OBJECTIVES:  This study aimed to demonstrate real-world use of the Making Electronic Data More Available for Research and Public Health (MedMorph) Reference Architecture (RA) for automated exchange of hepatitis C-related data for public health surveillance and research using Fast Healthcare Interoperability Resources (FHIR). METHODS:  Pilot participants included a public health authority (PHA), research organization (RO), clinical sites, and electronic health record (EHR) vendors. The RA was tested for hepatitis C public health surveillance and research data exchange. A mixed methods evaluation used multiple data sources to assess impact of the RA compared with usual methods. RESULTS:  After implementation of the RA components, there was no burden on clinical staff to report data for public health surveillance or research purposes. Data were successfully transferred and passed from EHR to PHA and RO, which revealed the value of receiving clinical data in addition to laboratory data via electronic laboratory reporting for the PHA and limitations in the Bulk FHIR standard. CONCLUSION:  Initial results indicate potential for long-term reduction of level of effort of reporting while improving the availability and completeness of clinical data for public health surveillance and research. Using a FHIR-based approach that aligns with regulatory health information technology certification requirements and existing infrastructure may reduce implementation burden. The MedMorph approach can enhance public health surveillance and research, resulting in improved data completeness and reduced reporting burden through automated data exchange using industry standards. MedMorph will continue to inform Centers for Disease Control and Prevention's Public Health Data Strategy, which provides the agency's direction for data modernization.

BACKGROUND: Population size estimation (PSE) for key populations is needed to inform HIV programming and policy. OBJECTIVE: This study aimed to examine the utility of applying a recently proposed method using Google Trend (GT) internet search data to generate PSE (Google Trends Population Size Estimate [GTPSE]) for men who have sex with men (MSM) in 54 countries in Africa, Asia, the Americas, and Europe. METHODS: We examined GT relative search volumes (representing the relative internet search frequency of specific search terms) for "porn" and, as a comparator term, "gay porn" for the year 2020. We assumed "porn" represents "men" (denominator) while "gay porn" represents a subset of "MSM" (numerator) in each county, resulting in a proportional size estimate for MSM. We multiplied the proportional GTPSE values with the countries' male adult population (15-49 years) to obtain absolute size estimates. Separately, we produced subnational MSM PSE limited to countries' (commercial) capitals. Using linear regression analysis, we examined the effect of countries' levels of urbanization, internet penetration, criminalization of homosexuality, and stigma on national GTPSE results. We conducted a sensitivity analysis in a subset of countries (n=14) examining the effect of alternative English search terms, different language search terms (Spanish, French, and Swahili), and alternative search years (2019 and 2021). RESULTS: One country was excluded from our analysis as no GT data could be obtained. Of the remaining 53 countries, all national GTPSE values exceeded the World Health Organization's recommended minimum PSE threshold of 1% (range 1.2%-7.5%). For 44 out of 49 (89.8%) of the countries, GTPSE results were higher than Joint United Nations Programme on HIV/AIDS (UNAIDS) Key Population Atlas values but largely consistent with the regional UNAIDS Global AIDS Monitoring results. Substantial heterogeneity across same-region countries was evident in GTPSE although smaller than those based on Key Population Atlas data. Subnational GTPSE values were obtained in 51 out of 53 (96%) countries; all subnational GTPSE values exceeded 1% but often did not match or exceed the corresponding countries' national estimates. None of the covariates examined had a substantial effect on the GTPSE values (R2 values 0.01-0.28). Alternative (English) search terms in 12 out of 14 (85%) countries produced GTPSE>1%. Using non-English language terms often produced markedly lower same-country GTPSE values compared with English with 10 out of 14 (71%) countries showing national GTPSE exceeding 1%. GTPSE used search data from 2019 and 2021, yielding results similar to those of the reference year 2020. Due to a lack of absolute search volume data, credibility intervals could not be computed. The validity of key assumptions, especially who (males and females) searches for porn and gay porn, could not be assessed. CONCLUSIONS: GTPSE for MSM provides a simple, fast, essentially cost-free method. Limitations that impact the certainty of our estimates include a lack of validation of key assumptions and an inability to assign credibility intervals. GTPSE for MSM may provide an additional data source, especially for estimating national-level PSE.

