turn lead to improved national strategic planning and resource allocation.

No abstract

IntroductionThe World Health Organization's (WHO) recommendation of “Treat All” has accelerated the call for differentiated antiretroviral therapy (ART) delivery, a method of care that efficiently uses limited resources to increase access to HIV treatment. WHO has further recommended that stable individuals on ART receive refills every 3 to 6 months and attend clinical visits every 3 to 6 months. However, there is not yet consensus on how to ensure that the quality of services is maintained as countries strive to meet these standards. This commentary responds to this gap by defining a pragmatic approach to the monitoring and evaluation (M&E) of the scale up of differentiated ART delivery for global and national stakeholders.DiscussionProgramme managers need to demonstrate that the scale up of differentiated ART delivery is achieving the desired effectiveness and efficiency outcomes to justify continued support by national and global stakeholders. To achieve this goal, the two existing global WHO HIV treatment indicators of ART retention and viral suppression should be augmented with two broad aggregate measures. The addition of indicators measuring the frequency of (1) clinical and (2) refill visits by PLHIV per year will allow evaluation of the pace of scale up while monitoring its overall effect on the quality and efficiency of services. The combination of these four routinely collected aggregate indicators will also facilitate the comparison of outcomes among facilities, regions or countries implementing different models of ART delivery. Enhanced monitoring or additional assessments will be required to answer other critical questions on the process of implementation, acceptability, effectiveness and efficiency.ConclusionsThese proposed outcomes are useful markers for the effectiveness and efficiency of the health system's attempts to deliver quality treatment to those who need it—and still reserve as much of the available resource pool as possible for other key elements of the HIV response.