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Lipsitch M, Bassett MT, Brownstein JS, Elliott P, Eyre D, Grabowski MK, Hay JA, Johansson MA, Kissler SM, Larremore DB, Layden JE, Lessler J, Lynfield R, MacCannell D, Madoff LC, Metcalf CJE, Meyers LA, Ofori SK, Quinn C, Bento AI, Reich NG, Riley S, Rosenfeld R, Samore MH, Sampath R, Slayton RB, Swerdlow DL, Truelove S, Varma JK, Grad YH. Infectious disease surveillance needs for the United States: lessons from Covid-19. Front Public Health 2024; 12:1408193. [PMID: 39076420 PMCID: PMC11285106 DOI: 10.3389/fpubh.2024.1408193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 06/18/2024] [Indexed: 07/31/2024] Open
Abstract
The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.
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Affiliation(s)
- Marc Lipsitch
- Center for Forecasting and Outbreak Analytics, US Centers for Disease Control and Prevention, Atlanta, GA, United States
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Mary T. Bassett
- François-Xavier Bagnoud Center for Health and Human Rights, Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - John S. Brownstein
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Paul Elliott
- Department of Epidemiology and Public Health Medicine, Imperial College London, London, United Kingdom
| | - David Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - M. Kate Grabowski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - James A. Hay
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Stephen M. Kissler
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
| | - Daniel B. Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, United States
| | - Jennifer E. Layden
- Office of Public Health Data, Surveillance, and Technology, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Justin Lessler
- Department of Epidemiology, UNC Gillings School of Public Health, Chapel Hill, NC, United States
| | - Ruth Lynfield
- Minnesota Department of Health, Minneapolis, MN, United States
| | - Duncan MacCannell
- US Centers for Disease Control and Prevention, Office of Advanced Molecular Detection, Atlanta, GA, United States
| | | | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States
| | - Lauren A. Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States
| | - Sylvia K. Ofori
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Celia Quinn
- Division of Disease Control, New York City Department of Health and Mental Hygiene, New York City, NY, United States
| | - Ana I. Bento
- Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States
| | - Nicholas G. Reich
- Departments of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, United States
| | - Steven Riley
- United Kingdom Health Security Agency, London, United Kingdom
| | - Roni Rosenfeld
- Departments of Computer Science and Computational Biology, Carnegie Melon University, Pittsburgh, PA, United States
| | - Matthew H. Samore
- Division of Epidemiology, Department of Medicine, University of Utah, Salt Lake City, UT, United States
| | | | - Rachel B. Slayton
- Division of Healthcare Quality Promotion, US Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - David L. Swerdlow
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Shaun Truelove
- Department of Epidemiology, UNC Gillings School of Public Health, Chapel Hill, NC, United States
| | - Jay K. Varma
- SIGA Technologies, New York City, NY, United States
| | - Yonatan H. Grad
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, United States
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Pouwels KB, Eyre DW, House T, Aspey B, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor NGH, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS. Improving the representativeness of UK's national COVID-19 Infection Survey through spatio-temporal regression and post-stratification. Nat Commun 2024; 15:5340. [PMID: 38914564 PMCID: PMC11196632 DOI: 10.1038/s41467-024-49201-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK's national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.
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Affiliation(s)
- Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
| | - David W Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech, Daresbury, UK
| | - Ben Aspey
- Office for National Statistics, Newport, UK
| | - Philippa C Matthews
- The Francis Crick Institute, London, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | - Jaison Kolenchery
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim E A Peto
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - A Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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Kuh S, Kennedy L, Chen Q, Gelman A. Using leave-one-out cross validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale. Stat Med 2024; 43:953-982. [PMID: 38146825 DOI: 10.1002/sim.9964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 09/07/2023] [Accepted: 11/07/2023] [Indexed: 12/27/2023]
Abstract
In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how leave-one-out cross validation (LOO) can be used to compare Bayesian models for MRP. We investigate two approximate calculations of LOO: Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO). Using two simulation designs, we examine how accurately these two criteria recover the correct ordering of model goodness at predicting population and small-area estimands. Focusing first on variable selection, we find that neither PSIS-LOO nor WTD-PSIS-LOO correctly recovers the models' order for an MRP population estimand, although both criteria correctly identify the best and worst model. When considering small-area estimation, the best model differs for different small areas, highlighting the complexity of MRP validation. When considering different priors, the models' order seems slightly better at smaller-area levels. These findings suggest that, while not terrible, PSIS-LOO-based ranking techniques may not be suitable to evaluate MRP as a method. We suggest this is due to the aggregation stage of MRP, where individual-level prediction errors average out. We validate these results by applying to the real world National Health and Nutrition Examination Survey (NHANES) data in the United States. Altogether, these results show that PSIS-LOO-based model validation tools need to be used with caution and might not convey the full story when validating MRP as a method.