IMPORTANCE: Health information technology, such as electronic health records (EHRs), has been widely adopted, yet accessing and exchanging data in the fragmented US health care system remains challenging. To unlock the potential of EHR data to improve patient health, public health, and health care, it is essential to streamline the exchange of health data. As leaders across the US Department of Health and Human Services (DHHS), we describe how DHHS has implemented fundamental building blocks to achieve this vision. OBSERVATIONS: Across DHHS, we have implemented 3 foundational building blocks called for by the 2016 21st Century Cures Act to create a unified approach for secure, high-quality, and timely exchange of health data across the health care system. The United States Core Data for Interoperability provides a minimum baseline for data elements that must be available in federally regulated health information technology systems such as certified EHRs. These data elements now must be accessible using Fast Healthcare Interoperability Resources-a secure, flexible, and open-industry standard for health data exchange. The Trusted Exchange Framework and Common Agreement provides a network to securely exchange health data across the country. The 3 building blocks of United States Core Data for Interoperability, Fast Healthcare Interoperability Resources, and Trusted Exchange Framework and Common Agreement are now in place thanks to diligent public and private sector work over 2 administrations. Across DHHS, we are working to refine these building blocks and increase their adoption through regulatory authorities, grants, and public-private partnerships. CONCLUSIONS AND RELEVANCE: The technological building blocks described in this article are creating a unified approach to health data exchange for patient access, clinical care, quality improvement, scientific research, public health, and other uses of health data. Collaborations between the public, nonprofit, and private sectors are needed to maximize their potential. By unlocking the potential of health data, these building blocks are the foundation of a 21st-century digital health care system that will improve the experience of patients and clinicians and result in better health outcomes.

OBJECTIVE: The resurgence of syphilis in the United States presents a significant public health challenge. Much of the information needed for syphilis surveillance resides in electronic health records (EHRs). In this manuscript, we describe a surveillance platform for automating the extraction of EHR data, known as SmartChart Suite, and the results from a pilot. MATERIALS AND METHODS: The SmartChart Suite framework has been developed in compliance with the HHS Health IT Alignment Policy. The platform's major functionalities are (1) data retrieval; (2) logical evaluation; (3) standardized data storage; and (4) results display. The SmartChart Suite was deployed in September 2023 at the Grady Health System in Atlanta, Georgia. We established a cohort of likely syphilis patients, randomly selected 50 medical records for manual and automated chart review, and analyzed the results. RESULTS: The SmartChart Suite was successfully deployed and integrated with the Epic EHR system at Grady. The overall performance results were precision of 97.6%, recall of 100.0%, and F-Score of 98.8. DISCUSSION: Automated abstraction of EHR data has significant potential to improve public health surveillance and case investigation processes while reducing the resource burden on health departments and reporters. The SmartChart Suite comprises a flexible open-source solution for registry development and maintenance across a wide spectrum of conditions and use cases. CONCLUSION: SmartChart Suite demonstrates the potential of automated chart abstraction to support disease surveillance. HHS-compliant open-source tools such as SmartChart Suite can support more efficient human review by providing accurate and relevant data for critical public health activities.

Poxviruses have exceptionally large genomes compared to most other viruses, which represent unique challenges to sequencing and assembly due to complex features such as repeat elements and low complexity sequences. The 2022 global mpox outbreak led to an unprecedented level of poxvirus sequencing as public health and research institutions faced with large sample numbers and demand for fast turnaround, merged NGS protocols designed for small RNA viruses with poxvirus expertise. Traditional manual assembly, checking, and editing of genomes was not feasible. Here, we present a protocol for metagenomic sequencing and orthopoxvirus genome assembly directly from DNA extracted from a patient lesion swab with no viral enrichment or host depletion. This sequencing approach is cost effective when using high throughput sequencing instruments and allows for detection of genomic insertions, deletions, and large rearrangement with confidence. We describe usage of two publicly available bioinformatic pipelines for genome assembly, quality control, annotation, and submission to sequence repositories.

