Center for Disease Dynamics, Economics and Policy

Human group B Streptococcus (GBS) infections attributable to an invasive, hypervirulent sequence type (ST) 283 have been associated with freshwater fish consumption in Asia. The origin, geographic dispersion pathways and host transitions of GBS ST283 remain unresolved. We gather 328 ST283 isolate whole-genome sequences collected from humans and fish between 1998 and 2021, representing eleven countries across four continents. We apply Bayesian phylogeographic analyses to reconstruct the dispersal history of ST283 and combine ST283 phylogenies with genetic markers and host association to investigate host switching and the gain and loss of antimicrobial resistance and virulence factor genes. Initial dispersal within Asia followed ST283 emergence in the early 1980s, with Singapore, Thailand and Hong Kong observed as early transmission hubs. Subsequent intercontinental dispersal originating from Vietnam began in the decade commencing 2001, demonstrating ST283 holds potential to expand geographically. Furthermore, we observe bidirectional host switching, with the detection of more frequent human-to-fish than fish-to-human transitions, suggesting that sound wastewater management, hygiene and sanitation may help to interrupt chains of transmission between hosts. We also show that antimicrobial resistance and virulence factor genes were lost more frequently than gained across the evolutionary history of ST283. Our findings highlight the need for enhanced surveillance, clinical awareness, and targeted risk mitigation to limit transmission and reduce the impact of an emerging pathogen associated with a high-growth aquaculture industry.

BackgroundBrazil is among the countries in South America where the COVID-19 pandemic has hit the general population hardest. Self-testing for SARS-CoV-2 infection is one of the community-based strategies that could help asymptomatic individuals at-risk of COVID-19, as well as those living in areas that are difficult for health personnel to reach, to know their infectious status and contribute to impeding further transmission of the virus.MethodsA population-based survey was conducted in November 2021, to assess the acceptability of rapid SARS-CoV-2 antigen self-testing among the population of São Paulo. Survey respondents were approached at more than 400 different street-points that were randomly selected using a five-stage randomization process. A 35-item structured questionnaire was used. Dependent variables for our analyses were the likelihood to use and willingness to pay for self-testing, and the likelihood of taking preventive measures to prevent onward transmission of SARS-CoV-2 following a reactive self-test result. Bivariate and multivariate regression analyses were performed.ResultsOverall, 417 respondents (44.12% female) participated; 19.66% had previously had COVID-19 disease. A minority (9.59%) felt at high-risk of COVID-19. The majority of both females and males (73.91% and 60.09%, respectively) were in favor of the idea of SARS-CoV-2 self-testing. Overall, if self-tests were available, almost half of the sample would be very likely (n = 54, 12.95%) or likely (n = 151, 36.21%) to use one if they felt they needed to. Upon receiving a positive self-test result, the majority of respondents would communicate it (88.49%), request facility-based post-test counseling (98.32%), self-isolate (97.60%), and warn their close contacts (96.64%).ConclusionRapid SARS-CoV-2 antigen self-testing could be an acceptable screening tool in São Paulo. The population would be empowered by having access to a technology that would allow them to test, even if asymptomatic, when traveling, or going to work or school. If there is a surge in the incidence of cases, self-testing could be a good approach for mass case detection by Brazil’s already overstretched Unified Health System.

Although COVID-19 vaccines are globally available, waning immunity and emerging vaccine-evasive variants of concern have hindered the international response and transition to a post-pandemic era. Testing to identify and isolate infectious individuals remains the most proactive strategy for containing an ongoing COVID-19 outbreak. We developed a stochastic, compartmentalized model to simulate the impact of using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) assays, rapid antigen tests, and vaccinations on SARS-CoV-2 spread. We compare testing strategies across an example high-income country (the United States) and low- and middle-income country (India). We detail the optimal testing frequency and coverage in the US and India to mitigate an emerging outbreak even in a vaccinated population: overall, maximizing testing frequency is most important, but having high testing coverage remains necessary when there is sustained transmission. A resource-limited vaccination strategy still requires high-frequency testing to minimize subsequent outbreaks and is 16.50% more effective in reducing cases in India than the United States. Tailoring testing strategies to transmission settings can help effectively reduce disease burden more than if a uniform approach were employed without regard to epidemiological variability across locations.

