Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic
Abstract Background During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants’ “survey fatigue”, which may impact i...
| Principais autores: | , , , , , , , , , , , , , , |
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| Formato: | Artigo |
| Idioma: | English |
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BMC
2023-07-01
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| coleção: | BMC Public Health |
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| Acesso em linha: | https://doi.org/10.1186/s12889-023-16193-7 |
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| author | Neilshan Loedy Pietro Coletti James Wambua Lisa Hermans Lander Willem Christopher I. Jarvis Kerry L. M. Wong W. John Edmunds Alexis Robert Quentin J. Leclerc Amy Gimma Geert Molenberghs Philippe Beutels Christel Faes Niel Hens |
| author_facet | Neilshan Loedy Pietro Coletti James Wambua Lisa Hermans Lander Willem Christopher I. Jarvis Kerry L. M. Wong W. John Edmunds Alexis Robert Quentin J. Leclerc Amy Gimma Geert Molenberghs Philippe Beutels Christel Faes Niel Hens |
| author_sort | Neilshan Loedy |
| collection | DOAJ |
| description | Abstract Background During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants’ “survey fatigue”, which may impact inferences. Methods A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. Results Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ( $$R_0$$ R 0 ) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. Conclusions CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys. |
| first_indexed | 2024-03-13T00:38:39Z |
| format | Article |
| id | doaj.art-4faefdd6f5c34f1283fe901b114b7c11 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-13T00:38:39Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-4faefdd6f5c34f1283fe901b114b7c112023-07-09T11:27:20ZengBMCBMC Public Health1471-24582023-07-0123111810.1186/s12889-023-16193-7Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemicNeilshan Loedy0Pietro Coletti1James Wambua2Lisa Hermans3Lander Willem4Christopher I. Jarvis5Kerry L. M. Wong6W. John Edmunds7Alexis Robert8Quentin J. Leclerc9Amy Gimma10Geert Molenberghs11Philippe Beutels12Christel Faes13Niel Hens14Data Science Institute, I-BioStat, Hasselt UniversityData Science Institute, I-BioStat, Hasselt UniversityData Science Institute, I-BioStat, Hasselt UniversityData Science Institute, I-BioStat, Hasselt UniversityCentre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of AntwerpCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineCentre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical MedicineData Science Institute, I-BioStat, Hasselt UniversityCentre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of AntwerpData Science Institute, I-BioStat, Hasselt UniversityData Science Institute, I-BioStat, Hasselt UniversityAbstract Background During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants’ “survey fatigue”, which may impact inferences. Methods A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. Results Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ( $$R_0$$ R 0 ) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. Conclusions CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.https://doi.org/10.1186/s12889-023-16193-7Bias assessmentSocial contact dataCOVID-19SARS-CoV-2Survey fatigueUnder-reporting |
| spellingShingle | Neilshan Loedy Pietro Coletti James Wambua Lisa Hermans Lander Willem Christopher I. Jarvis Kerry L. M. Wong W. John Edmunds Alexis Robert Quentin J. Leclerc Amy Gimma Geert Molenberghs Philippe Beutels Christel Faes Niel Hens Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic BMC Public Health Bias assessment Social contact data COVID-19 SARS-CoV-2 Survey fatigue Under-reporting |
| title | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic |
| title_full | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic |
| title_fullStr | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic |
| title_full_unstemmed | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic |
| title_short | Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic |
| title_sort | longitudinal social contact data analysis insights from 2 years of data collection in belgium during the covid 19 pandemic |
| topic | Bias assessment Social contact data COVID-19 SARS-CoV-2 Survey fatigue Under-reporting |
| url | https://doi.org/10.1186/s12889-023-16193-7 |
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