BACKGROUND: The OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could enhance both HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, when compared to gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We used a compartmental model to evaluate the effects of self-testing in diverse scenarios among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020-2030, and to understand which scenarios maximize the advantages of self-testing. METHODS: We introduced a novel 4-compartment model for HIV self-testing. We employed the model under different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential effects of self-testing on HIV incidence and awareness of status when applied to the US MSM population. We studied scenarios in which self-tests supplement laboratory-based tests, with no replacement, and scenarios in which some replacement occurs. We also examined how future improvements in self-test sensitivity may affect our results. RESULTS: When HIV self-tests are supplemental rather than substitutes for laboratory-based testing, self-testing can decrease HIV incidence among MSM in the US by up to 10 % and increase awareness of status among MSM from 85 % to 91 % over a 10-year period, provided linkage to care and formal diagnosis occur promptly following a positive self-test (90 days or less). As self-tests replace a higher percentage laboratory-based testing algorithms, increases in overall testing rates were necessary to ensure reductions in HIV incidence. However, such needed increases were relatively small (under 10 % for prompt engagement in care and moderate levels of replacement). Improvements in self-test sensitivity and/or decreases in the detection period may further reduce any necessary increases in overall testing by up to 40 %. CONCLUSIONS: If properly utilized, self-testing can provide significant long-term reductions to HIV incidence and improve awareness of HIV status. Ensuring that self-testing increases overall testing and that formal diagnosis and engagement in care occur promptly following a positive self-test are necessary to maximize the benefits of self-testing. Future improvements in self-test sensitivity and reductions in the detection period would further reduce HIV incidence and the potential risks associated with replacing laboratory tests with self-tests.

ABSTRACTNipah virus (NiV) is an emerging zoonotic RNA virus that can cause fatal respiratory and neurological disease in animals and humans. Accurate NiV diagnostics and surveillance tools are crucial for the identification of acute and resolved infections and to improve our understanding of NiV transmission and circulation. Here, we have developed and validated a split NanoLuc luciferase NiV glycoprotein (G) biosensor for detecting antibodies in clinical and animal samples. This assay is performed by simply mixing reagents and measuring luminescence, which depends on the complementation of the split NanoLuc luciferase G biosensor following its binding to antibodies. This anti-NiV-G "mix-and-read" assay was validated using the WHO's first international standard for anti-NiV antibodies and more than 700 serum samples from the NiV-endemic country of Bangladesh. Anti-NiV antibodies from survivors persisted for at least 8 years according to both ⍺NiV-G mix-and-read and NiV neutralization assays. The ⍺NiV-G mix-and-read assay sensitivity (98.6%) and specificity (100%) were comparable to anti-NiV IgG ELISA performance but failed to detect anti-NiV antibodies in samples collected less than a week following the appearance of symptoms. Overall, the anti-NiV-G biosensor represents a simple, fast, and reliable tool that could support the expansion of NiV surveillance and retrospective outbreak investigations.

Ricin is a toxic protein regarded as a potential chemical weapon for bioterrorism or criminal use. In the event of a ricin incident, rapid analytical methods are essential for ricin confirmation in a diversity of matrices, from environmental to human or food samples. Mass spectrometry-based methods provide specific toxin identification but require prior enrichment by antibodies to reach trace-level detection in matrices. Here, we describe a novel assay using the glycoprotein asialofetuin as an alternative to antibodies for ricin enrichment, combined with the specific detection of signature peptides by high-resolution mass spectrometry. Additionally, optimizations made to the assay reduced the sample preparation time from 5 h to 80 min only. Method evaluation confirmed the detection of ricin at trace levels over a wide range of pH and in protein-rich samples, illustrating challenging matrices. This new method constitutes a relevant antibody-free solution for the fast and specific mass spectrometry detection of ricin in the situation of a suspected toxin incident, complementary to active ricin determination by adenine release assays.

BACKGROUND: Injection drug use (IDU) is a major contributor to the syndemic of viral hepatitis, human immunodeficiency virus, and drug overdose. However, information on IDU is frequently missing in national viral hepatitis surveillance data, which limits our understanding of the full extent of IDU-associated infections. Multiple imputation by chained equations (MICE) has become a popular approach to address missing data, but its application for IDU imputation is less studied. METHODS: Using the 2019-2021 National Notifiable Diseases Surveillance System acute hepatitis C case data and publicly available county-level measures, we evaluated listwise deletion (LD) and 3 models imputing missing IDU data through MICE: parametric logistic regression, semi-parametric predictive mean matching (PMM), and nonparametric random forest (RF) (both standard RF [sRF] and fast implementation of RF [fRF]). RESULTS: The estimated IDU prevalence among acute hepatitis C cases increased from 63.5% by LD to 65.1% by logistic regression, 66.9% by PMM, 76.0% by sRF, and 85.1% by fRF. Evaluation studies showed that RF-based MICE imputation, especially fRF, has the highest accuracy (as measured by smallest raw bias, percent bias, and root mean square error) and highest efficiency (as measured by smallest 95% confidence interval width) compared to LD and other models. Sensitivity analyses indicated that fRF remained robust when data were missing not at random. CONCLUSION: Our analysis suggested that RF-based MICE imputation, especially fRF, could be a valuable approach for addressing missing IDU data in the context of population-based surveillance systems like National Notifiable Diseases Surveillance System. The inclusion of imputed IDU data may enhance the effectiveness of future surveillance and prevention efforts for the IDU-driven syndemic.

