Abstract

Objective

To construct a territorial measure and classification of child and maternal health in the countries of the Horn of Africa based on the 2030 Agenda for Sustainable Development adopted by all United Nations Member States in 2015.

Method

The design of our index includes the variables child and maternal health defined in the Sustainable Development Goals (SDGs) to enable territorial ranking of the countries. For this purpose, we used Pena's distance method for 2017.

Results

The results indicate a relatively high territorial disparity in maternal health between the countries of the Horn of Africa according to the differing values of the SDGs variables of child and maternal health.

Conclusions

We propose a territorial classification in the countries of the Horn of Africa. We believe that the most striking differences between countries relate to basic variables of maternal health such as being attended by skilled health personnel.

Keywords:
Africa; Child health; Health status disparities; Maternal health; Human rights; Sustainable development

Resumen

Objetivo

Elaborar una medida y clasificación territorial de la salud infantil y materna en los países del Cuerno de África, basada en la Agenda 2030 para el Desarrollo Sostenible, que fue adoptada por todos los Estados miembros de las Naciones Unidas en 2015.

Método

El diseño del índice incluye variables de salud infantil y materna definidas en los Objetivos de Desarrollo Sostenible (ODS), para permitir la clasificación territorial de los países. Para este propósito, utilizamos el método de distancia de Pena para 2017.

Resultados

Los resultados revelan una disparidad territorial relativamente alta en salud materna entre los países del Cuerno de África, de acuerdo con los diferentes valores de las variables ODS.

Conclusiones

Proponemos una clasificación territorial en el Cuerno de África. Consideramos que las mayores diferencias entre los países se relacionan con variables básicas de salud materna, como la asistencia de personal de salud cualificado.

Palabras clave:
África; Salud infantil; Disparidades en el estado de la salud; Salud materna; Derechos humanos; Desarrollo sostenible

Introduction

On 1^st January 2016, the world officially began implementation of the action plan based on Sustainable Development Goals (SDGs). Goal three aims to ensure health and well-being for all people of all ages by improving reproductive, maternal and child health.¹1. United Nations. The Sustainable Development Goals Report 2016. New York: Oxford University Press; 2016. 119 p.

Study of the Horn of Africa countries is especially important, as the situation remains disastrous.²2. Rodríguez JA, Moreno D, Sánchez J. An index of education and child health in the Horn of Africa. Qual Quant. 2014;48:863-70. The Horn of Africa region is plagued by a set of complex,³3. Crawleya H, Blitz B. Common agenda or Europe's agenda? International protection, human rights and migration from the Horn of Africa. J Ethn Migr Stud. 2018;45:2258-74. often interrelated factors including environmental degradation, climate-related disasters such as droughts and floods.⁴4. UNHCR, World Bank. Forced displacement and mixed migration in the Horn of Africa, Eastern Africa. Geneva and Washington: The UNHCR and The World Bank Group; 2015. 110 p.^-55. United Nations Development Programme. MDG Report 2015: assessing progress in Africa toward the Millennium Development Goals. Addis Ababa: United Nations Economic Commission for Africa; 2015. 112 p.

Multiple factors hinder access to and utilization of health services in the Horn of Africa. These factors include lack of a functional health system, geographical accessibility, financial barriers and limited availability of services.¹1. United Nations. The Sustainable Development Goals Report 2016. New York: Oxford University Press; 2016. 119 p.^-22. Rodríguez JA, Moreno D, Sánchez J. An index of education and child health in the Horn of Africa. Qual Quant. 2014;48:863-70.

In particular, this study aims to construct a synthetic indicator of maternal and child health to enable comparison between five countries in 2017 in the Horn of Africa.

The index also allows to study the impact of each variable individually so as to determine disparities in the variables associated with the SDGs for each country. Additionally, the research explores the relative impact of each variable by using the correction factor.

Method

The methodological approaches used in this study are based on the construction of a synthetic index that follows Pena's method (DP₂).⁶6. Pena B. Problemas de la medición del bienestar y conceptos afines: una aplicación al caso español. Madrid: Instituto Nacional de Estadística; 1977. p. 218. The DP₂ provides an ideal solution to the problems involved in devising a synthetic indicator, particularly those related to aggregation and weighting of simple indicators and information duplicity.⁷7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200.

