Medicine:Acoustic epidemiology

From HandWiki

Acoustic epidemiology refers to the study of the determinants and distribution of disease. It also refers to the analysis of sounds produced by the body (coughs, sneezes, wheezing, etc) through a single tool or a combination of diagnostic tools.[1] In many cases, epidemiologists have worked across multiple disciplines and used different technologies in order to find answers pertaining to disease distribution. For example, in the 1800’s, John Snow determined that cholera was plaguing Europe through contaminated water. This led to the decision to remove a pump that was the cause of this contamination, thus effectively ending the epidemic. More broadly, Snow’s epidemiological efforts led to the development of sewage drainage and water purifying systems in other areas.[2]

As COVID-19 developed, genomic epidemiologists began using whole genomes to study the disease. On the CDC’s website, they have posted a “COVID-19 Genomic Epidemiology Toolkit”, which provides a means to expand the field of genomic epidemiology with regards to COVID-19 within state and local populations.[3]

Acoustic epidemiology is a field that studies bodily sounds, such as coughs and breath sounds, in order to better identify determinants and distribution of disease. Following in the footsteps of epidemiological tools and efforts such as those outlined above, acoustic epidemiology is concerned with using body sound data to improve disease surveillance capabilities for COVID-19 and any other applicable diseases of the future.[4][5]

Clinical relevance

Being that epidemiology is a population-based area of study, findings from acoustic disease surveillance are important on a large scale, and have far-reaching implications for society as a whole. Cough and breath sounds provide rich epidemiological data.[6]

Baseline Measurements and Deviations

Studying respiratory sounds and identifying deviations from baseline is an invaluable epidemiologic tool.[7] On a community and population level, this can help to determine to what extent a disease may be spreading or changing. One of the major themes of concern throughout the COVID 19 pandemic has been travel safety, hotspots, and outbreaks in certain areas.[8]

Acoustic Epidemiology Through Use of Smartphone Apps

As a means to overcome some of the restrictions imposed by the COVID-19 pandemic, smartphone apps were developed to capture and analyze respiratory health data safely.[9]

In a 2020-2021 study of acoustic epidemiology, in Navarra, Spain , the Hyfe app was used to track respiratory sounds in over 800 study participants.[10][11]

Syndromic Surveillance

Syndromic surveillance is a complementary, and potentially faster method of health data collection and analysis as compared to standard methods of public health monitoring.[12]

Examples of Syndromic Surveillance

Instances of syndromic surveillance are easy to find. Examples include:[13]

  • Logs that record missed school or work due to illness[14]
  • Symptoms recorded on patients in emergency rooms[15]
  • How often certain lab tests are ordered and performed[16]

Bias in Syndromic Surveillance

Sources for syndromic surveillance may be biased, as they vary based on healthcare access in a given area. Therefore, some have questioned whether certain common methods of syndromic surveillance are truly representative of the larger population.[17][18]

The future of acoustic epidemiology

The value of being able to track signs of deviations from baseline with regards to respiratory sounds at a population level is becoming clear through research.[19][20] Epidemiologists predict that respiratory viruses could continue to be a problem in the future. Therefore, effective monitoring of acoustic data will need to be easy, affordable, and available on a wide scale.[21][19][20]

