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Sounds of sickness: can people identify infectious disease using sounds of coughs and sneezes?

ABSTRACT: Cough, cough. Is that person sick, or do they just have a throat tickle? A growing body of research suggests pathogen threats shape key aspects of human sociality. However, less research has investigated specific processes involved in pathogen threat detection. Here, we examine whether perceivers can accurately detect pathogen threats using an understudied sensory modality-sound. Participants in four studies judged whether cough and sneeze sounds were produced by people infected with a communicable disease or not. We found no evidence that participants could accurately identify the origins of these sounds. Instead, the more disgusting they perceived a sound to be, the more likely they were to judge that it came from an infected person (regardless of whether it did). Thus, unlike research indicating perceivers can accurately diagnose infection using other sensory modalities (e.g. sight, smell), we find people overperceive pathogen threat in subjectively disgusting sounds.

SUBMITTER: Michalak NM

PROVIDER: S-EPMC7341931 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Sounds of sickness: can people identify infectious disease using sounds of coughs and sneezes?

Michalak Nicholas M NM Sng Oliver O Wang Iris M IM Ackerman Joshua J

Proceedings. Biological sciences 20200610 1928

Cough, cough. Is that person sick, or do they just have a throat tickle? A growing body of research suggests pathogen threats shape key aspects of human sociality. However, less research has investigated specific processes involved in pathogen threat detection. Here, we examine whether perceivers can accurately detect pathogen threats using an understudied sensory modality-sound. Participants in four studies judged whether cough and sneeze sounds were produced by people infected with a communica ...[more]

PMID: 32517611

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Project description:BackgroundAn important prerequisite for the Chronic Care Model is to be able to identify, in a valid, simple and inexpensive way, the population with a chronic condition that needs proactive and planned care. We investigated if a set of administrative data could be used to identify patients with Chronic Obstructive Pulmonary Disease in a Danish population.MethodsSeven general practices were asked to identify patients with known Chronic Obstructive Pulmonary Disease in their practices. For the 266 patients (population A), we used administrative data on hospital admissions for lung-related diagnoses, redeemed prescriptions for lung-diseases drugs and lung- function tests combined to develop an algorithm that identified the highest proportion of patients with Chronic Obstructive Pulmonary Disease with the fewest criteria involved. We tested nine different algorithms combining two to four criteria. The simplest algorithm with highest positive predictive value identified 532 patients (population B); with possible diagnosis of Chronic Obstructive Pulmonary Disease in five general practices. The doctors were asked to confirm the diagnosis. The same algorithm identified 2,895 patients whom were asked to confirm their diagnosis (population C).ResultsIn population A the chosen algorithm had a positive predictive value of 72.2 % and three criteria: a) discharged patients with a chronic lung-disease diagnosis at least once during the preceding 5 years; or b) redeemed prescription of lung-medication at least twice during the preceding 12 months; or c) at least two spirometries performed at different dates during the preceding 12 months. In population B the positive predictive value was 65.0 % [60.8;69.1 %] and the sensitivity 44.8 % [41.3;48.4 %)] when the "uncertain" were added to where doctors agreed with the diagnosis. For the 1,984 respondents in population C, the positive predictive value was 72.9 % [70.8;74.8 %] and the sensitivity 29.7 % [28.4;31.0 %].ConclusionsAn algorithm based on administrative data has been developed and validated with sufficient positive predictive value to be used as a tool for identifying patients with Chronic Obstructive Pulmonary Disease. Some of the identified patients had other chronic lung-diseases (asthma). The algorithm should mostly be regarded as a tool for identifying chronic lung-disease and further development of the algorithm is needed.Trial registrationwww.clinicaltrials.gov (NCT01228708).

Dataset Information

Sounds of sickness: can people identify infectious disease using sounds of coughs and sneezes?

Publications

Sounds of sickness: can people identify infectious disease using sounds of coughs and sneezes?

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