Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.
Comprehensive profiling of social mixing patterns in resource poor countries: a mixed methods research protocol.
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资源匮乏国家社会混合模式的综合分析：混合方法研究协议。
DOI：
10.1101/2023.12.05.23299472
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
U
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作者：
Aguolu,ObianujuGenevieve;Kiti,MosesChapa;Nelson,Kristin;Liu,CarolY;Sundaram,Maria;Gramacho,Sergio;Jenness,Samuel;Melegaro,Alessia;Sacoor,Charfudin;Bardaji,Azucena;Macicame,Ivalda;Jose,Americo;Cavele,Nilzio;Amosse,Felizarda;U
背景低收入和中等收入国家(LMIC)承担着不成比例的传染病负担。社会互动数据为传染病模型和疾病预防策略提供信息。不同年龄、文化和地点的人口统计和接触模式的差异对传染病动态和病原体传播产生重大影响。LMIC缺乏足够的社会互动数据用于传染病建模。方法为了弥补这一差距，我们将从危地马拉、印度、巴基斯坦和莫桑比克的八个研究地点(包括农村和城市环境)收集定性和定量数据。我们将进行焦点小组讨论和认知访谈，以评估我们的数据收集工具在每个地点的可行性和可接受性。专题和快速分析将有助于通过编码、指导设计量化数据收集工具(入学调查、联系日记、离职调查和可穿戴的接近感应器)和执行研究程序来确定关键主题和类别。我们将使用标准化接触日记中的数据在每个研究地点创建三个特定于年龄的联系矩阵(物理的、非物理的和两者)，以表征社会融合的模式。将进行回归分析，以确定接触的关键驱动因素。我们将使用来自接近传感器的高分辨率数据，全面描述婴儿与家庭成员互动的频率、持续时间和强度，并计算每个家庭成员的婴儿接近得分(每个家庭成员与婴儿接近的时间占婴儿接触总时间的比例)。讨论我们的定性数据有助于深入了解接触日记和可穿戴式接近传感器在LMIC中收集社交交往数据的感知和可接受性。量化数据将使人们能够更准确地描述人类通过近距离接触传播病原体的相互作用。我们的研究结果将为LMIC群体的数学模型的参数化提供更合适的社会混合数据。我们的研究工具可以适用于其他研究。
BackgroundLow-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.MethodsTo 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.DiscussionOur 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.