The DP₂ measures the distance between the issue studied in each country and a fictitious base reference. We take as reference a theoretical country that obtains the worst values for the variables studied⁷7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200..

The DP₂ from country j is defined as follows:

where d_i = |x_ji - x_*i | is the distance between the value of variable i in country j and the reference base. The reference base comprises the results from an imaginary country which reflects the worst possible scenario for all the variables where X_*= (x_*1, x_*2,..., x_*n) coincides with the minimum vector. The reference base would therefore be attributed a value of zero in the synthetic indicator.⁷7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200. n is the number of variables, (_i is the standard deviation of variable i, and (1-R²_i, _i-1,...,₁) is a “correction factor”⁶6. Pena B. Problemas de la medición del bienestar y conceptos afines: una aplicación al caso español. Madrid: Instituto Nacional de Estadística; 1977. p. 218. that avoids redundancy.²2. Rodríguez JA, Moreno D, Sánchez J. An index of education and child health in the Horn of Africa. Qual Quant. 2014;48:863-70.^,66. Pena B. Problemas de la medición del bienestar y conceptos afines: una aplicación al caso español. Madrid: Instituto Nacional de Estadística; 1977. p. 218.

7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200.

8. Rodríguez JA, Salinas JA. An index of maternal and child health in the least developed countries of Asia. Gac Sanit. 2012;26:190-2.^-99. Somarriba N, Zarzosa P. Quality of life in Latin America: a proposal for a synthetic indicator. In: Tonon G, editor. Indicators of quality of life in Latin America. Cham: Springer International Publishing; 2016. p. 19-56. The coefficient of determination, R²_i, _i−1,...,1, is the determination coefficient in regression X_i over X_i-1, X_i-2,...,X₁, which is already included, with R²₁=0. Put differently, the coefficient measures the part of the variance of each variable explained by the linear regression estimated using the preceding variables.²2. Rodríguez JA, Moreno D, Sánchez J. An index of education and child health in the Horn of Africa. Qual Quant. 2014;48:863-70. The ordering of the variables corresponds to their relative weight measured in terms of linear correlation with the final synthetic indicator.⁷7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200. The input order of the variables is determined by an algorithm that reaches convergence when the indicator fulfils a number of desirable properties.²2. Rodríguez JA, Moreno D, Sánchez J. An index of education and child health in the Horn of Africa. Qual Quant. 2014;48:863-70.^,66. Pena B. Problemas de la medición del bienestar y conceptos afines: una aplicación al caso español. Madrid: Instituto Nacional de Estadística; 1977. p. 218.

7. Somarriba N, Zarzosa P. Quality of life in the European Union: an econometric analysis from a gender perspective. Soc Indic Res. 2018;42:179-200.

8. Rodríguez JA, Salinas JA. An index of maternal and child health in the least developed countries of Asia. Gac Sanit. 2012;26:190-2.^-99. Somarriba N, Zarzosa P. Quality of life in Latin America: a proposal for a synthetic indicator. In: Tonon G, editor. Indicators of quality of life in Latin America. Cham: Springer International Publishing; 2016. p. 19-56.

It is also possible to establish an order or hierarchy based on the amount of information that each variable contributes to the DP₂. To determine this, we construct the Ivanovic Discrimination Coefficient (IDC).¹⁰10. Pena JB. La medición del bienestar social: una revisión crítica. Est Econ Aplic. 2009;2:299-324.

11. Ivanovic B. Comment établir une liste des indicateurs de développement. Rev Stat Appli. 1974;2:37-50.^-1212. Zarzosa P. Aproximación a la medición del bienestar social. Valladolid: Secretariado de Publicaciones; 1996. p. 248. This indicator finally shows us the amount of information provided by the i-th variable. It can range from 0 (in the event that the values of Xi are identical in all the countries) to 2 (in the event that the variable has total discriminatory power, that is, when the amount of information varies greatly across territories). Thus, the closer the IDC value is to 2, the more useful information it provides to explain the differences in the level of child and maternal health in the countries studied.¹¹11. Ivanovic B. Comment établir une liste des indicateurs de développement. Rev Stat Appli. 1974;2:37-50.^-1212. Zarzosa P. Aproximación a la medición del bienestar social. Valladolid: Secretariado de Publicaciones; 1996. p. 248.

