Paper and screen media in current health education practices aimed at older adults: a scoping review protocol.

INTRODUCTION: With technological advancement and the COVID-19 pandemic, paper-based media are giving way to screen-based media to promote healthy ageing. However, there is no review available covering paper and screen media use by older people, so the objective of this review is to map the current use of paper-based and/or screen-based media for health education aimed at older people. METHODS AND ANALYSIS: The literature will be searched in Scopus, Web of Science, Medline, Embase, Cinahl, The ACM Guide to Computing Literature and Psyinfo databases. Studies in English, Portuguese, Italian or Spanish published from 2012 to the date of the search will be examined. In addition, an additional strategy will be carried out, which will be a Google Scholar search, in which the first 300 studies according to Google's relevance algorithm will be verified. The terms used in the search strategy will be focused on older adults, health education, paper-based and screen-based media, preferences, intervention and other related terms. This review will include studies where the average age of the participants was 60 years or older and were users of health education strategies through paper-based or screen-based media. Two reviewers will carry out the selection of studies in five steps: identification of studies and removal of duplicates, pilot test, selection by reading titles and abstracts, full-text inclusion and search for additional sources. A third reviewer will resolve disagreements. To record information from the included studies, a data extraction form will be used. The quantitative data will be presented in a descriptive way and the qualitative data through Bardin's content analysis. ETHICS AND DISSEMINATION: Ethical approval is not applicable to the scoping review. The results will be disseminated through presentations at significant scientific events and published in journals in the area. PROTOCOL REGISTRATION NUMBER: Open science framework (DOI: DOI 10.17605/OSF.IO/GKEAH).

Towards a Consistent Representation of Contradictions Within Health Data for Efficient Implementation of Data Quality Assessments.

Contradictions as a data quality indicator are typically understood as impossible combinations of values in interdependent data items. While the handling of a single dependency between two data items is well established, for more complex interdependencies, there is not yet a common notation or structured evaluation method established to our knowledge. For the definition of such contradictions, specific biomedical domain knowledge is required, while informatics domain knowledge is responsible for the efficient implementation in assessment tools. We propose a notation of contradiction patterns that reflects the provided and required information by the different domains. We consider three parameters (α, ß, θ): the number of interdependent items as α, the number of contradictory dependencies defined by domain experts as ß, and the minimal number of required Boolean rules to assess these contradictions as θ. Inspection of the contradiction patterns in existing R packages for data quality assessments shows that all six examined packages implement the (2,1,1) class. We investigate more complex contradiction patterns in the biobank and COVID-19 domains showing that the minimum number of Boolean rules might be significantly lower than the number of described contradictions. While there might be a different number of contradictions formulated by the domain experts, we are confident that such a notation and structured analysis of the contradiction patterns helps to handle the complexity of multidimensional interdependencies within health data sets. A structured classification of contradiction checks will allow scoping of different contradiction patterns across multiple domains and effectively support the implementation of a generalized contradiction assessment framework.

Community Health Workers as Vaccinators: A Rapid Review of the Global Landscape, 2000-2021.

INTRODUCTION: Community health workers (CHWs) could expand immunization access in under-reached communities by administering vaccines. This rapid review identifies countries where CHWs administered vaccines and synthesizes health systems factors that may contribute to or detract from the feasibility of CHWs administering vaccines. METHODS: We conducted a rapid review of peer-reviewed literature from 3 databases and gray literature identified through web searches and by CHW subject matter experts. We treated extracted data on conditions related to vaccine administration by CHWs as qualitative data and conducted deductive content analysis. RESULTS: We retained 32 documents from 497 initial records and identified 23 CHW cadres that vaccinated in 20 countries, ranging from long-established national programs delivering routine immunizations to pilot projects delivering 1 specific vaccine. CHWs who vaccinate face the following challenges: (1) inadequate supply chain training, (2) inadequate cold chain equipment, (3) transportation for supplies and to communities, (4) heavy existing workload, (5) inadequate or irregular remuneration, (6) inadequate or irregular supervision. CONCLUSION: To improve immunization coverage in underimmunized and zero-dose communities, countries where CHWs vaccinate should provide CHWs with adequate remuneration, supervision, supply chain support and management, and formal integration within the health system. CHWs administered vaccines in 20 of the 75 countries with documented CHW programs, suggesting the majority of an estimated 3.3 million CHWs globally do not yet administer vaccines. In light of health care workforce shortages and immunization equity gaps, further exacerbated by the COVID-19 pandemic, policymakers should consider task-shifting vaccine administration to CHWs to bolster immunization access for under-reached communities. Additional systematic documentation is needed to further explore best practices to support CHWs as vaccinators, especially related to supply chain, policy, safety, and efficacy.

