Estimating health adjusted age at death (HAAD)

Objectives: At any point in time, a person’s lifetime health is the number of healthy life years they are expected to experience during their lifetime. In this article we propose an equity-relevant health metric, Health Adjusted Age at Death (HAAD), that facilitates comparison of lifetime health for individuals at the onset of different medical conditions, and allows for the assessment of which patient groups are worse off. A method for estimating HAAD is presented, and we use this method to rank four conditions in six countries according to several criteria of “worse off” as a proof of concept. Methods: For individuals with specific conditions HAAD consists of two components: past health (before disease onset) and future expected health (after disease onset). Four conditions (acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), schizophrenia, and epilepsy) are analysed in six countries (Ethiopia, Haiti, China, Mexico, United States and Japan). Data from 2017 for all countries and for all diseases were obtained from the Global Burden of Disease Study database. In order to assess who are the worse off, we focus on four measures: the proportion of affected individuals who are expected to have HAAD<20 (T20), the 25th and 75th percentiles of HAAD for affected individuals (Q1 and Q3, respectively), and the average HAAD (aHAAD) across all affected individuals. Results: Even in settings where aHAAD is similar for two conditions, other measures may vary. One example is AML (aHAAD = 59.3, T20 = 2.0%, Q3-Q1 = 14.8) and ALL (58.4, T20 = 4.6%, Q3-Q1 = 21.8) in the US. Many illnesses, such as epilepsy, are associated with more lifetime health in high-income settings (Q1 in Japan = 59.2) than in low-income settings (Q1 in Ethiopia = 26.3). Conclusion: Using HAAD we may estimate the distribution of lifetime health of all individuals in a population, and this distribution can be incorporated as an equity consideration in setting priorities for health interventions.publishedVersio

Understanding Inequalities in Child Health in Ethiopia: Health Achievements Are Improving in the Period 2000– 2011

Objective In Ethiopia, coverage of key health services is low, and community based services have been implemented to improve access to key services. This study aims to describe and assess the level and the distribution of health outcomes and coverage for key services in Ethiopia, and their association with socioeconomic and geographic determinants. Methods Data were obtained from the 2000, 2005 and 2011 Ethiopian Demographic and Health Surveys. As indicators of access to health care, the following variables were included: Under-five and neonatal deaths, skilled birth attendance, coverage of vaccinations, oral rehydration therapy for diarrhoea, and antibiotics for suspected pneumonia. For each of the indicators in 2011, inequality was described by estimating their concentration index and a geographic Gini index. For further assessment of the inequalities, the concentration indices were decomposed. An index of health achievement, integrating mean coverage and the distribution of coverage, was estimated. Changes from 2000 to 2011 in coverage, inequality and health achievement were assessed. Results Significant pro-rich inequalities were found for all indicators except treatment for suspected pneumonia in 2011. The geographic Gini index showed significant regional inequality for most indicators. The decomposition of the 2011 concentration indices revealed that the factor contributing the most to the observed inequalities was different levels of wealth. The mean of all indicators improved from 2000 to 2011, and the health achievement index improved for most indicators. The socioeconomic inequalities seem to increase from 2000 to 2011 for under-five and neonatal deaths, whereas they are stable or decreasing for the other indicators. Conclusion There is an unequal socioeconomic and geographic distribution of health and access to key services in Ethiopia. Although the health achievement indices improved for most indicators from 2000 to 2011, socioeconomic determinants need to be addressed in order to achieve better and more fairly distributed health

Concentration curves and geographic Lorenz curves for under-five deaths and skilled birth attendance in 2011.

<p>The two figures to the left are concentration curves, with the cumulative proportion of the individuals ranked by wealth on the x-axis and the cumulative proportion of the outcome variable on the y-axis. The two figures to the right are geographic Lorenz curves, with the cumulative proportion of the regions ranked from worst to best achievement of the indicator, and weighed by population size on the x-axis. The cumulative proportion of the outcome variable is on the y-axis. The solid red lines represent the concentration and Lorenz curves, and the dashed blue lines represent the “line of equality”.</p

Changes in average level (given as percentage) and health achievement index from 2000 to 2011.

<p>Change from 2000 to 2011 in average level and health achievement index for each of the indicators. The time is on the x-axis, and the health achievement index and average level or coverage are on the y-axis.</p

Factors contributing to socioeconomic inequality.

<p>Contribution of wealth, the mother's education and region of residence to the total socioeconomic inequality, as measured by the concentration index, for each of the indicators in 2011.</p

Summary statistics of the indicators (2011).

<p>*Estimates from the Ethiopia DHS 2011 Final Report <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106460#pone.0106460-Central2" target="_blank">[26]</a>. The mortality rates are reported as number of deaths per thousand live births.</p><p>**The figure is based on fewer than 25 cases and has been supressed.</p><p>Summary statistics of the indicators (2011).</p

Absolute and percentage contribution of mother's education. wealth and region of residence to the concentration indices (C): results of the decomposition analysis.

<p>*The contribution of the region Harari to the inequality in use of antibiotics was omitted in the regression analysis because ALL = 0.</p><p>Absolute and percentage contribution of mother's education. wealth and region of residence to the concentration indices (C): results of the decomposition analysis.</p

Estimating health adjusted age at death (HAAD)

Objectives: At any point in time, a person’s lifetime health is the number of healthy life years they are expected to experience during their lifetime. In this article we propose an equity-relevant health metric, Health Adjusted Age at Death (HAAD), that facilitates comparison of lifetime health for individuals at the onset of different medical conditions, and allows for the assessment of which patient groups are worse off. A method for estimating HAAD is presented, and we use this method to rank four conditions in six countries according to several criteria of “worse off” as a proof of concept. Methods: For individuals with specific conditions HAAD consists of two components: past health (before disease onset) and future expected health (after disease onset). Four conditions (acute myeloid leukemia (AML), acute lymphoid leukemia (ALL), schizophrenia, and epilepsy) are analysed in six countries (Ethiopia, Haiti, China, Mexico, United States and Japan). Data from 2017 for all countries and for all diseases were obtained from the Global Burden of Disease Study database. In order to assess who are the worse off, we focus on four measures: the proportion of affected individuals who are expected to have HAAD<20 (T20), the 25th and 75th percentiles of HAAD for affected individuals (Q1 and Q3, respectively), and the average HAAD (aHAAD) across all affected individuals. Results: Even in settings where aHAAD is similar for two conditions, other measures may vary. One example is AML (aHAAD = 59.3, T20 = 2.0%, Q3-Q1 = 14.8) and ALL (58.4, T20 = 4.6%, Q3-Q1 = 21.8) in the US. Many illnesses, such as epilepsy, are associated with more lifetime health in high-income settings (Q1 in Japan = 59.2) than in low-income settings (Q1 in Ethiopia = 26.3). Conclusion: Using HAAD we may estimate the distribution of lifetime health of all individuals in a population, and this distribution can be incorporated as an equity consideration in setting priorities for health interventions