Improving health in the most disadvantaged populations requires reliable estimates of health and demographic indicators to inform policy and interventions. Low- and middle-income countries with the largest burden of disease and disability tend to have the least comprehensive data, relying primarily on household surveys. Subnational estimates are increasingly used to inform targeted interventions and health policies. Producing reliable estimates from these data at fine geographical scales requires statistical modeling, and small area estimation models are commonly used in this context. Although most current methods model univariate outcomes, improved estimates may be attained by borrowing strength across related outcomes via multivariate modeling. In this paper, we develop classes of area- and unit-level multivariate shared component models using complex survey data. This framework jointly models multiple outcomes to improve accuracy of estimates compared to separately fitting univariate models. We conduct simulation studies to validate the methodology and use the proposed approach on survey data from Kenya in 2014; first, to jointly model height-for-age and weight-for-age in children, and second, to model three categories of contraceptive use in women. These models produce improved estimates compared to univariate and naive multivariate modeling approaches.

翻译：改善最弱势人群的健康状况需要可靠的健康与人口指标估计值，以指导政策制定和干预措施。疾病和残疾负担最重的低收入和中等收入国家往往数据最不全面，主要依赖家庭调查。次国家级估计值正越来越多地用于指导针对性干预和卫生政策。要在精细地理尺度上从这些数据中生成可靠估计，需要统计建模，而小区域估计模型在此背景下被广泛使用。尽管当前多数方法针对单变量结果建模，但通过多变量建模在相关结果间借用信息，可获得更优的估计。本文基于复杂调查数据，开发了区域层次和单元层次的多变量共享成分模型类别。该框架通过联合建模多个结果，相比单独拟合单变量模型，提高了估计的准确性。我们通过模拟研究验证了该方法，并将所提方法应用于2014年肯尼亚的调查数据：首先联合建模儿童年龄别身高和年龄别体重，其次建模女性避孕措施使用的三个类别。与单变量及简单多变量建模方法相比，这些模型能生成更优的估计结果。