TY - JOUR
T1 - Comprehensive profiling of social mixing patterns in resource poor countries
T2 - A mixed methods research protocol
AU - Aguolu, Obianuju Genevieve
AU - Kiti, Moses Chapa
AU - Nelson, Kristin
AU - Liu, Carol Y.
AU - Sundaram, Maria
AU - Gramacho, Sergio
AU - Jenness, Samuel
AU - Melegaro, Alessia
AU - Sacoor, Charfudin
AU - Bardaji, Azucena
AU - Macicame, Ivalda
AU - Jose, Americo
AU - Cavele, Nilzio
AU - Amosse, Felizarda
AU - Uamba, Migdalia
AU - Jamisse, Edgar
AU - Tchavana, Corssino
AU - Briones, Herberth Giovanni Maldonado
AU - Jarquín, Claudia
AU - Ajsivinac, María
AU - Pischel, Lauren
AU - Ahmed, Noureen
AU - Mohan, Venkata Raghava
AU - Srinivasan, Rajan
AU - Samuel, Prasanna
AU - John, Gifta
AU - Ellington, Kye
AU - Joaquim, Orvalho Augusto
AU - Zelaya, Alana
AU - Kim, Sara
AU - Chen, Holin
AU - Kazi, Momin
AU - Malik, Fauzia
AU - Yildirim, Inci
AU - Lopman, Benjamin
AU - Omer, Saad B.
N1 - Publisher Copyright:
© 2024 Aguolu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/6
Y1 - 2024/6
N2 - Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
AB - Background Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling. Methods To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member. Discussion Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
UR - http://www.scopus.com/inward/record.url?scp=85196772233&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0301638
DO - 10.1371/journal.pone.0301638
M3 - Article
AN - SCOPUS:85196772233
SN - 1932-6203
VL - 19
JO - PLoS ONE
JF - PLoS ONE
IS - 6 June
M1 - e0301638
ER -