Circulating Illness and Changes in Thermometer Use Behavior: Series of Cross-sectional Analyses
Notice bibliographique
Résumé
BACKGROUND: Temperature-taking behaviors vary with levels of circulating infectious illness; however, little is known about how these behaviors differ by demographic characteristics. Populations with higher perceived risks of illness are more likely to adopt protective health behaviors. OBJECTIVE: We investigated differences in temperature-taking frequency and the proportion of readings that were feverish among demographic groups (age, gender, urban/rural status) over influenza offseason; influenza season; and waves 1, 2, and 3 of the COVID-19 pandemic. METHODS: Using data from smart thermometers collected from May 1, 2019, to February 28, 2021, across the United States, we calculated the frequency of temperature-taking and the proportion of temperature readings that were feverish. Mixed-effects negative binomial and mixed-effects logistic regression analyses were performed to identify demographic characteristics associated with temperature-taking frequency and the proportion of feverish readings, respectively. Separate models were fit over five study periods: influenza offseason (n=122,480), influenza season (n=174,191), wave 1 of COVID-19 (n=350,385), wave 2 (n=366,489), and wave 3 (n=391,578). RESULTS: Both temperature-taking frequency and the proportion of feverish readings differed by study period (ANOVA P<.001) and were the highest during influenza season. During all periods, children aged 2-5 years and 6-11 years had significantly higher frequencies of temperature-taking than users aged 19-30 years, and children had the highest proportion of feverish readings of all age groups, after adjusting for covariates. During wave 1 of COVID-19, users over the age of 60 years had 1.79 times (95% CI 1.76-1.83) the rate of temperature-taking as users aged 19-30 years and 74% lower odds (95% CI 72%-75%) of a reading being feverish. Across all periods, men had significantly lower temperature-taking frequency and significantly higher odds of having a feverish reading compared to women. Users living in urban areas had significantly higher frequencies of temperature-taking than rural users during all periods, except wave 2 of COVID-19, and urban users had higher odds of a reading being feverish in all study periods except wave 1 of COVID-19. CONCLUSIONS: Temperature-taking behavior and the proportion of readings that were feverish are associated with both population disease levels and individual demographic characteristics. Differences in the health behavior of temperature-taking may reflect changes in both perceived and actual illness risk. Specifically, older adults may have experienced an increase in perceived risk during the first three waves of COVID-19, leading to increased rates of temperature monitoring, even when their odds of fever were lower than those of younger adults. Men's perceived risk of circulating infectious illnesses such as influenza and COVID-19 may be lower than that of women, since men took their temperature less frequently and each temperature had a higher odds of being feverish across all study periods. Infectious disease surveillance should recognize and incorporate how behavior impacts illness monitoring and testing.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».