PROBLEM/CONDITION: Elimination of tuberculosis (TB) is defined as reducing TB disease incidence in the United States to less than 1 case per million persons per year. In 2022, TB incidence in the United States was 2.5 TB cases per 100,000 persons. CDC's TB program developed a set of national TB indicators to evaluate progress toward TB elimination through monitoring performance of state and city TB program activities. Examining TB indicator data enables state- and city-level TB programs to identify areas for program evaluation and improvement activities. These data also help CDC identify states and cities that might benefit from technical assistance. PERIOD COVERED: The 5-year period for which the most recent data were available for each of five indicators: 1) overall TB incidence (2018-2022), 2) TB incidence among non-U.S.-born persons (2018-2022), 3) percentage of persons with drug susceptibility results reported (2018-2022), 4) percentage of contacts to sputum acid-fast bacillus (AFB) smear-positive TB patients with newly diagnosed latent TB infection (LTBI) who completed treatment (2017-2021), and 5) percentage of patients with completion of TB therapy within 12 months (2016-2020). DESCRIPTION OF SYSTEM: The National TB Indicators Project (NTIP) is a web-based performance monitoring tool that uses national TB surveillance data reported through the National TB Surveillance System and the Aggregate Reports for TB Program Evaluation. NTIP was developed to facilitate the use of existing data to help TB program staff members prioritize activities, monitor progress, and focus program improvement efforts. The following five indicators were selected for this report because of their importance in Federal TB funding allocation and in accelerating the decline in TB cases: 1) overall TB incidence in the United States, 2) TB incidence among non-U.S.-born persons, 3) percentage of persons with drug susceptibility results reported, 4) percentage of contacts to sputum AFB smear-positive TB cases who completed treatment for LTBI, and 5) percentage of patients with completion of TB therapy within 12 months. For this report, 52 TB programs (50 states, the District of Columbia, and New York City) were categorized into terciles based on the 5-year average number of TB cases reported to National TB Surveillance System. This grouping allows comparison of TB programs that have similar numbers of TB cases and allocates a similar number of TB programs to each category. The following formula was used to calculate the relative change by TB program for each indicator: [(% from year 5 - % from year 1 ÷ % from year 1) × 100]. RESULTS: During the 5-year period for which the most recent data were available, most TB programs had improvements in reducing overall TB incidence (71.2%) and increasing the percentage of contacts receiving a diagnosis of LTBI who completed LTBI treatment (55.8%); the majority of programs (51.0%) also had improvements in reducing incidence among non-U.S.-born persons. The average percentage of persons with drug susceptibility results reported in most jurisdictions (28 of 52, [53.9%]) met or exceeded the 5-year national average of 97% (2018-2022). The percentage of contacts to sputum acid-fast bacillus (AFB) smear-positive TB patients with newly diagnosed latent TB infection (LTBI) who completed treatment increased in 29 of 52 (55.8%) jurisdictions from 2017 to 2021, signifying that, for most jurisdictions, steps have been taken to enhance performance in this area. The average percentage of patients with completion of TB therapy within 12 months was at or above the national average of 89.7% in approximately two-thirds (32 of 52 [61.5%]) of jurisdictions. INTERPRETATION: This report is the first to describe a 5-year relative change for TB program performance. These results suggest that TB programs are making improvements in activities that help identify persons with TB and LTBI and ensure patients complete treatment in a timely manner. PUBLIC HEALTH ACTION: Use of NTIP data from individual TB programs enables a more detailed examination of trends in program performance and identification of areas for program improvement. Assessing indicator trends by TB program provides an opportunity to gain a better understanding of program performance in comparison to other programs. It can also facilitate communication between programs regarding successes and challenges in program improvement. This information is valuable for TB programs to allocate resources effectively and provide additional context on TB control for public health policymakers.