Abstract IntroductionDue to the accelerated pace and quantum of scientific publication during the COVID-19 pandemic, a large number of articles on COVID-19 have been retracted. Pre-prints though not peer-reviewed offer the advantage of rapid dissemination of new findings. In this study, we aim to systematically compare the article characteristics, time to retraction, social media attention, citations, and reasons for retraction between retracted pre-print and peer-reviewed articles on COVID-19.MethodsWe utilized the Retraction Watch database to identify retracted articles on COVID-19 published from 1st January 2020 to 10th March 2022. The articles were reviewed and metadata such as article characteristics (type, category), time to retraction, reasons for retraction, and Altmetric Attention Score (AAS) and citation count were collected.ResultsWe identified 40 retracted pre-prints and 143 retracted peer-reviewed articles. The median (IQR) retraction time for pre-print and peer-reviewed articles was 29 (10-81.5) days and 139 (63-202) days (p = 0.0001). Pre-prints and peer-reviewed article had median (IQR) AAS of 26.5 (4-1155) and 8 (1-38.5), respectively (p = 0.0082). The median (IQR) citation count for pre-prints and peer-reviewed articles was 3 (0-14) and 3 (0-17), respectively (p = 0.5633). The AAS and citation counts were correlated for both pre-prints (r = 0.5200, p = 0.0006) and peer-reviewed articles(r = 0.5909, p = 0.0001). The commonest reason for retraction for pre-prints and peer-reviewed articles concerns about data and results.ConclusionThe increased adoption of pre-prints results in faster identification of erroneous articles compared to the traditional peer-review process.

ABSTRACTObjectivesInnovative diagnostics are essential to assist members of the general population become active agents of case detection. In Indonesia, a country with an over-burdened healthcare system, individuals could use self-tests for SARS-CoV-2 to determine their COVID-19 status. To assess the acceptability of SARS-CoV-2 self-testing among the general population in Indonesia, a cross-sectional, population-based survey was conducted in mid-2021 in Jakarta and the provinces of Banten and North Sulawesi.MethodsThis was a survey that approached respondents in >600 randomly selected street-points in the three study geographies. A 35-item questionnaire was used to collect data on key variables, such as willingness to use and to pay for a SARS-CoV-2 self-test and likely actions following a positive result. Bivariate and multivariate regression analyses were performed.ResultsOf 630 respondents, (318 were female), 14% knew about COVID-19 self-testing, while 62.7% agreed with the concept of people being able to self-test at home, unassisted, for COVID-19. If self-tests were available in Indonesia, >60% of respondents would use them if they felt it necessary and would undertake regular self-testing e.g., weekly if recommended. Upon receiving a positive self-test result, most respondents would communicate it (86.03%), request post-test counseling (80.79%), self-isolate (97.46%), and/or warn their close contacts (n=570, 90.48%).ConclusionsSARS-CoV-2 self-testing would be acceptable to a majority of the Indonesian public, to learn whether they have COVID-19. Self-testing could contribute to an over-burdened healthcare system by helping COVID-19-infected people become agents of change in epidemiological surveillance of SARS-CoV-2 in their communities.

India’s Universal Immunization Programme (UIP) is among the largest routine childhood vaccination programmes in the world. However, only an estimated 65% of Indian children under the age 2 years were fully vaccinated in 2019. We estimated the cost of raising childhood vaccination coverage to a minimum of 90% in each district in India. We obtained vaccine price data from India’s comprehensive multi-year strategic plan for immunization. Cost of vaccine delivery by district was derived from a 2018 field study in 24 districts. We used propensity score matching methods to match the remaining Indian districts with these 24, based on indicators from the National Family Health Survey (2015–16). We assumed the same unit cost of vaccine delivery in matched pair districts and estimated the total and incremental cost of providing routine vaccines to 90% of the current cohort of children in each district. The estimated national cost of providing basic vaccinations—one dose each of Bacillus Calmette–Guerin (BCG) and measles vaccines, and three doses each of oral polio (OPV) and diphtheria, pertussis and tetanus vaccines—was $784.91 million (2020 US$). Considering all childhood vaccines included in UIP during 2018–22 (one dose each of BCG, hepatitis B and measles–rubella; four doses of OPV; two doses of inactivated polio; and three doses each of rotavirus, pneumococcal and pentavalent vaccines), the estimated national cost of vaccines and delivery to 90% of target children in each district was $1.73 billion. The 2018 UIP budget for vaccinating children, pregnant women and adults was $1.17 billion (2020 US$). In comparison, $1.73 billion would be needed to vaccinate 90% of children in all Indian districts with the recommended schedule of routine childhood vaccines. Additional costs for infrastructural investments and communication activities, not incorporated in this study, may also be necessary.

