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Politics is increasingly reliant on numerical descriptions of the world. Numbers are relied upon for their ability to communicate some unambiguous facts of life. Equivalence frames are equivalent descriptions of the same quantity and they help us understand how different ways of presenting the objectively same piece of numerical information affect political behavior. Equivalence framing effects denote that these different presentation of the fundamentally same fact have very profound effects on preferences. However, most research in political behavior have relied on other forms of framing and largely regarded equivalence framing as a well-defined concept without much relevance to real-world politics. The standard form of equivalence framing changes the valence of a label which describes the same numerical fact. This form of negative and positive framing of the same metric will often elicit very different responses for the recipient of the information. A less studied type of equivalence framing in political behavior manipulates the same numerical fact but with a different metric or scale. These have often not been explicitly recognized as equivalence frames but are clearly an important example in a world of numbers. As for valence manipulation, changing the metric can also have profound effects. Moving forward studies of equivalence framing must both gain a better descriptive understanding of the actual use and abuse of equivalence frames in observational setting and at the same time aim to understand the causal properties of equivalence frames in the field—outside the controlled environment of the survey or lab where they most often are studied.

There is one common health objective among all nations, as stated in the constitution of the World Health Organization in 1947: progress towards the best feasible level of health for all people. This goal captures the concept of health equity: fair distribution of unequal health. However, 70 years later, this common global objective has never been measured. Most of the available literature focuses on measuring health inequalities, not inequities, and compare health indicators (mainly access to health services) among population subgroups. A method is hereby proposed to identify standards for the best feasible levels of health through criteria of healthy, replicable, and sustainable (HRS) models. Once the HRS model countries were identified, adjusted mortality rates were applied to age- and sex-specific populations from 1950 to 2015, by calculating the net difference between the observed and expected mortality, using the HRS countries as the standard. This difference in mortality represents the net burden of health inequity (NBHiE), measured in avoidable deaths. This burden is due to global health inequity, that is, unfair inequality, due to social injustice. We then calculated the relative burden of health inequity (RBHiE), which is the proportion of NBHiE compared with all deaths. The analysis identified some 17 million avoidable deaths annually, representing around one-third of all deaths during the 2010–2015 period. This avoidable death toll (NBHiE) and proportion (RBHiE) have not changed much since the 1970s. Younger age groups and women are affected the most. When data were analyzed using smaller sample units (such as provinces, states, counties, or municipalities) in some countries, the sensitivity was increased and could detect higher levels of burden of health inequity. Most of the burden of health inequity takes place in countries with levels of income per capita below the average of the HRS countries, which we call the “dignity threshold.” Based on this threshold, a distribution of the world’s resources compatible with the universal right to health—the “equity curve”—is estimated. The equity curve would hypothetically be between this dignity threshold and a symmetric upper threshold around the world’s average per capita GDP. Such excess income prevents equitable distribution is correlated with a carbon footprint leading to >1.5º global warming (thus undermining the health of coming generations), and does not translate to better health or well-being. This upper threshold is defined as the “excess accumulation threshold.” The international redistribution required to enable all nations to have at least an average per capita income above the dignity threshold would be around 8% of the global GDP, much higher than the present levels of international cooperation. At subnational levels, the burden of health inequity can be the most sensitive barometer of socioeconomic justice between territories and their populations, informing and directing fiscal and territorial equity schemes and enabling all people within and between nations to enjoy the universal right to health. HRS models can also inspire lifestyles, and political and economic frameworks of ethical well-being, without undermining the rights of others in present and future generations.

Monitoring and evaluation (M&E) can be defined as the systematic collection, analysis, and use of data to answer questions about program performance and achievements. An M&E system encompasses all the activities related to setting up, collecting, reporting, and using program information. A robust, well-functioning M&E system can provide program stakeholders with the information necessary to carry out a responsive and successful program intervention and is therefore a critical tool for program management. There are many tools and techniques needed for successful M&E of sexual and reproductive health (SRH) programs. These include frameworks to visually depict the organization of the program, its context and goals, and the logic of its M&E system. Essential practices of M&E also include continuous stakeholder engagement, the development of indicators to measure program activities and outcomes, the collection and use of data to calculate the indicators, and the design and implementation of evaluation research to assess the benefits of the program. Over time, language around “M&E” has evolved, and multiple variations of the phrase are in use, including “MEL” (monitoring, evaluation, and learning), “MER” (monitoring, evaluation, and reporting), and “MERL” (monitoring, evaluation, research, and learning), to name but a few. These terms bring to the forefront a particular emphasis of the M&E system, with an apparent trend toward the use of “MEL” to emphasize the importance of organizational learning. Despite this trend, “M&E” continues to be the most widely known and understood phrase and implicitly includes activities such as learning, research, and reporting within a robust system.