摘要：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. Tudor Hart’s inverse care law, enunciated in 1979, stated that availability of good medical care tends to vary inversely with the need for it in the population served . 1 In 2000, the authors of a Lancet article proposed a corollary to this law: the inverse equity hypothesis, 2 which stated that newly introduced health interventions would be initially adopted by the wealthier segments of a population, who likely had the least need for such interventions. They suggested that absolute health inequalities would therefore increase in the short term and would only decline as the intervention gradually reaches the most deprived sectors of the population, by which time coverage among the most privileged sectors was already close to 100%. In 2005, the same authors elaborated on the hypothesis 3 and referred to top inequality as the early adoption pattern 4 in which the wealthy picked up the service and showed substantially higher coverage than the rest of the population, and to bottom inequality as the late pattern when high coverage was reached by most groups, except for the poorest who were still lagging behind. Independently, the 2005 World Health Report 5 described similar patterns, referring to top inequality as “mass deprivation” and to bottom inequality as “marginal exclusion.” We avoid the expression mass deprivation because, as we will demonstrate, the top inequality pattern may include relatively high coverage among the wealthiest, who therefore cannot be regarded as deprived. The Lancet article received 347 citations so far in the Scopus database ( https://www.scopus.com ) and more than 650 in Google Scholar ( https://goo.gl/OHYPUo ) up to September 22, 2017. We were able to obtain copies of 329 of the Scopus citations, of which 191 cited the article without testing the hypothesis, and 138 tested it with their own data: according to the authors, 90 studies (72%) supported and 31 (28%) rejected the hypothesis. Studies that supported the hypothesis addressed many different outcomes including breast and cervical cancer screening, 6 blood pressure monitoring, 7 child immunization, 8 dental caries prevention, 9 management of coronary heart disease, 10 HIV/AIDS screening and treatment, 11,12 insecticide-treated nets, 13 and smoking cessation. 14 Of particular interest are articles in which the outcomes were based on attitudes related to the perceived benefits or harms associated with certain interventions, even when these were not evidence-based. For example, the authors of articles on cesarean deliveries without medical indication 15,16 and on refusal to vaccinate children in the United Kingdom 17,18 quoted the inverse equity hypothesis to explain why these behaviors were initially adopted by the better-off within a population. Most authors who failed to detect the sequence of events postulated by the hypothesis were describing the impact of programs that were specifically designed to reduce inequalities. Examples include voucher schemes for maternal services in Bangladesh 19 and Korea, 20 community-wide vitamin A treatment of children in Nepal, 21 improving poor women’s access to institutional delivery in Burkina Faso, 22 equity-focused maternal and child health programs in Cambodia, 23 and free access to antiretroviral treatment for HIV in Brazil. 24 Growing availability of data from low- and middle-income countries allowed us to test the hypothesis in national surveys that provided information on socioeconomic position and on the proportion of births occurring in a health facility (institutional delivery coverage). We chose this outcome because it is measured in many surveys with high validity, 25 usually presenting marked socioeconomic gradients, 26,27 and because delivery by a skilled attendant in a health facility is a key intervention to reduce maternal and newborn mortality. 28,29
