Objectives: To test the inverse equity hypothesis, which postulates that new health interventions are initially adopted by the wealthy and thus increase inequalities-as population coverage increases, only the poorest will lag behind all other groups.
Methods: We analyzed the proportion of births occurring in a health facility by wealth quintile in 286 surveys from 89 low- and middle-income countries (1993-2015) and developed an inequality pattern index. Positive values indicate that inequality is driven by early adoption by the wealthy (top inequality), whereas negative values signal bottom inequality.
Results: Absolute inequalities were widest when national coverage was around 50%. At low national coverage levels, top inequality was evident with coverage in the wealthiest quintile taking off rapidly; at 60% or higher national coverage, bottom inequality became the predominant pattern, with the poorest quintile lagging behind.
Conclusions: Policies need to be tailored to inequality patterns. When top inequalities are present, barriers that limit uptake by most of the population must be identified and addressed. When bottom inequalities exist, interventions must be targeted at specific subgroups that are left behind.