Facilitators and barriers impacting in-hospital Trauma Quality Improvement Program (TQIP) implementation across country income levels: a scoping review.

OBJECTIVE: Trauma is a leading cause of mortality and morbidity globally, disproportionately affecting low/middle-income countries (LMICs). Understanding the factors determining implementation success for in-hospital Trauma Quality Improvement Programs (TQIPs) is critical to reducing the global trauma burden. We synthesised topical literature to identify key facilitators and barriers to in-hospital TQIP implementation across country income levels. DESIGN: Scoping review. DATA SOURCES: PubMed, Web of Science and Global Index Medicus databases were searched from June 2009 to January 2022. ELIGIBILITY CRITERIA: Published literature involving any study design, written in English and evaluating any implemented in-hospital quality improvement programme in trauma populations worldwide. Literature that was non-English, unpublished and involved non-hospital TQIPs was excluded. DATA EXTRACTION AND SYNTHESIS: Two reviewers completed a three-stage screening process using Covidence, with any discrepancies resolved through a third reviewer. Content analysis using the Consolidated Framework for Implementation Research identified facilitator and barrier themes for in-hospital TQIP implementation. RESULTS: Twenty-eight studies met the eligibility criteria from 3923 studies identified. The most discussed in-hospital TQIPs in included literature were trauma registries. Facilitators and barriers were similar across all country income levels. The main facilitator themes identified were the prioritisation of staff education and training, strengthening stakeholder dialogue and providing standardised best-practice guidelines. The key barrier theme identified in LMICs was poor data quality, while high-income countries (HICs) had reduced communication across professional hierarchies. CONCLUSIONS: Stakeholder prioritisation of in-hospital TQIPs, along with increased knowledge and consensus of trauma care best practices, are essential efforts to reduce the global trauma burden. The primary focus of future studies on in-hospital TQIPs in LMICs should target improving registry data quality, while interventions in HICs should target strengthening communication channels between healthcare professionals.

Factors associated with contact tracing compliance among communities of color in the first year of the COVID-19 pandemic.

RATIONALE: The disproportionate impact of COVID-19 on communities of color has raised questions about the unique experiences within these communities not only in terms of becoming infected with COVID-19 but also mitigating its spread. The utility of contact tracing for managing community spread and supporting economic reopening is contingent upon, in part, compliance with contact tracer requests. OBJECTIVE: We investigated how trust in and knowledge of contact tracers influence intentions to comply with tracing requests and whether or not these relationships and associated antecedent factors differ between communities of color. METHOD: Data were collected from a U.S. sample of 533 survey respondents from Fall (2020) to Spring 2021. Multi-group SEM tested quantitative study hypotheses separately for Black, AAPI, Latinx, and White sub-samples. Qualitative data were collected via open-ended questions to inform the roles of trust and knowledge in contact tracing compliance. RESULTS: Trust in contact tracers was associated with increased intentions to comply with tracing requests and significantly mediated the positive relationship between trust in healthcare professionals and government health officials with compliance intentions. Yet, the indirect effects of trust in government health officials on compliance intentions were significantly weaker for the Black, Latinx, and AAPI samples compared to Whites, suggesting this strategy for increasing compliance may not be as effective among communities of color. Health literacy and contact tracing knowledge played a more limited role in predicting compliance intentions directly or indirectly, and one that was inconsistent across racial groups. Qualitative results reinforce the importance of trust relative to knowledge for increasing tracing compliance intentions. CONCLUSIONS: Building trust in contact tracers, more so than increasing knowledge, may be key to encouraging contact tracing compliance. Differences among communities of color and between these communities and Whites inform the policy recommendations provided for improving contact tracing success.

Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA.

With the proliferation of online data collection in human-subjects research, concerns have been raised over the presence of inattentive survey participants and non-human respondents (bots). We compared the quality of the data collected through five commonly used platforms. Data quality was indicated by the percentage of participants who meaningfully respond to the researcher's question (high quality) versus those who only contribute noise (low quality). We found that compared to MTurk, Qualtrics, or an undergraduate student sample (i.e., SONA), participants on Prolific and CloudResearch were more likely to pass various attention checks, provide meaningful answers, follow instructions, remember previously presented information, have a unique IP address and geolocation, and work slowly enough to be able to read all the items. We divided the samples into high- and low-quality respondents and computed the cost we paid per high-quality respondent. Prolific ($1.90) and CloudResearch ($2.00) were cheaper than MTurk ($4.36) and Qualtrics ($8.17). SONA cost $0.00, yet took the longest to collect the data.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Digital health interventions and quality of home-based primary care for older adults: A scoping review protocol.

