Addressing Health Disparities: Strategies Within European Health Insurance

Addressing Health Disparities: Strategies Within European Health Insurance – Editing: Schlotheuber, A.; Hossenpohr, A.R. Summary of health inequality measures: an internal review of existing measures and their application. J. Environ. Resolution Public Health 2022, 19, 3697.

Integrating socio-economic status and spatial factors to improve access to community care resources by maximizing capacity allocation.

Addressing Health Disparities: Strategies Within European Health Insurance

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National Institute On Minority Health And Health Disparities

By Anne Schlotheuber Anne Schlotheuber Scilit Preprints.org Google Scholar and Ahmad Reza Hosseinpoor Ahmad Reza Hosseinpoor Scilit Preprints.org Google Scholar*

Received: January 31, 2022 / Revised: March 14, 2022 / Accepted: March 16, 2022 / Published: March 20, 2022 / Revised: June 7, 2022

Measuring and monitoring health inequalities is key to achieving health equity. Although fragmented data have been used to assess health differences between different population subgroups, it is not uncommon. But summary measures of health inequality play an important role in monitoring health inequality. Built from fragmented data They measure the degree of inequality in single numbers. and is useful in comparing inequalities over time and between different health indicators, programs, and settings. We provide a comprehensive overview of existing health inequality summary measures. including definitions, calculations, interpretations, and applications The use of these measures is illustrated by examples from the WHO Health Equity Monitor database using the WHO’s Health Equity Assessment Toolkit (HEAT) software. We will discuss the strengths and limitations of the measures. and provide guidance on selecting appropriate summary measures for analyzing health inequalities and communicating the results. Outline measures on health inequality should be part of health inequality monitoring to inform equity-focused policies and programs.

Measuring and monitoring health inequalities is essential for achieving health equity. This is a key commitment of the World Health Organization (WHO) and a key goal of the 2030 Agenda for Sustainable Development [1, 2]. (observable health differences between different sub-groups of the population) to inform policies and programs aimed at addressing health inequalities. (Unfair health inequality avoidable In the context of the Sustainable Development Goals (SDGs), fragmented data has been given special visibility and recognition for examining inequalities, with SDG goal 17.18 clearly calling for an increase. of high-quality, timely, and reliable discrete data [3]. Discrete data is, in fact, an important type of data inequality. Because these data divide national averages and enable the identification of patterns of inequality in surviving populations and subgroups. In addition to fragmented information Summarized measures of health inequality also play an important role in examining health inequalities. Summary measures are built from discrete data. It measures the degree of inequality in the population as a single number. thus allowing comparisons of health inequalities over time. and various settings and indicators easily

Health Equity In Healthy People 2030

Several previous works from the 1990s and 2000s have described existing summary measures of health inequality [4, 5, 6, 7, 8, 9, 10]. Turn of the century is a combined measure of social inequality in health [11, 12, 13]. Measures of total inequality, such as the Gini coefficient, consider only the distribution of health indicators in a population. while social inequality measures assess how health indicators differ according to different demographic, economic, social or geographical characteristics. In the context of monitoring health inequality, the term “summary measure of health inequality” often refers to a measure of social inequality. Because it’s the main point of interest. There are a number of summary measures. Each measure has different characteristics. which can lead to different conclusions about the magnitude and direction of the inequality [5, 9]. It is therefore important to consider several methodological issues when calculating summary measures and choosing a set. Appropriate measures for reporting results [10, 14].

The aim of this paper is to provide a current and systematic overview of how health inequities exist. These are all contained in the World Health Organization’s Health Equality Assessment Toolkit (HEAT). [15] Software applications have been developed to facilitate the assessment of health inequalities in countries. and calculate various summary measures according to separate data After discussion of the important methodological considerations for calculating summary measures, We have provided a detailed description of the measurements calculated in HEAT, including their definitions, calculations, interpretations, and implementations. The use of these measures is illustrated as an example from the WHO Health Equity Monitor database after presentation of the results. We will discuss the strengths and limitations of the measures. and provide guidance on selecting appropriate measures for analyzing and communicating the results effectively.

There are several important considerations to consider when calculating summary measures of health inequality [14, 16, 17]. First, not all measures can be calculated for all data types. Therefore, it is necessary to take into account the nature of the baseline data on health indicators and the inequality dimensions. of inequality It is therefore important to consider the inherent properties of each summary measure and the purpose of the analysis. It is worth noting that at this point some summary measures can be calculated from individual or group-level data. We focus on calculating summary measures based on group-level or discrete data only. The following section briefly describes the relevant characteristics for calculating summary measures.

Health indicators may measure various aspects of health, including inputs and processes, outputs, outcomes and impacts. As outlined in the World Health Organization Monitoring, Evaluation and Review Framework [18, 19], the WHO Global Reference List for 100 Key Health Indicators presents a set of standardized health indicators that are prioritized for: Global and national health monitoring [20]. Examples include health facility density (input), access to medicines (output), antenatal care coverage (outcome), and under-five mortality (impact). Each indicator has a defined unit of measurement (eg number, rate, proportion or percentage) and a level that is best achieved or maintained by public health action. For the two types of health indicators, optimal levels are clearly defined: for satisfactory indicators The goal is to achieve the highest level, i.e. total coverage of antenatal care or the highest possible life expectancy. while for the undesirable indicator The goal is to recognize thresholds such as zero prevalence of stunting or zero mortality in children under five. There are also health indicators that do not fall under either of these categories. such as the fertility rate Caesarean section rate or hospital admission rate for these indicators The optimum value is neither the maximum nor the minimum. But it depends a lot on settings and context.

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The dimension of inequality refers to demographic, economic, social or geographical characteristics. It considers a population that can be subdivided into various subgroups [21, 22]. A commonly used dimension of inequality for examining global health inequalities. As it provides comparable data for a large number of countries, including age, economic status, education, place of residence and gender [23], it is also recommended that these dimensions be used as a basis for data breakdown by SDGs, as well as for other dimensions. other that may be related It depends on settings and contexts such as disability, ethnicity, language, immigration status, ethnicity and sub-region [3 ]. These dimensions can be divided into those that compare situations in two population subgroups (such as place of residence, such as urban and rural areas) and dimensions that consider situations in more than two population subgroups (such as wealth groups). and subregions) in the case of a dimension with more than two subgroups. It is possible to further differentiate between dimensions with ordered subgroups and non-ordered subgroups. The order dimension has subgroups that are ordered naturally, such as the wealth quintile. that can be ranked from the poorest to the richest

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