Results and Strategies for a Diversity-Oriented Public Health Monitoring in Germany

Germany is a country of immigration; 27% of the population are people with a migration background (PMB). As other countries, Germany faces difficulties in adequately including hard-to-survey populations like PMB into national public health monitoring. The IMIRA project was initiated to develop strategies to adequately include PMB into public health monitoring and to represent diversity in public health reporting. Here, we aim to synthesize the lessons learned for diversity-oriented public health monitoring and reporting in Germany. We also aim to derive recommendations for further research on migration and health. We conducted two feasibility studies (interview and examination surveys) to improve the inclusion of PMB. Study materials were developed in focus groups with PMB. A systematic review investigated the usability of the concept of acculturation. A scoping review was conducted on discrimination as a health determinant. Furthermore, core indicators were defined for public health reporting on PMB. The translated questionnaires were well accepted among the different migrant groups. Home visits increased the participation of hard-to-survey populations. In examination surveys, multilingual explanation videos and video-interpretation services were effective. Instead of using the concept of acculturation, we derived several dimensions to capture the effects of migration status on health, which were more differentiated. We also developed an instrument to measure subjectively perceived discrimination. For future public health reporting, a set of 25 core indicators was defined to report on the health of PMB. A diversity-oriented public health monitoring should include the following: (1) multilingual, diversity-sensitive materials, and tools; (2) different modes of administration; (3) diversity-sensitive concepts; (4) increase the participation of PMB; and (5) continuous public health reporting, including constant reflection and development of concepts and methods.

A survey of current activities and technologies used to detect carbapenem resistance in bacteria isolated from companion animals at veterinary diagnostic laboratories—United States, 2020

Carbapenems are antimicrobial drugs reserved for the treatment of severe multidrug-resistant Gram-negative bacterial infections. Carbapenem-resistant organisms (CROs) are an urgent public health threat and have been made reportable to public health authorities in many jurisdictions. Recent reports of CROs in companion animals and veterinary settings suggest that CROs are a One Health problem. However, standard practices of U.S. veterinary diagnostic laboratories (VDLs) to detect CROs are unknown. We assessed the capacity of VDLs to characterize carbapenem resistance in isolates from companion animals. Among 74 VDLs surveyed in 42 states, 23 laboratories (31%) from 22 states responded. Most (22/23, 96%) include ≥1 carbapenem on their primary antimicrobial susceptibility testing panel; approximately one-third (9/23, 39%) perform phenotypic carbapenemase production testing or molecular identification of carbapenemase genes. Overall, 35% (8/23) of VDLs across eight states reported they would notify public health if a CRO was detected. Most (17/21, 81%) VDLs were not aware of CRO reporting mandates; some expressed uncertainty about whether the scope of known mandates included CROs from veterinary sources. Although nearly all surveyed VDLs tested for carbapenem resistance, fewer had capacity for mechanism testing or awareness of public health reporting requirements. Addressing these gaps is critical to monitoring CRO incidence and trends in veterinary medicine, preventing spread in veterinary settings, and mounting an effective One Health response. Improved collaboration and communication between public health and veterinary medicine is critical to inform infection control practices in veterinary settings and conduct public health response when resistant isolates are detected.

Accuracy of Case-Based Seroprevalence of SARS-CoV-2 Antibodies in Maricopa County, Arizona

We conducted a community seroprevalence survey in Arizona, from September 12 to October 1, 2020, to determine the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We used the seroprevalence estimate to predict SARS-CoV-2 infections in the jurisdiction by applying the adjusted seroprevalence to the county’s population. The estimated community seroprevalence of SARS-CoV-2 infections was 4.3 times greater (95% confidence interval = 2.2, 7.5) than the number of reported cases. Field surveys with representative sampling provide data that may help fill in gaps in traditional public health reporting. (Am J Public Health. 2022;112(1):38–42. https://doi.org/10.2105/AJPH.2021.306568 )

The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

An open repository of real-time COVID-19 indicators

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Mobilizing COVID-19 Testing: Impact and Challenges

Introduction/Objective In response to the rapidly evolving COVID-19 pandemic, Sanford Health developed a mobile diagnostic testing program capable of reaching geographically dispersed sites and communities. These mobile laboratories provided on-site testing and sensitive detection of SARS-CoV-2 by leveraging Cepheid’s GeneXpert platform, enabling rapid reporting of results directly to the patient and physician. Aggregation of these results allowed monitoring population infection rates and public health reporting. Methods/Case Report Within 3 weeks of conception, the first mobile unit was designed, engineered and deployed. Key requirements for successful implementation included mobile lab licensure, CLIA certification, COLA enrollment, Quality and Risk assessments, inventory management, lab maintenance and ongoing monitoring. Testing was performed using the Xpert Xpress SARS-CoV-2 test and the population tested were primarily asymptomatic individuals. Results (if a Case Study enter NA) Between May 3rd, 2020 and June 23rd, 2021, a total of 31,148 Xpert Xpress SARS-CoV-2 tests were run across 3 mobile laboratories, with an average of 600 tests performed per week. The percent positivity ranged from 0% to 5.8%, reaching highest positivity in week beginning May 10th, 2020. The average turnaround time from sample collection to result verification was 2.0 hours, and the average time from sample receipt to result verification was under 1 hour. Conclusion Sanford Health’s mobile testing program brings SARS-CoV-2 PCR testing to the community and dramatically reduces the time from sample collection to result reporting compared with traditional testing labs, enabling rapid intervention following a positive result. The flexibility of the GeneXpert platform, including the instrument’s robustness, the independently functioning analyzers, and the wide range of tests available, makes it particularly well suited to mobile laboratories. This program demonstrates the impact of on-site testing and highlights the challenges that were overcome for successful implementation, providing a blueprint to support the development of other mobile laboratories in the US.

On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence

Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.

The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

An Open Repository of Real-Time COVID-19 Indicators

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.