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Affiliation(s)
- Swen Kuh
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia
- Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
| | - Lauren Kennedy
- School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia
- Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
| | - Qixuan Chen
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Andrew Gelman
- Department of Statistics and Political Science, Columbia University, New York, New York, USA
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Li K, Si Y. Embedded multilevel regression and poststratification: Model-based inference with incomplete auxiliary information. Stat Med 2024; 43:256-278. [PMID: 37965978 PMCID: PMC11418010 DOI: 10.1002/sim.9956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/12/2023] [Accepted: 10/29/2023] [Indexed: 11/16/2023]
Abstract
Health disparity research often evaluates health outcomes across demographic subgroups. Multilevel regression and poststratification (MRP) is a popular approach for small subgroup estimation as it can stabilize estimates by fitting multilevel models and adjust for selection bias by poststratifying on auxiliary variables, which are population characteristics predictive of the analytic outcome. However, the granularity and quality of the estimates produced by MRP are limited by the availability of the auxiliary variables' joint distribution; data analysts often only have access to the marginal distributions. To overcome this limitation, we embed the estimation of population cell counts needed for poststratification into the MRP workflow: embedded MRP (EMRP). Under EMRP, we generate synthetic populations of the auxiliary variables before implementing MRP. All sources of estimation uncertainty are propagated with a fully Bayesian framework. Through simulation studies, we compare different methods of generating the synthetic populations and demonstrate EMRP's improvements over alternatives on the bias-variance tradeoff to yield valid subpopulation inferences of interest. We apply EMRP to the Longitudinal Survey of Wellbeing and estimate food insecurity prevalence among vulnerable groups in New York City. We find that all EMRP estimators can correct for the bias in classical MRP while maintaining lower standard errors and narrower confidence intervals than directly imputing with the weighted finite population Bayesian bootstrap (WFPBB) and design-based estimates. Performances from the EMRP estimators do not differ substantially from each other, though we would generally recommend using the WFPBB-MRP for its consistently high coverage rates.
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Affiliation(s)
- Katherine Li
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Yajuan Si
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
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Swilley-Martinez ME, Coles SA, Miller VE, Alam IZ, Fitch KV, Cruz TH, Hohl B, Murray R, Ranapurwala SI. "We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021. Epidemiol Rev 2023; 45:15-31. [PMID: 37789703 DOI: 10.1093/epirev/mxad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/31/2023] [Accepted: 09/28/2023] [Indexed: 10/05/2023] Open
Abstract
Race is a social construct, commonly used in epidemiologic research to adjust for confounding. However, adjustment of race may mask racial disparities, thereby perpetuating structural racism. We conducted a systematic review of articles published in Epidemiology and American Journal of Epidemiology between 2020 and 2021 to (1) understand how race, ethnicity, and similar social constructs were operationalized, used, and reported; and (2) characterize good and poor practices of utilization and reporting of race data on the basis of the extent to which they reveal or mask systemic racism. Original research articles were considered for full review and data extraction if race data were used in the study analysis. We extracted how race was categorized, used-as a descriptor, confounder, or for effect measure modification (EMM)-and reported if the authors discussed racial disparities and systemic bias-related mechanisms responsible for perpetuating the disparities. Of the 561 articles, 299 had race data available and 192 (34.2%) used race data in analyses. Among the 160 US-based studies, 81 different racial categorizations were used. Race was most often used as a confounder (52%), followed by effect measure modifier (33%), and descriptive variable (12%). Fewer than 1 in 4 articles (22.9%) exhibited good practices (EMM along with discussing disparities and mechanisms), 63.5% of the articles exhibited poor practices (confounding only or not discussing mechanisms), and 13.5% were considered neither poor nor good practices. We discuss implications and provide 13 recommendations for operationalization, utilization, and reporting of race in epidemiologic and public health research.
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Affiliation(s)
- Monica E Swilley-Martinez
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Serita A Coles
- Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7440, United States
| | - Vanessa E Miller
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Ishrat Z Alam
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Kate Vinita Fitch
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Theresa H Cruz
- Prevention Research Center, Department of Pediatrics, Health Sciences Center, University of New Mexico, Albuquerque, NM 87131, United States
| | - Bernadette Hohl
- Penn Injury Science Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6021, United States
| | - Regan Murray
- Center for Public Health and Technology, Department of Health, Human Performance and Recreation, University of Arkansas, Fayetteville, AR 72701, United States
| | - Shabbar I Ranapurwala
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7435, United States
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC 27599, United States
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Si Y, Covello L, Wang S, Covello T, Gelman A. Beyond Vaccination Rates: A Synthetic Random Proxy Metric of Total SARS-CoV-2 Immunity Seroprevalence in the Community. Epidemiology 2022; 33:457-464. [PMID: 35394966 PMCID: PMC9148633 DOI: 10.1097/ede.0000000000001488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/17/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Explicit knowledge of total community-level immune seroprevalence is critical to developing policies to mitigate the social and clinical impact of SARS-CoV-2. Publicly available vaccination data are frequently cited as a proxy for population immunity, but this metric ignores the effects of naturally acquired immunity, which varies broadly throughout the country and world. Without broad or random sampling of the population, accurate measurement of persistent immunity post-natural infection is generally unavailable. METHODS To enable tracking of both naturally acquired and vaccine-induced immunity, we set up a synthetic random proxy based on routine hospital testing for estimating total immunoglobulin G (IgG) prevalence in the sampled community. Our approach analyzed viral IgG testing data of asymptomatic patients who presented for elective procedures within a hospital system. We applied multilevel regression and poststratification to adjust for demographic and geographic discrepancies between the sample and the community population. We then applied state-based vaccination data to categorize immune status as driven by natural infection or by vaccine. RESULTS We validated the model using verified clinical metrics of viral and symptomatic disease incidence to show the expected biologic correlation of these entities with the timing, rate, and magnitude of seroprevalence. In mid-July 2021, the estimated immunity level was 74% with the administered vaccination rate of 45% in the two counties. CONCLUSIONS Our metric improves real-time understanding of immunity to COVID-19 as it evolves and the coordination of policy responses to the disease, toward an inexpensive and easily operational surveillance system that transcends the limits of vaccination datasets alone.
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Affiliation(s)
- Yajuan Si
- From the Institute for Social Research, University of Michigan, Ann Arbor, MI
| | | | - Siquan Wang
- Department of Biostatistics, Columbia University, New York, NY
| | | | - Andrew Gelman
- Department of Statistics, Columbia University, New York, NY
- Department of Political Science, Columbia University, New York, NY
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