RESUMO
BACKGROUND: Over time, demographic and health survey (DHS) data remain valuable to examine variables relating to nationally representative population outcomes for low- and middle-income countries. In Tanzania, there are very limited DHS-based studies on the uptake of Modern Contraceptive Use (MCU). Present studies have focused on measurements at the level of individuals, yet research has shown that MCU variations exists at other levels within populations. In this study, we use a variance component modelling approach to explore variation in MCU at primary sampling unit (PSU) and regional levels while considering survey sample weights. METHODS: Using DHS data from 2016-2017 in Tanzania, we study different variance structures and the respective variation on MCU in a sample of 5263 Women of Reproductive Age (WRA) defined as between the ages of 15-49 years. First, a single variance component was used, followed by its extension to a random coefficient model and we tracked changes in the models. RESULTS: There was an influence of random variations on MCU on the levels of populations much explained by PSU-level clustering than region. On the fixed part, age of a woman, husband education level, desire to have children, and exposure to media and wealth tertiles were important determinants for MCU. Compared to WRA in 15-19 years, the odds of MCU among middle aged women (20-29 and 30-39 years) were 1.94 (95%CI:1.244-3.024) and 2.28 (95%CI:1.372-3.803). Also, increases in media exposure and middle and rich wealth tertiles women led to higher odds for MCU. We also found the presence of random effects influence of wealth tertiles levels on MCU. CONCLUSION: This study highlighted the utility of accounting for variance structures in addressing determinants of MCU while using DHS national level data. Apart from MCU, the DHS data have been widely applied to examine other variables pertaining to public health issues. This approach could be considered a better modelling technique for the DHS studies compared to traditional survey approaches, and to guide hierarchical population-based interventions to increase MCU.