Introduction: The use of digital health interventions has expanded, particularly in home-based primary care (HBPC), following the increase in the older adult population and the need to respond to the higher demand of chronic conditions, weakness and loss of autonomy of this population. There was an even greater demand with COVID-19 and subsequent isolation/social distancing measures for this risk group. The objective of this study is to map and identify the uses and types of digital health interventions and their reported impacts on the quality of HBPC for older adults worldwide. Methods and analysis: This is a scoping review protocol which will enable a rigorous, transparent and reliable synthesis of knowledge. The review will be developed from the theoretical perspective of Arksey and O'malley, with updates by Levac and Peters and respective collaborators based on the Joanna Briggs Institute manual, and guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Data from white literature will be extracted from multidisciplinary health databases such as: the Virtual Health Library, LILACS, MEDLINE/PubMed, Scopus, Web of Science, Cinahl and Embase; while Google Scholar will be used for gray literature. No date limit or language restrictions will be determined. The quantitative data will be analyzed through descriptive statistics and qualitative data through thematic analysis. The results will be submitted to stakeholder consultation for preliminary sharing of the study and will later be disseminated through publication in open access scientific journals, scientific events and academic and community journals. The full scoping review report will present the main impacts, challenges, opportunities and gaps found in publications related to the use of digital technologies in primary home care. Discussion: The organization of this protocol will increase the methodological rigor, quality, transparency and accuracy of scoping reviews, reducing the risk of bias.

A comparison of four epidemic waves of COVID-19 in Malawi; an observational cohort study.

BACKGROUND: Compared to the abundance of clinical and genomic information available on patients hospitalised with COVID-19 disease from high-income countries, there is a paucity of data from low-income countries. Our aim was to explore the relationship between viral lineage and patient outcome. METHODS: We enrolled a prospective observational cohort of adult patients hospitalised with PCR-confirmed COVID-19 disease between July 2020 and March 2022 from Blantyre, Malawi, covering four waves of SARS-CoV-2 infections. Clinical and diagnostic data were collected using an adapted ISARIC clinical characterization protocol for COVID-19. SARS-CoV-2 isolates were sequenced using the MinION™ in Blantyre. RESULTS: We enrolled 314 patients, good quality sequencing data was available for 55 patients. The sequencing data showed that 8 of 11 participants recruited in wave one had B.1 infections, 6/6 in wave two had Beta, 25/26 in wave three had Delta and 11/12 in wave four had Omicron. Patients infected during the Delta and Omicron waves reported fewer underlying chronic conditions and a shorter time to presentation. Significantly fewer patients required oxygen (22.7% [17/75] vs. 58.6% [140/239], p < 0.001) and steroids (38.7% [29/75] vs. 70.3% [167/239], p < 0.001) in the Omicron wave compared with the other waves. Multivariable logistic-regression demonstrated a trend toward increased mortality in the Delta wave (OR 4.99 [95% CI 1.0-25.0 p = 0.05) compared to the first wave of infection. CONCLUSIONS: Our data show that each wave of patients hospitalised with SARS-CoV-2 was infected with a distinct viral variant. The clinical data suggests that patients with severe COVID-19 disease were more likely to die during the Delta wave.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Tracking health system performance in times of crisis using routine health data: lessons learned from a multicountry consortium.

COVID-19 has prompted the use of readily available administrative data to track health system performance in times of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health System Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People's Democratic Republic, Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health conditions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health information technologies; reduce delays and loosen restrictions for researchers to access the data; complement routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of service disruptions.

Health Experiences of Nurses during the COVID-19 Pandemic: A Mixed Methods Study.

