*3.3. How Equity Has Been Incorporated into Equity-Informative Economic Evaluations of Vaccines* 3.3.1. Overall Methods

All studies were cost-effectiveness analyses that performed health equity impact analyses to estimate the distributional impact of vaccines across equity-relevant subpopulations of interest (Table 2, with details in Table S4 in the Supplementary Materials). Eleven studies performed only health equity impact analysis as part of cost-effectiveness analyses to estimate the distributional impact and subpopulation incremental cost-effectiveness ratios (ICERs) of vaccines [23–32,34]. Nine studies are Extended Cost-Effectiveness Analyses that performed health equity impact analysis of vaccines with an estimation of the distributional financial risk protection [6–13,35]. One study is a Distributional Cost-Effectiveness Analysis that performed a health equity impact analysis of vaccines, incorporating equity-weighting and opportunity costs as the money was displaced to be spent on vaccines instead of other health services [33]. All studies used static models, of which herd protection of vaccines was considered in a base-case analysis in one study [25] and in a scenario analysis in four studies [10,13,32,34].

#### 3.3.2. Existing Inequities across Equity-Relevant Subpopulations

These analyses were designed to simulate the distributional impact of vaccines within the existing health inequities across the equity-relevant subpopulation in the context of interest, where there were differences between more or less socially disadvantaged subpopulations. Existing inequities in these studies were inequities in disease mortality (*n* = 17, 81%) [7–9,11,13,23,25–35], vaccination coverage (*n* = 12, 57%) [7,8,11,12,23,25,27–30,33,35], disease incidence/prevalence (*n* = 11, 52%) [6,7,12,24–26,31–35], and financial risk (*n* = 9, 43%) [6–13,35].

Equity-relevant subpopulations of interest were socioeconomic status (*n* = 11, 52%) [6–13,27,33,35], race/ethnicity (*n* = 3, 14%) [26,31,32], and place of residence (regions, states, or rural/urban areas) (*n* = 2, 10%) [24,34]. The other five studies assessed the combination of characteristics of equity-relevant subpopulations (socioeconomic status, race/ethnicity, place of residence, and gender) [23,25,28–30], such as estimating the distributional effect of rotavirus vaccine across rural/urban areas, regions, gender, and income quintiles in India [28].

Socioeconomic status was categorized as income quintiles [6–13,23,27–30,33,35] or tertiles [25], ranging from the poorest to the richest. Income quintiles were defined using an asset index [23,28–30], gross domestic product per capita, Gini coefficient [8,13,35], and the National Demographic Health Survey [10,12]. However, some studies did not report how socioeconomic status was defined [6,7,9,11,25,27,33]. Regions were categorized following the National Demographic Health Survey [23,28,30]. There was no clear description of how rural and urban areas were defined [24,28,29].