BackgroundBabies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1–2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.MethodsThis study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.ResultsOverall model estimated GA had MAE of 5.2 days (95% CI 4.6–6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6–6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31–94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0–99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5–23.7; p = 0.002).ConclusionsMachine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

BackgroundThere is a strong policy impetus for the One Health cross-sectoral approach to address the complex challenge of zoonotic diseases, particularly in low/lower middle income countries (LMICs). Yet the implementation of this approach in LMIC contexts such as India has proven challenging, due partly to the relatively limited practical guidance and understanding on how to foster and sustain cross-sector collaborations. This study addresses this gap by exploring the facilitators of and barriers to successful convergence between the human, animal and environmental health sectors in India.MethodsA mixed methods study was conducted using a detailed content review of national policy documents and in-depth semi-structured interview data on zoonotic disease management in India. In total, 29 policy documents were reviewed and 15 key informant interviews were undertaken with national and state level policymakers, disease managers and experts operating within the human-animal-environment interface of zoonotic disease control.ResultsOur findings suggest that there is limited policy visibility of zoonotic diseases, although global zoonoses, especially those identified to be of pandemic potential by international organisations (e.g. CDC, WHO and OIE) rather than local, high burden endemic diseases, have high recognition in the existing policy agenda setting. Despite the widespread acknowledgement of the importance of cross-sectoral collaboration, a myriad of factors operated to either constrain or facilitate the success of cross-sectoral convergence at different stages (i.e. information-sharing, undertaking common activities and merging resources and infrastructure) of cross-sectoral action. Importantly, participants identified the lack of supportive policies, conflicting departmental priorities and limited institutional capacities as major barriers that hamper effective cross-sectoral collaboration on zoonotic disease control. Building on existing informal inter-personal relationships and collaboration platforms were suggested by participants as the way forward.ConclusionOur findings point to the importance of strengthening existing national policy frameworks as a first step for leveraging cross-sectoral capacity for improved disease surveillance and interventions. This requires the contextual adaptation of the One Health approach in a manner that is sensitive to the underlying socio-political, institutional and cultural context that determines and shapes outcomes of cross-sector collaborative arrangements.

Objectives/purposeThe costs attributable to antimicrobial resistance (AMR) remain theoretical and largely unspecified. Current figures fail to capture the full health and economic burden caused by AMR across human, animal, and environmental health; historically many studies have considered only direct costs associated with human infection from a hospital perspective, primarily from high-income countries. The Global Antimicrobial Resistance Platform for ONE-Burden Estimates (GAP-ON€) network has developed a framework to help guide AMR costing exercises in any part of the world as a first step towards more comprehensive analyses for comparing AMR interventions at the local level as well as more harmonized analyses for quantifying the full economic burden attributable to AMR at the global level.MethodsGAP-ON€ (funded under the JPIAMR 8th call (Virtual Research Institute) is composed of 19 international networks and institutions active in the field of AMR. For this project, the Network operated by means of Delphi rounds, teleconferences and face-to-face meetings. The resulting costing framework takes a bottom-up approach to incorporate all relevant costs imposed by an AMR bacterial microbe in a patient, in an animal, or in the environment up through to the societal level.ResultsThe framework itemizes the epidemiological data as well as the direct and indirect cost components needed to build a realistic cost picture for AMR. While the framework lists a large number of relevant pathogens for which this framework could be used to explore the costs, the framework is sufficiently generic to facilitate the costing of other resistant pathogens, including those of other aetiologies.ConclusionIn order to conduct cost-effectiveness analyses to choose amongst different AMR-related interventions at local level, the costing of AMR should be done according to local epidemiological priorities and local health service norms. Yet the use of a common framework across settings allows for the results of such studies to contribute to cumulative estimates that can serve as the basis of broader policy decisions at the international level such as how to steer R&D funding and how to prioritize AMR amongst other issues. Indeed, it is only by building a realistic cost picture that we can make informed decisions on how best to tackle major health threats.