One of the saddest words in the dictionary to me is inequality. Simply put, there is unfairness in a system, a society, a country. Now, I believe that all men are created equal because we are of one race: the human race. So wherever, or whenever, there is unfairness or inequality, it is up to us, the human race, to address and rebuke such matter, because we are all created equal and we should all be treated with equal rights, consequence, and respect.
Dictionary of Inequalities
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The term health inequality can describe racial/ethnic disparities in US infant mortality rates, which are nearly three times higher for non-Hispanic blacks versus whites (15), as well as the fact that people in their 20s enjoy better health than those in their 60s (3). Of these two examples, only the difference in infant mortality would also be considered a health inequity. Health differences between those in their 20s versus 60s can be considered health inequalities but not health inequities. Health differences based on age are largely unavoidable, and it is difficult to argue that the health differences between younger and older people are unjust, since older people were once younger people and younger people, with some luck, will someday become old.
From a strictly utilitarian standpoint, the cost of health inequalities is staggering. Between 2003 and 2006 alone, the direct economic cost of health inequalities based on race or ethnicity in the United States was estimated at $230 billion. Researchers calculated that medical costs faced by African Americans, Asian Americans, and Hispanics were in excess by 30% due to racial and ethnic health inequalities, including premature death and preventable illnesses which reduced worker productivity. When indirect costs were factored into the calculations, the economic burden was estimated as $1.24 trillion (26). In addition to the costs that could be avoided if socially disadvantaged groups enjoyed equitable health outcomes, inequality itself may be harmful to health. A review of 155 papers that explored income inequality and population health found that health tends to be poorer in less equal societies, especially when inequality is measured at large geographic scales (27).
It can also be useful to compare outcomes across individuals within a single country. For example, applying this approach to the study of inequalities in BMI in India might yield data on the difference in BMI from the fattest to thinnest person. While examining inequalities across individuals provides important information on how outcomes are distributed, it does not allow us to understand who fares better or worse, and whether the gap between the healthy and sick is preventable or unjust. Despite this limitation, some researchers have argued that considering the overall health distribution of a population is especially useful for comparing health in different places because social groups are defined differently, and carry different meanings, across the world (8). For example, race is defined differently in the United States than it is in other countries, while social grouping according to caste is relevant for just a handful of countries, including India, Nepal, Pakistan, and Sri Lanka. Considering the overall health distribution of a population may also avoid making incorrect assumptions about what social groupings matter in a particular place. Despite the challenges associated with measuring and interpreting social inequalities in health, the remainder of this article focuses on health inequalities across social groups rather than individuals.
Researchers and consumers of information on health differences should carefully consider how social groups are constructed, as health inequality data can only be interpreted with respect to group composition. Some social groupings are based on categories of membership, as is in the case with religion or race, while others are created according to ordered or continuous levels of a given variable, such as education or income. Clearly defined membership categories grounded in theory and backed by a priori contextual knowledge can facilitate the study of health inequalities, though researchers will have to make decisions about when to collapse or further differentiate groups. For example, should Catholics and Protestants be broadly categorized under the umbrella Christian, or are denominational differences important? Is it meaningful to compare non-Hispanic whites to minorities in general, or does each racial/ethnic group require its own category? Increasingly complex considerations, including, for example, how race and ethnicity are defined, differentiated, and conceptualized (30, 31), add to the challenge of meaningfully comparing social groups. Such questions can only be answered with respect to the specific hypotheses being tested, or the disparities monitored, and should be grounded in context and theory. In general, however, it is important to be aware that group construction will drive the interpretation of health inequality data.
Life course perspective: A consideration of health inequalities that acknowledges that one's health status reflects both prior and contemporary conditions, including in utero and childhood effects. The life course perspective recognizes the impact of latent, pathway, and cumulative effects on later health.
One type of explanation points to material factors in the creation of health disparities. Material factors include food, shelter, pollution, and other physical risks and resources that influence health outcomes. Measures of absolute resources, such as absolute income, are useful in testing the role of material deprivation in creating health differences, as are objective measures of physical health risk factors such as air quality. An unequal distribution of physical health risks and resources across geographies and social groups contributes to social inequalities in health via material pathways.
A second class of explanation points to psychosocial (62) factors as driving health inequalities and social group differences in health in particular. Psychosocial health impacts stem from feelings of social exclusion, discrimination, stress, low social support, and other psychological reactions to social experiences. Negative psychological states affect physical health by activating the biological stress response, which can lead to increased inflammation, elevated heart rates, and blood pressure, among other outcomes (63, 64). Measures of relative position, perceived versus objectively measured variables, and instruments that capture different experiences of stress are all useful in studies of psychosocial risk factors. To the extent that certain social groups are systematically more likely to have stressful, demoralizing, and otherwise emotionally negative experiences, psychosocial factors can help explain health inequities.
A fourth type of explanation points to differences in biological health risk factors that are patterned across social groups or contexts (60, 68), or vary across individuals in a population. Biomedical explanations can suffer the same weaknesses as behavioral explanations for social inequalities in health when they focus on the downstream effects of social context without acknowledging why levels of biological risk factors vary across populations. Genetic and gene-by-environment interactions explanations are also, in part, biomedical in their nature. This class of explanation may be more useful for understanding variations in health observed across individuals in a population where social group differences are not the focus of investigation.
Even more difficult than executing well-designed studies of health inequalities is deciding what to study and how to use findings to narrow gaps between groups. A central task is deciding when a health inequality is inequitable, and why. Setting a policy agenda around health inequities is also fraught with difficult questions and decisions, including whether it is better to reduce absolute or relative health differences between groups; whether to focus on improving health for the worst-off groups or for the largest groups; and how to set benchmarks for health outcomes for various groups. For example, should we set the target life expectancy for black Americans to that of whites, or should we be aiming for both groups to live even longer? Are certain social groups or health outcomes more deserving of attention than others? If so, why? Do particularly unjust health differences deserve attention, or should we focus on health outcomes that are especially expensive or prevalent? What are the merits of investing resources into improving overall population health, and what are arguments for focusing on the elimination of health disparities instead?
I need to pass inequalities to a function for evaluation within the function. Is there a way to evaluation the inequality if passed as a string? Or must I pass a representation of the inequality and use if/else statements to generate the sign?
Now that I know you are processing user strings, I would definitely suggest creating a dictionary that maps strings to functions. (Perhaps that's what you meant in your title?) Passing userland strings into getattr seems bad in a number of ways. What happens if you want to add a string that doesn't correspond to an attribute of operator? What happens if the user passes in a string corresponding to a private attribute? Better to create a custom dictionary mapping strings to functions. A dict allows you to specify just those strings you want to accept.
This is a collection of some of the most important mathematical inequalities. I tried to include non-trivial inequalities that can be useful in solving problems or proving theorems, particularly in computer science. I omitted many details, in some cases even necessary conditions (hopefully only when they were obvious). If you are not sure whether an inequality can be applied in some context, try to find a more detailed source for the exact definition. For lack of space I omitted proofs and discussions on when equality holds. 2ff7e9595c
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