OBJECTIVES: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7(®) FHIR(®)) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. MATERIALS AND METHODS: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. RESULTS: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. DISCUSSION: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. CONCLUSION: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Anopheles stephensi, an Asian malaria vector, continues to expand across Africa. The vector is now firmly established in urban settings in the Horn of Africa. Its presence in areas where malaria resurged suggested a possible role in causing malaria outbreaks. Here, using a prospective case-control design, we investigated the role of An. stephensi in transmission following a malaria outbreak in Dire Dawa, Ethiopia in April-July 2022. Screening contacts of patients with malaria and febrile controls revealed spatial clustering of Plasmodium falciparum infections around patients with malaria in strong association with the presence of An. stephensi in the household vicinity. Plasmodium sporozoites were detected in these mosquitoes. This outbreak involved clonal propagation of parasites with molecular signatures of artemisinin and diagnostic resistance. To our knowledge, this study provides the strongest evidence so far for a role of An. stephensi in driving an urban malaria outbreak in Africa, highlighting the major public health threat posed by this fast-spreading mosquito.

A 23-y-old gelding was presented to a veterinary teaching hospital with a history of chronic, refractory diarrhea. Clinically, the horse was in poor body condition, with a thickened and corrugated large intestine identified by transcutaneous abdominal ultrasonography. At postmortem examination following euthanasia, the large colon and cecum had segmental thickening of the intestinal wall with innumerable mucosal ulcers and prominent polypoid mucosal masses. Many mesenteric and hepatic lymph nodes were enlarged. Histology revealed granulomatous and ulcerative typhlocolitis and granulomatous lymphadenitis with myriad acid-fast, variably gram-positive, intrahistiocytic bacilli that stained by immunohistochemistry for mycobacteria. Molecular testing by PCR and sequencing identified the causative agent as Mycobacterium genavense, which is an unusual presentation of infection in a horse.

OBJECTIVE: This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. CONCLUSIONS: The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Amoxicillin, doxycycline, and clindamycin are among the commonly used antibiotics to treat bacterial infections. However, dosage forms of antibiotics for pediatric patients may not be as readily available as the formulations for adult patients. As such, it is anticipated that during a public health emergency, special instruction may need to be provided on home preparation and administration procedures to dose pediatric patients using available stockpiles of oral tablet and capsule dosage forms. Mixing crushed tablets or capsule contents with soft- or liquid- foods is one of the most common home preparation procedures. To gain knowledge for safe and effective use of prepared drug product instead of the intended intact dosage form, the impact of manipulation of the dosage form was studied. Capsule opening, capsule content assay and uniformity, dissolution, homogeneity, and stability studies of drug mixed with various liquid and soft foods were carried out using intact capsules of amoxicillin, doxycycline, and clindamycin. Higher recovery of capsule contents was achieved when using hands or knives to open capsules compared to using scissors. The capsules of all three antibiotic products contained the labeled amount of active pharmaceutical ingredients (API). The peanut butter-drug mixtures failed both United States Pharmacopeia (USP) assay and dissolution criteria because the peanut butter significantly affected the solubility of the drugs, and hence it was omitted from further study. All drug-food mixtures of the three antibiotic products and 15 selected foods exhibited fast dissolution (e.g., >80 % in 60 min) in the tested medium, except for the amoxicillin-chocolate pudding mixture. Three household containers (cups, plates, and bowls) and four mixing times (0.5 min, 1 min, 2 min, and 5 min) were found to be suitable for preparation of homogeneous mixtures of the antibiotics and foods. For practical purposes, 1 to 2 min mixing time is sufficient to produce homogeneous mixtures. The results of this study provided product quality data on the interactions between the antibiotics and the foods and can potentially support future development of home preparation instructions of antibiotics for pediatric patients or patients with swallowing difficulties.

As Rwanda approaches the UNAIDS Fast Track goals which recommend that 95% of HIV-infected individuals know their status, of whom 95% should receive treatment and 95% of those on treatment achieve viral suppression, the country currently relies on an inefficient paper, and disjointed electronic, systems for case-based surveillance (CBS). Rwanda has established an ecosystem of interoperable systems based on open standards to support HIV CBS. Data were successfully exchanged between an EMR, a client registry, laboratory information system and DHIS-2 Tracker, and subsequently, a complete analytic dataset was ingested into MS-Power Business Intelligence (MS-PowerBI) for analytics and visualization of the CBS data. Existing challenges included inadequate workforce capacity to support mapping of data elements to HL7 FHIR resources. Interoperability optimization to support CBS is work in progress and rigorous evaluations on the effect on health information exchange on monitoring patient outcomes are needed.

