Andria MousaPeter WinskillOliver J. WatsonOliver RatmannMélodie MonodMarco AjelliAldiouma DialloPeter J. DoddCarlos G. GrijalvaMoses Chapa KitiAnand KrishnanRakesh KumarSupriya KumarKin On KwokClaudio F. LanataOlivier Le Polain De WarouxKathy LeungWiriya MahikulAlessia MelegaroCarl D. MorrowJoël MossongEleanor Fg NealDavid J. NokesWirichada Pan-NgumGail E. PotterFiona M. RussellSiddhartha SahaJonathan D. SugimotoWan In WeiRobin R. WoodJoseph T. WuJuanjuan ZhangPatrick Gt WalkerCharles WhittakerLaboratory of Data Discovery for HealthFaculty of Tropical Medicine, Mahidol UniversityFaculty of Medicine, Dentistry and HealthFaculty of Science, Engineering and MedicineThe University of Hong Kong Li Ka Shing Faculty of MedicineInstituto de Investigacion NutricionalWellcome Trust Research Laboratories NairobiLondon School of Hygiene & Tropical MedicineVanderbilt University Medical CenterIRD Institut de Recherche pour le DeveloppementNortheastern UniversityUniversity of MelbourneDepartment of Veterans AffairsChulabhorn Royal AcademyBill and Melinda Gates FoundationUniversità BocconiCenters for Disease Control and PreventionIndiana University BloomingtonImperial College LondonFaculty of Health SciencesFudan UniversityAll India Institute of Medical Sciences, New DelhiMurdoch Children's Research InstituteNational Institutes of Health (NIH)Fred Hutchinson Cancer Research CenterChinese University of Hong KongUniversity of WashingtonEmmes CompanyHealth Directorate2022-08-042022-08-042021-11-01eLife. Vol.10, (2021)2050084X2-s2.0-85120920571https://repository.li.mahidol.ac.th/handle/20.500.14594/75965Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focussed on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys we explored how contact characteristics (number, location, duration and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age-groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, but low-income settings were characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income-strata on the frequency, duration and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens, as well as the effectiveness of different non-pharmaceutical interventions.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyImmunology and MicrobiologyNeuroscienceArticleSCOPUS10.7554/eLife.70294