Data were collected from the work of the United Nations Statistical Commission, which created the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs). In particular, we used five variables of child and maternal health associated with the goal 3¹1. United Nations. The Sustainable Development Goals Report 2016. New York: Oxford University Press; 2016. 119 p. (Table 1), using as a reference the detailed information contained in a set of variables set out under the SDGs in the Report 2018,¹³13. United Nations. The Sustainable Development Goals Report 2018. New York: Oxford University Press; 2018. 40 p. which provide a more extensive and more reliable set of statistics on the SDG 3 of Horn of Africa. The countries included into the analysis were Ethiopia, Kenya, Somalia, Eritrea and Djibouti. To guarantee fulfilment of the properties of the synthetic indicator, we multiply specific variables whose increase implies a worsening of the child and maternal health by −1.

The year of analysis is 2017, but for those variables where information was not available for that date, the nearest year was taken as an alternative. This has occurred in the variable “Attended by skilled health personnel, percentage”, whose available information is from 2016.

Results

Constructed from the variables included in Table 1, the result is shown in Table 2, which ranks the five countries by level of child and maternal health.

The resulting classification (Table 2) shows, first, a distance of almost 5 points between the best-positioned country (Djibouti) and the worst-positioned (Somalia) in 2017. These results indicate a relatively high disparity between the countries analysed.

The results show that Djibouti made the greatest progress toward the goals for child and maternal health, with a distance of 4.56 from the baseline (Table 2). It was followed by Kenya (3.46), which accounts for 29% of the total population of the Horn of Africa.

Taken together, Somalia and Ethiopia account for nearly 70% of the population of the Horn of Africa. They, in contrast, are the countries with the worst theoretical scenarios (Table 2).

If we analyse the results obtained for the variables with the greatest inequality in intercountry values (IDC),¹⁰10. Pena JB. La medición del bienestar social: una revisión crítica. Est Econ Aplic. 2009;2:299-324. the most discriminating variable is “Attended by skilled health personnel, percentage” (Table 1). The second-most-discriminating variable is “Maternal mortality ratio per 100,000 live births”.

In addition, by means of correction factors, the synthetic indicator DP₂ only includes the new information from each variable.⁹9. Somarriba N, Zarzosa P. Quality of life in Latin America: a proposal for a synthetic indicator. In: Tonon G, editor. Indicators of quality of life in Latin America. Cham: Springer International Publishing; 2016. p. 19-56. In particular, the variable “Maternal mortality ratio per 100,000 live births” contains all of its information, so the corresponding correction factor is 100%, as a result of being most closely correlated with it (Table 1).

Discussion and conclusion

The DP₂ method shows territorial disparities in child and maternal health in the Horn of Africa in 2017. We obtained a difference of 4.56 units between Djibouti and the reference value. Djibouti achieved a higher level of child and maternal health, but it accounts for only 0.5% of the total Horn of Africa population. At the opposite extreme, Somalia registers extremely low values in the set of partial indicators.

Priority must be given to interventions to address the variables that have greater power to explain the differences in the values between countries relative-primarily the variable “Attended by skilled health personnel, percentage”.

The differing values of these variables suggest that progress in maternal health is uneven throughout the Horn of Africa, while fewer territorial differences exist in the variables associated with child health as defined in the SDGs.

In summary, delivery of health services is greatly in need of improvement, especially in Somalia and Ethiopia, and there is an urgent need to increase the number of health workers throughout the region to lower maternal and infant mortality.¹⁴14. UNICEF/WHO. Tracking progress towards universal coverage for women's, children's and adolescents' health. The 2017 Report. Geneva and New York: WHO/UNICEF; 2018. 268 p.

In general, the DP₂ classification for these countries differs from that made by the Human Development Index (HDI) for countries with low human development in 2017 (Table 2). In this sense, our analysis takes into account a range of SDGs variables, some of which are not included in the HDI.

References

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