This study characterizes the impact of the COVID-19 pandemic on the mental and physical health of nurses. Qualitative data (collected using semi-structured interviews) were integrated with quantitative data (collected concurrently using the SF-12 Health Survey). Nurses (N = 30) compared their health prior to and during the first pandemic wave (March-May 2020). Interviews were analyzed thematically; descriptive statistics and t-tests compared pre-pandemic to current SF-12 scores. Qualitative findings demonstrated an impact on nurses' mental health expressed as isolation, loss, intense emotions, and feelings of being expendable. Impact on nurses' physical health included exhaustion, personal protective equipment skin breakdown, limited breaks from work, and virus exposure. Quantitative results show nurses' experienced declines in overall mental health (p < .001), and multiple physical health domains: role limitations due to physical problems (p < .0001), bodily pain (p < .0001), and general health (p < .0001). Promotion of nurses' well-being and safety, as well as education in emergency preparedness, must be given precedence to protect nurses' health.

Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?

BACKGROUND: Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders. METHODS: This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews. RESULTS: Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality-denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don't Matter: Just Another Tool in the Toolbox- reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword-the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care-broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care-elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation. CONCLUSION: The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.

Developing valid test bank of surveillance case study scenarios for inter-rater reliability.

Case studies are utilized for training on National Healthcare Safety Network (NHSN) healthcare associated infection surveillance definitions. Item discrimination and item analysis were applied to case studies to identify questions that most accurately assess infection preventionists (IPs) application of surveillance definitions.

Evaluating patient data quality in South Africa's National Health Laboratory Service Data Warehouse, 2017-2020: implications for monitoring child health programmes.

BACKGROUND: South Africa's National Health Laboratory Service (NHLS), the only clinical laboratory service in the country's public health sector, is an important resource for monitoring public health programmes. OBJECTIVES: We describe NHLS data quality, particularly patient demographics among infants, and the effect this has on linking multiple test results to a single patient. METHODS: Retrospective descriptive analysis of NHLS data from 1st January 2017-1st September 2020 was performed. A validated probabilistic record-linking algorithm linked multiple results to individual patients in lieu of a unique patient identifier. Paediatric HIV PCR data was used to illustrate the effect on monitoring and evaluating a public health programme. Descriptive statistics including medians, proportions and inter quartile ranges are reported, with Chi-square univariate tests for independence used to determine association between variables. RESULTS: During the period analysed, 485 300 007 tests, 98 217 642 encounters and 35 771 846 patients met criteria for analysis. Overall, 15.80% (n = 15 515 380) of all encounters had a registered national identity (ID) number, 2.11% (n = 2 069 785) were registered without a given name, 63.15% (n = 62 020 107) were registered to women and 32.89% (n = 32 304 329) of all folder numbers were listed as either the patient's date of birth or unknown. For infants tested at < 7 days of age (n = 2 565 329), 0.099% (n = 2 534) had an associated ID number and 48.87% (n = 1 253 620) were registered without a given name. Encounters with a given name were linked to a subsequent encounter 40.78% (n = 14 180 409 of 34 775 617) of the time, significantly more often than the 21.85% (n = 217 660 of 996 229) of encounters registered with a baby-derivative name (p-value < 0.001). CONCLUSION: Unavailability and poor capturing of patient demographics, especially among infants and children, affects the ability to accurately monitor routine health programmes. A unique national patient identifier, other than the national ID number, is urgently required and must be available at birth if South Africa is to accurately monitor programmes such as the Prevention of Mother-to-Child Transmission of HIV.

Improving injury surveillance data quality: a study based on hospitals contributing to the Victorian Emergency Minimum Dataset.

OBJECTIVE: In this paper, we describe the design and baseline data of a study aimed at improving injury surveillance data quality of hospitals contributing to the Victorian Emergency Minimum Dataset (VEMD). METHODS: The sequential study phases include a baseline analysis of data quality, direct engagement and communication with each of the emergency department (ED) hospital sites, collection of survey and interview data and ongoing monitoring. RESULTS: In 2019/20, there were 371,683 injury-related ED presentations recorded in the VEMD. Percentage unspecified, the indicator of (poor) data quality, was lowest for 'body region' (2.7%) and 'injury type' (7.4%), and highest for 'activity when injured' (29.4%). In the latter, contributing hospitals ranged from 3.0-99.9% unspecified. The 'description of event' variable had a mean word count of 10; 16/38 hospitals had a narrative word count of <5. CONCLUSIONS: Baseline hospital injury surveillance data vary vastly in data quality, leaving much room for improvement and justifying intervention as described. IMPLICATIONS FOR PUBLIC HEALTH: Hospital engagement and feedback described in this study is expected to have a marked effect on data quality from 2021 onwards. This will ensure that Victorian injury surveillance data can fulfil their purpose to accurately inform injury prevention policy and practice.

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research.

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.