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      An equity dashboard to monitor vaccination coverage.

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          Abstract

          Equity monitoring is a priority for Gavi, the Vaccine Alliance, and for those implementing The2030 agenda for sustainable development. For its new phase of operations, Gavi reassessed its approach to monitoring equity in vaccination coverage. To help inform this effort, we made a systematic analysis of inequalities in vaccination coverage across 45 Gavi-supported countries and compared results from different measurement approaches. Based on our findings, we formulated recommendations for Gavi's equity monitoring approach. The approach involved defining the vulnerable populations, choosing appropriate measures to quantify inequalities, and defining equity benchmarks that reflect the ambitions of the sustainable development agenda. In this article, we explain the rationale for the recommendations and for the development of an improved equity monitoring tool. Gavi's previous approach to measuring equity was the difference in vaccination coverage between a country's richest and poorest wealth quintiles. In addition to the wealth index, we recommend monitoring other dimensions of vulnerability (maternal education, place of residence, child sex and the multidimensional poverty index). For dimensions with multiple subgroups, measures of inequality that consider information on all subgroups should be used. We also recommend that both absolute and relative measures of inequality be tracked over time. Finally, we propose that equity benchmarks target complete elimination of inequalities. To facilitate equity monitoring, we recommend the use of a data display tool - the equity dashboard - to support decision-making in the sustainable development period. We highlight its key advantages using data from Côte d'Ivoire and Haiti.

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          Most cited references15

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          On the measurement of inequalities in health.

          This paper offers a critical appraisal of the various methods employed to date to measure inequalities in health. It suggests that only two of these--the slope index of inequality and the concentration index--are likely to present an accurate picture of socioeconomic inequalities in health. The paper also presents several empirical examples to illustrate of the dangers of using other measures such as the range, the Lorenz curve and the index of dissimilarity.
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            Implicit value judgments in the measurement of health inequalities.

            Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. It is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable. We discuss five examples in which normative judgments play a role in the measurement process itself, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured. Overall, we find that many commonly used measures of inequality are value laden and that the normative judgments implicit in these measures have important consequences for interpreting and responding to health inequalities. Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data, we urge researchers to explicitly consider and transparently discuss the normative judgments underlying their measures. We also urge policymakers and other consumers of health inequalities data to pay close attention to the measures on which they base their assessments of current and future health policies.
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              • Article: not found

              Low P-values or narrow confidence intervals: which are more durable?

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                Author and article information

                Journal
                Bull. World Health Organ.
                Bulletin of the World Health Organization
                WHO Press
                1564-0604
                0042-9686
                Feb 01 2017
                : 95
                : 2
                Affiliations
                [1 ] Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada .
                [2 ] Statistics Canada, Ottawa, Canada .
                [3 ] Gavi, the Vaccine Alliance, Geneva, Switzerland .
                [4 ] Centre de recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada .
                Article
                BLT.16.178079
                10.2471/BLT.16.178079
                5327933
                28250513
                6ff3dc02-5f1f-447d-9b06-8f8f242b0420
                History

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