Fang Di, Sun Renyuan, Wilson Jeffrey R
Department of Agricultural Economics and Agribusiness, University of Arkansas, Fayetteville, AR, United States of America.
School of Mathematical and Statistical Science, Arizona State University, Tempe, AZ, United States of America.
PLoS One. 2018 Jan 19;13(1):e0190917. doi: 10.1371/journal.pone.0190917. eCollection 2018.
Recent advances in statistical methods enable the study of correlation among outcomes through joint modeling, thereby addressing spillover effects. By joint modeling, we refer to simultaneously analyzing two or more different response variables emanating from the same individual. Using the 2011 Bangladesh Demographic and Health Survey, we jointly address spillover effects between contraceptive use (CUC) and knowledge of HIV and other sexually transmitted diseases. Jointly modeling these two outcomes is appropriate because certain types of contraceptive use contribute to the prevention of HIV and STDs and the knowledge and awareness of HIV and STDs typically lead to protection during sexual intercourse. In particular, we compared the differences as they pertained to the interpretive advantage of modeling the spillover effects of joint modeling HIV and CUC as opposed to addressing them separately. We also identified risk factors that determine contraceptive use and knowledge of HIV and STDs among women in Bangladesh. We found that by jointly modeling the correlation between HIV knowledge and contraceptive use, the importance of education decreased. The HIV prevention program had a spillover effect on CUC: what seemed to be impacted by education can be partially contributed to one's exposure to HIV knowledge. The joint model revealed a less significant impact of covariates as opposed to both separate models and standard models. Additionally, we found a spillover effect that would have otherwise been undiscovered if we did not jointly model. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed for the commonality between different responses and deflate, which is otherwise overestimated when examined separately.
统计方法的最新进展使得通过联合建模来研究结果之间的相关性成为可能，从而解决溢出效应问题。通过联合建模，我们指的是同时分析来自同一个体的两个或更多不同的响应变量。利用2011年孟加拉国人口与健康调查，我们联合解决了避孕措施使用（CUC）与艾滋病毒及其他性传播疾病知识之间的溢出效应。对这两个结果进行联合建模是合适的，因为某些类型的避孕措施有助于预防艾滋病毒和性传播疾病，而对艾滋病毒和性传播疾病的了解和认识通常会在性交过程中起到保护作用。特别是，我们比较了将艾滋病毒与CUC的溢出效应进行联合建模与分别处理这两种效应在解释优势方面的差异。我们还确定了孟加拉国女性中决定避孕措施使用以及对艾滋病毒和性传播疾病了解程度的风险因素。我们发现，通过对艾滋病毒知识与避孕措施使用之间的相关性进行联合建模，教育的重要性有所降低。艾滋病毒预防项目对CUC有溢出效应：看似受教育影响的部分因素可归因于个人对艾滋病毒知识的接触。与单独模型和标准模型相比，联合模型显示协变量的影响较小。此外，我们发现了一种溢出效应，如果不进行联合建模，这种效应原本是不会被发现的。这些发现进一步表明，对于不同响应之间的共性，相关结果的同时影响可以得到充分解决并被缩减，否则在单独研究时会被高估。