See also

  • Cough Tracking
  • Respiratory Health
  • Pulmonary System

References

  1. ↑ Frérot, Mathilde; Lefebvre, Annick; Aho, Simon; Callier, Patrick; Astruc, Karine; Glélé, Ludwig Serge Aho (2018-12-10). "What is epidemiology? Changing definitions of epidemiology 1978-2017" (in en). PLOS ONE 13 (12): e0208442. doi:10.1371/journal.pone.0208442. ISSN 1932-6203. PMID 30532230. Bibcode: 2018PLoSO..1308442F. 
  2. ↑ Tulchinsky, Theodore H. (2018), "John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now", Case Studies in Public Health (Elsevier): pp. 77–99, doi:10.1016/b978-0-12-804571-8.00017-2, ISBN 9780128045718 
  3. ↑ "COVID-19 Genomic Epidemiology Toolkit | Advanced Molecular Detection (AMD) | CDC" (in en-us). 2021-11-17. https://www.cdc.gov/amd/training/covid-19-gen-epi-toolkit.html. 
  4. ↑ Sara, Jaskanwal Deep Singh; Maor, Elad; Borlaug, Barry; Lewis, Bradley R.; Orbelo, Diana; Lerman, Lliach O.; Lerman, Amir (2020). "Non-invasive vocal biomarker is associated with pulmonary hypertension". PLOS ONE 15 (4): e0231441. doi:10.1371/journal.pone.0231441. ISSN 1932-6203. PMID 32298301. Bibcode: 2020PLoSO..1531441S. 
  5. ↑ Maor, Elad; Tsur, Nir; Barkai, Galia; Meister, Ido; Makmel, Shmuel; Friedman, Eli; Aronovich, Daniel; Mevorach, Dana et al. (2021). "Noninvasive Vocal Biomarker is Associated With Severe Acute Respiratory Syndrome Coronavirus 2 Infection". Mayo Clinic Proceedings. Innovations, Quality & Outcomes 5 (3): 654–662. doi:10.1016/j.mayocpiqo.2021.05.007. ISSN 2542-4548. PMID 34007956. 
  6. ↑ Imran, Ali; Posokhova, Iryna; Qureshi, Haneya N.; Masood, Usama; Riaz, Muhammad Sajid; Ali, Kamran; John, Charles N.; Hussain, MD Iftikhar et al. (2020-01-01). "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app" (in en). Informatics in Medicine Unlocked 20: 100378. doi:10.1016/j.imu.2020.100378. ISSN 2352-9148. PMID 32839734. 
  7. ↑ Lau, Bryan; Duggal, Priya; Ehrhardt, Stephan; Armenian, Haroutune; Branas, Charles C; Colditz, Graham A; Fox, Matthew P; Hawes, Stephen E et al. (2020-07-01). "Perspectives on the Future of Epidemiology: A Framework for Training". American Journal of Epidemiology 189 (7): 634–639. doi:10.1093/aje/kwaa013. ISSN 0002-9262. PMID 32003778. 
  8. ↑ "Risk scores in real-time: the untapped potential of mobile health". https://global-uploads.webflow.com/601331581ba868154325e525/604287f3604756b49407a107_Hyfe%20Risk%20Score%20in%20Real-Time.pdf. 
  9. ↑ Faezipour, Miad; Abuzneid, Abdelshakour (2020-10-01). "Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds". Telemedicine and e-Health 26 (10): 1202–1205. doi:10.1089/tmj.2020.0114. ISSN 1530-5627. PMID 32487005. https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114. 
  10. ↑ Gabaldon-Figueira, Juan Carlos; Brew, Joe; Doré, Dominique Hélène; Umashankar, Nita; Chaccour, Juliane; Orrillo, Virginia; Tsang, Lai Yu; Blavia, Isabel et al. (2021-07-01). "Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study" (in en). BMJ Open 11 (7): e051278. doi:10.1136/bmjopen-2021-051278. ISSN 2044-6055. PMID 34215614. PMC 8257291. https://bmjopen.bmj.com/content/11/7/e051278. 
  11. ↑ "Hyfe AI - Measure cough as an objective clinical finding". https://www.hyfe.ai/. 
  12. ↑ Colón-González, Felipe J.; Lake, Iain R.; Morbey, Roger A.; Elliot, Alex J.; Pebody, Richard; Smith, Gillian E. (2018-04-24). "A methodological framework for the evaluation of syndromic surveillance systems: a case study of England". BMC Public Health 18 (1): 544. doi:10.1186/s12889-018-5422-9. ISSN 1471-2458. PMID 29699520. 
  13. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC" (in en-us). Emerging Infectious Diseases 10 (5): 858–864. doi:10.3201/eid1005.030646. PMID 15200820. https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article. 
  14. ↑ Faezipour, Miad; Abuzneid, Abdelshakour (2020-10-01). "Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds". Telemedicine and e-Health 26 (10): 1202–1205. doi:10.1089/tmj.2020.0114. ISSN 1530-5627. PMID 32487005. https://www.liebertpub.com/doi/abs/10.1089/tmj.2020.0114. 
  15. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC" (in en-us). Emerging Infectious Diseases 10 (5): 858–864. doi:10.3201/eid1005.030646. PMID 15200820. https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article. 
  16. ↑ Heffernan, Richard; Mostashari, Farzad; Das, Debjani; Karpati, Adam; Kulldorff, Martin; Weiss, Don (2004). "Syndromic Surveillance in Public Health Practice, New York City - Volume 10, Number 5—May 2004 - Emerging Infectious Diseases journal - CDC" (in en-us). Emerging Infectious Diseases 10 (5): 858–864. doi:10.3201/eid1005.030646. PMID 15200820. https://wwwnc.cdc.gov/eid/article/10/5/03-0646_article. 
  17. ↑ Gabaldon-Figueira, Juan Carlos; Brew, Joe; Doré, Dominique Hélène; Umashankar, Nita; Chaccour, Juliane; Orrillo, Virginia; Tsang, Lai Yu; Blavia, Isabel et al. (2021-07-02). "Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study". BMJ Open 11 (7): e051278. doi:10.1136/bmjopen-2021-051278. ISSN 2044-6055. PMID 34215614. 
  18. ↑ Clinica Universidad de Navarra, Universidad de Navarra (2021-09-08). Digital Acoustic Surveillance for Early Detection of Respiratory Disease Outbreaks: An Exploratory Observational Study in Navarra, Spain. Centre de Recherche du Centre Hospitalier de l'Université de Montréal. https://clinicaltrials.gov/ct2/show/NCT04762693. 
  19. ↑ 19.0 19.1 Rennoll, Valerie; McLane, Ian; Emmanouilidou, Dimitra; West, James; Elhilali, Mounya (2021). "Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope". IEEE Journal of Biomedical and Health Informatics 25 (5): 1542–1549. doi:10.1109/JBHI.2020.3020494. ISSN 2168-2208. PMID 32870803. 
  20. ↑ 20.0 20.1 Birring, S. S.; Fleming, T.; Matos, S.; Raj, A. A.; Evans, D. H.; Pavord, I. D. (2008-05-01). "The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough" (in en). European Respiratory Journal 31 (5): 1013–1018. doi:10.1183/09031936.00057407. ISSN 0903-1936. PMID 18184683. https://erj.ersjournals.com/content/31/5/1013. 
  21. ↑ Smith, Jaclyn A.; Ashurst, H. Louise; Jack, Sandy; Woodcock, Ashley A.; Earis, John E. (2006-01-25). "The description of cough sounds by healthcare professionals". Cough 2 (1): 1. doi:10.1186/1745-9974-2-1. ISSN 1745-9974. PMID 16436200. 



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