INTRODUCTION: Advocacy for the provision of public health resources, including vaccine for the prevention of acute respiratory illnesses (ARIs) among older adults in India, needs evidence on costs and benefits. Using a cohort of community-dwelling adults aged 60 years and older in India, we estimated the cost of ARI episode and its determinants. METHODS: We enrolled 6016 participants in Ballabgarh, Chennai, Kolkata and Pune from July 2018 to March 2020. They were followed up weekly to identify ARI and classified them as acute upper respiratory illness (AURI) or pneumonia based on clinical features based on British Thoracic Society guidelines. All pneumonia and 20% of AURI cases were asked about the cost incurred on medical consultation, investigation, medications, transportation, food and lodging. The cost of services at public facilities was supplemented by WHO-Choosing Interventions that are Cost-Effective(CHOICE) estimates for 2019. Indirect costs incurred by the affected participant and their caregivers were estimated using human capital approach. We used generalised linear model with log link and gamma family to identify the average marginal effect of key determinants of the total cost of ARI. RESULTS: We included 2648 AURI and 1081 pneumonia episodes. Only 47% (range 36%-60%) of the participants with pneumonia sought care. The mean cost of AURI episode was US$13.9, while that of pneumonia episode was US$25.6, with indirect costs comprising three-fourths of the total. The cost was higher among older men by US$3.4 (95% CI: 1.4 to 5.3), those with comorbidities by US$4.3 (95% CI: 2.8 to 5.7) and those who sought care by US$17.2 (95% CI: 15.1 to 19.2) but not by influenza status. The mean per capita annual cost of respiratory illness was US$29.5. CONCLUSION: Given the high community disease and cost burden of ARI, intensifying public health interventions to prevent and mitigate ARI among this fast-growing older adult population in India is warranted.

Immunizations are an important tool to reduce the burden of vaccine preventable diseases and improve population health.(1) High-quality immunization data is essential to inform clinical and public health interventions and respond to outbreaks of vaccine-preventable diseases. To track COVID-19 vaccines and vaccinations, CDC established an integrated network that included vaccination provider systems, health information exchange systems, immunization information systems, pharmacy and dialysis systems, vaccine ordering systems, electronic health records, and tools to support mass vaccination clinics. All these systems reported data to CDC's COVID-19 response system (either directly or indirectly) where it was processed, analyzed, and disseminated. This unprecedented vaccine tracking effort provided essential information for public health officials that was used to monitor the COVID-19 response and guide decisions. This paper will describe systems, processes, and policies that enabled monitoring and reporting of COVID-19 vaccination efforts and share challenges and lessons learned for future public health emergency responses.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Before 2023, there were no immunizing tools to protect older adults and all infants from illness and death due to respiratory syncytial virus (RSV). In 2023, two RSV vaccines for older adults, one of which is also approved for use in pregnant persons, and a long-acting monoclonal antibody to protect infants and some toddlers up to 19 months of age were approved by the Food and Drug Administration and recommended by the Advisory Committee on Immunization Practices of the Centers for Disease Control and Prevention.1-3 These major developments occurred nearly 60 years after a formalin-inactivated RSV vaccine that had been administered to children in clinical trials resulted in enhanced respiratory disease in seronegative children who had been vaccinated, and hopes for a safe and effective RSV vaccine were dashed, or at least postponed. Investigation into why this vaccine failed ultimately led to identification of the stabilized prefusion conformation of the fusion (F) glycoprotein target, which propelled the scientific community to subsequently make remarkable progress in the development of RSV vaccines.4 | | Furthermore, the discovery of the RSV prefusion F protein as a stable antigen was foundational to the work on vaccine development for coronaviruses before the Covid-19 pandemic, and researchers were able to quickly pivot to targeting SARS-CoV-2 spike protein when it emerged.5 In December 2020, the first mRNA vaccines against SARS-CoV-2 were authorized, 1 year after the disease and causative virus were first recognized. The development of these vaccines benefited from the head start provided by previous research on RSV vaccines and the mRNA platform that allows for fast production of large quantities of vaccine. Both factors were critical in controlling the Covid-19 pandemic, but they also show the immense potential for the rapid introduction of new vaccines worldwide.

