دورية أكاديمية
Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity.
| العنوان: | Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity. |
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| المؤلفون: | Meadows AJ; Amanda J. Meadows, PhD, is a Data Scientist/Modeler, Metabiota, San Francisco, CA., Oppenheim B; Ben Oppenheim, PhD, MA, MSc, is Vice President of Product, Policy, and Partnerships, Metabiota, San Francisco, CA., Guerrero J; Jaclyn Guerrero, MPH, is an Advisor, Epidemiology Products, Metabiota, San Francisco, CA., Ash B; Benjamin Ash, MS, is Manager of NRT Data, Metabiota, San Francisco, CA., Badker R; Rinette Badker, MSc, is a Senior Epidemic Analyst, Metabiota, San Francisco, CA., Lam CK; Cathine K. Lam, ACAS, is a Data Scientist/Actuary, Metabiota, San Francisco, CA., Pardee C; Chris Pardee, MS, is Senior Manager of Data Acquisition, Metabiota, San Francisco, CA., Ngoon C; Christopher Ngoon, MS, is a Senior Data Analyst, Metabiota, San Francisco, CA., Savage PT; Patrick T. Savage is a Data Quality Analyst, Metabiota, San Francisco, CA., Sridharan V; Vikram Sridharan, MS, is a Senior Data Scientist and Technical Product Manager, Metabiota, San Francisco, CA., Madhav NK; Nita K. Madhav, MSPH, is Chief Executive Officer, Metabiota, San Francisco, CA., Stephenson N; Nicole Stephenson, DVM, MPVM, PhD, is Senior Director of Data Science and Modeling, Metabiota, San Francisco, CA. |
| المصدر: | Health security [Health Secur] 2022 Jul-Aug; Vol. 20 (4), pp. 331-338. Date of Electronic Publication: 2022 Aug 04. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 101654694 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2326-5108 (Electronic) Linking ISSN: 23265094 NLM ISO Abbreviation: Health Secur Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: New Rochelle, NY : Mary Ann Liebert, Inc., [2015]- |
| مواضيع طبية MeSH: | COVID-19*/epidemiology , Communicable Diseases*/epidemiology, Humans ; Pandemics/prevention & control ; Politics ; Public Health |
| مستخلص: | Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models. |
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| فهرسة مساهمة: | Keywords: Infectious disease; Public health preparedness/response; Reporting bias; Underreporting |
| تواريخ الأحداث: | Date Created: 20220804 Date Completed: 20220816 Latest Revision: 20240129 |
| رمز التحديث: | 20240129 |
| مُعرف محوري في PubMed: | PMC10818036 |
| DOI: | 10.1089/hs.2021.0197 |
| PMID: | 35925788 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2326-5108 |
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| DOI: | 10.1089/hs.2021.0197 |