COVID-19 has highlighted challenges in the measurement quality and comparability of serological binding and neutralization assays. Due to many different assay formats and reagents, these measurements are known to be highly variable with large uncertainties. The development of the WHO international standard (WHO IS) and other pool standards have facilitated assay comparability through normalization to a common material but does not provide assay harmonization nor uncertainty quantification. In this paper, we present the results from an interlaboratory study that led to the development of (1) a novel hierarchy of data analyses based on the thermodynamics of antibody binding and (2) a modeling framework that quantifies the probability of neutralization potential for a given binding measurement. Importantly, we introduced a precise, mathematical definition of harmonization that separates the sources of quantitative uncertainties, some of which can be corrected to enable, for the first time, assay comparability. Both the theory and experimental data confirmed that mAbs and WHO IS performed identically as a primary standard for establishing traceability and bridging across different assay platforms. The metrological anchoring of complex serological binding and neuralization assays and fast turn-around production of an mAb reference control can enable the unprecedented comparability and traceability of serological binding assay results for new variants of SARS-CoV-2 and immune responses to other viruses.

BACKGROUND: Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors. OBJECTIVE: This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US. METHODS: Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together. RESULTS: Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas. CONCLUSIONS: The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

The 2017-2018 North American influenza season caused more hospitalizations and deaths than any year since the 2009 H1N1 pandemic. The majority of recorded influenza infections were caused by A(H3N2) viruses, with most of the virus’s North American diversity falling into the A2 clade. Within A2, we observe a subclade which we call A2/re that rose to comprise almost 70% of A(H3N2) viruses circulating in North America by early 2018. Unlike most fast-growing clades, however, A2/re contains no amino acid substitutions in the hemagglutinin (HA) segment. Moreover, HI assays did not suggest substantial antigenic differences between A2/re viruses and viruses sampled during the 2016-2017 season. Rather, we observe that the A2/re clade was the result of a reassortment event that occurred in late 2016 or early 2017 and involved the combination of the HA and PB1 segments of an A2 virus with neuraminidase (NA) and other segments a virus from the clade A1b. The success of this clade shows the need for antigenic analysis that targets NA in addition to HA. Our results illustrate the potential for non-HA drivers of viral success and necessitate the need for more thorough tracking of full viral genomes to better understand the dynamics of influenza epidemics.

Next-generation sequencing (NGS) of amplicons is used in a wide variety of contexts. Most NGS amplicon sequencing remains overly expensive and inflexible, with library preparation strategies relying upon the fusion of locus-specific primers to full-length adapter sequences with a single identifying sequence or ligating adapters onto PCR products. In Adapterama I, we presented universal stubs and primers to produce thousands of unique index combinations and a modifiable system for incorporating them into Illumina libraries. Here, we describe multiple ways to use the Adapterama system and other approaches for amplicon sequencing on Illumina instruments. In the variant we use most frequently for large-scale projects, we fuse partial adapter sequences (TruSeq or Nextera) onto the 5’ end of locus-specific PCR primers with variable-length tag sequences between the adapter and locus-specific sequences. These fusion primers can be used combinatorially to amplify samples within a 96-well plate (eight forward primers + 12 reverse primers yield 8 × 12 = 96 combinations), and the resulting amplicons can be pooled. The initial PCR products then serve as template for a second round of PCR with dual-indexed iTru or iNext primers (also used combinatorially) to make full-length libraries. The resulting quadruple-indexed amplicons have diversity at most base positions and can be pooled with any standard Illumina library for sequencing. The number of sequencing reads from the amplicon pools can be adjusted, facilitating deep sequencing when required or reducing sequencing costs per sample to an economically trivial amount when deep coverage is not needed. We demonstrate the utility and versatility of our approaches with results from six projects using different implementations of our protocols. Thus, we show that these methods facilitate amplicon library construction for Illumina instruments at reduced cost with increased flexibility. A simple web page to design fusion primers compatible with iTru primers is available at: http://baddna.uga.edu/tools-taggi.html. A fast and easy to use program to demultiplex amplicon pools with internal indexes is available at: https://github.com/lefeverde/Mr_Demuxy.

Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naϊeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets. In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj

The design and execution of rigorous, fast, and ethical vaccine efficacy trials can be challenging during epidemics of emerging pathogens, such as the 2014-2016 Ebola virus and 2015-2016 Zika virus epidemics. Response to an urgent public health crisis requires accelerated research even as emerging epidemics themselves change rapidly and are inherently less well understood than well-established diseases. As part of the World Health Organization Research and Development Blueprint, we designed a web-based interactive decision support system (InterVax-Tool) to help diverse stakeholders navigate the epidemiological, logistical, and ethical decisions involved in designing a vaccine efficacy trial during a public health emergency. In contrast to existing literature on trial design, InterVax-Tool offers high-level visual and interactive assistance through a set of four decision trees, guiding users through selection of 1) the Primary Endpoint, (2) the Target Population, (3) Randomization, and (4) the Comparator. Guidance is provided on how each of fourteen key considerations–grouped as Epidemiological, Vaccine-related, Infrastructural, or Sociocultural–should be used to inform each decision in the trial design process. The tool is not intended to provide a black box decision framework for identifying an optimal trial design, but rather to facilitate transparent, collaborative and comprehensive discussion of the relevant decisions, while recording the decision process. The tool can also assist capacity building by providing a cross-disciplinary picture of trial design using concepts from epidemiology, study design, vaccinology, biostatistics, mathematical modeling and clinical research ethics. Here, we describe the goals and features of InterVax-Tool as well as its application to the design of a Zika vaccine efficacy trial.One Sentence Summary An interactive web-based decision support tool was developed to assist in the design of vaccine efficacy trials during emerging outbreaks.

Background: In the United States, national ecological studies suggest a positive impact of COVID-19 vaccination coverage on outcomes in adults. However, the national impact of the vaccination program on COVID-19 in children remains unknown. To determine the association of COVID-19 vaccination with U.S. case incidence, emergency department visits, and hospital admissions for pediatric populations during the Delta and Omicron periods. Method(s): We conducted an ecological analysis among children aged 5-17 and compared incidence rate ratios (RRs) of COVID-19 cases, emergency department visits, and hospital admissions by pediatric vaccine coverage, with jurisdictions in the highest vaccine coverage quartile as the reference. Result(s): RRs comparing states with lowest pediatric vaccination coverage to the highest pediatric vaccination coverage were 2.00 and 0.64 for cases, 2.96 and 1.11 for emergency department visits, and 2.76 and 1.01 for hospital admissions among all children during the Delta and Omicron periods, respectively. During the 3-week peak period of the Omicron wave, only children aged 12-15 and 16-17 years in the states with the lowest versus highest coverage, had a significantly higher rate of emergency department visits (RR=1.39 and RR=1.34, respectively). Conclusion(s): COVID-19 vaccines were associated with lower case incidence, emergency department visits and hospital admissions among children during the Delta period but the association was weaker during the Omicron period. Pediatric COVID-19 vaccination should be promoted as part of a program to decrease COVID-19 impact among children; however, vaccine effectiveness may be limited when available vaccines do not match circulating viral variants. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

The Illumina iSeq low-capacity sequencing platform was evaluated for use in foodborne disease surveillance and outbreak detection. The platform produced high quality sequence data comparable to that of the Illumina MiSeq and was cost-effective with fast turn-around time in low sample volume environments.

BACKGROUND: Close contacts infected with Mycobacterium tuberculosis are at high risk of tuberculosis (TB) disease and a priority for preventive treatment. Three tests measure infection: two interferon-gamma release assays (IGRAs) and the tuberculin skin test (TST). The objective of our study was to assess the association of positive test results in contacts with infectiousness of the presumed TB source case. METHODS: Contacts in a cohort study at 10 United States sites received both IGRAs (QuantiFERON-TB Gold In-Tube (QFT-GIT) and T-SPOT.TB (T-SPOT)) and TST. We defined test conversion as negative for all tests at baseline and positive for at least one on retest. Risk ratios (RR) and 95% confidence intervals (CI) assessed association of positive test results with increased infectiousness of the TB case-defined as acid-fast bacilli (AFB) on sputum microscopy or cavities on chest radiographs- and contact demographics. RESULTS: Adjusted for contacts' age, nativity, sex, and race, IGRAs (QFT-GIT RR = 6.1, 95% CI 1.7-22.2; T-SPOT RR = 9.4, 95% CI 1.1-79.1), but not TST (RR = 1.7, 95% CI 0.8-3.7), were more likely to convert among contacts exposed to persons with cavitary TB disease. CONCLUSIONS: Because IGRA conversions in contacts are associated with infectiousness of the TB case, their use may improve efficiency of health department contact investigations by focusing efforts on those likely to benefit from preventive treatment in the United States.