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Keywords:

  • health services research;
  • maternity services;
  • emergency obstetric care;
  • millennium development goals;
  • health services accessibility;
  • geographic information systems
  • recherche sur les services de santé;
  • services de maternité;
  • soins obstétricaux d’urgence;
  • Objectifs de Développement du Millénaire;
  • accessibilité aux services de santé;
  • Systèmes d’Information Géographique
  • Investigación en Servicios Sanitarios;
  • servicios de maternidad;
  • cuidados obstétricos de emergencia;
  • objetivos de desarrollo del milenio;
  • accesibilidad a servicios sanitarios;
  • sistemas de información geográfica

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

Several limitations of emergency obstetric care (EmOC) indicators and benchmarks are analysed in this short paper, which synthesises recent research on this topic. A comparison between Sri Lanka and Zambia is used to highlight the inconsistencies and shortcomings in current methods of monitoring EmOC. Recommendations are made to improve the usefulness and accuracy of EmOC indicators and benchmarks in the future.

La plupart des décès maternels sont évitables, pourtant plus de 350.000 femmes meurent chaque année de complications pendant la grossesse et l’accouchement. Le risque pour une mère de mourir de ces causes est de 1/30 en Afrique subsaharienne et l’objectif mondial de réduire le taux de mortalité maternelle de 75% d’ici 2015 – volet de l’Objectif 5 de Développement du Millénaire - est “encore loin”, selon l’ONU. Réduire la mortalité maternelle nécessite de meilleurs soins obstétricaux d’urgence (SOU) qui, à leur tour, dépendent de l’amélioration de la surveillance des services. Nous mettons en évidence les lacunes des indicateurs des SOU et les repères existants, et montrons comment ils peuvent induire en erreur les moyennes nationales. S’appuyant sur une comparaison entre le Sri Lanka et la Zambie, des recommandations sont faites sur la façon d’améliorer les indicateurs et de ventiler les données et tenir compte des variations infranationales.

La mayoría de las muertes maternas son evitables, y sin embargo más de 350,000 mujeres mueren cada año debido a complicaciones durante el embarazo y el parto. El riesgo de que una mujer muera de estas causas es de uno en 30 en África subsahariana, y el objetivo global de reducir la tasa de mortalidad materna en un 75% para el 2015 - parte del Objetivo de Desarrollo del Milenio 5 - está aún muy lejos, según las Naciones Unidas. Reducir las muertes maternas requiere de mejores cuidados de emergencia obstétrica (CEmO), lo cual a su vez depende de una mejor monitorización de los servicios. Hacemos énfasis en la falta de indicadores y referencias, y mostramos como ello puede crear confusión con los promedios nacionales. Basándonos en una comparación entre Sri Lanka y Zambia, se hacen recomendaciones sobre como mejorar los indicadores y desagregar datos y considerar variaciones subnacionales.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

Most maternal deaths are avoidable, yet more than 350 000 women die every year because of complications during pregnancy and childbirth. The risk of a mother dying from these causes is one in 30 in Sub-Saharan Africa, and the global target to reduce the maternal mortality ratio by 75% by 2015 – part of Millennium Development Goal 5 – is ‘still far off’, according to the UN. Reducing maternal deaths requires better emergency obstetric care (EmOC), which, in turn, depends upon better monitoring of services.

In this short paper, we highlight the shortcomings of existing EmOC indicators and benchmarks and how they can lead to misleading national averages. Drawing on a comparison of Sri Lanka and Zambia, recommendations are made on how to improve the indicators and to disaggregate data and consider subnational variations.

The monitoring challenge

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

Emergency obstetric care is complicated and diverse. It refers to many functions, including the administration of injectable antibiotics and caesarean delivery, and can be provided by skilled birth attendants (midwives and doctors) at a well-equipped health centre, or by doctors in a hospital setting. Measuring the effectiveness of such services requires insight and detail, yet current monitoring efforts are too general and inconsistent.

In 1997, UNICEF, WHO & UNFPA included the density of EmOC facilities as an indicator within their guidelines, and this indicator was subsequently adopted and tracked for 27 countries by the global health initiative ‘Countdown to 2015’. The indicator of EmOC availability is spelled out as the number of EmOC facilities both per size of population and per births. Benchmarks (i.e. minimum recommended levels) are given for both – at least five EmOC facilities for every 500 000 people or 20 000 births.

In 2005, the World Health Report (WHR) provided additional EmOC staffing indicators (number of doctors and midwives), yet these were not included in the 2009 handbook update of the UN’s guidelines (WHO, UNFPA, UNICEF & AMDD), thereby leading to confusion and inconsistency as to the most credible and respected indicators. The WHR benchmarks are approximately double the level of those recommended by the UN guidelines because of different assumptions on need and facility sizes.

Limitations of existing indicators

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

There are difficulties associated with the current EmOC indicators. These include the following:

  • Preference for data related to population size rather than the number of births: Of the two types of benchmark for EmOC facility density, the population denominator is used more often than birth data. This is problematic because the crude birth rate (CBR) varies hugely across the globe – from eight births per 1000 population in Japan to 54 births in Niger, meaning that population size does not accurately reflect the obstetric needs of a country or area. For example, a Peruvian Departamento of 500 000 people with a CBR of 20 (leading to 10 000 annual births) would, according to UN guidelines, need either a minimum of five or of 2.5 EmOC facilities, depending on whether population or births were used. Using births as the denominator yields a much better correlation with the maternal mortality ratio (Gabrysch et al. 2012).
  • Different assumptions about the need for EmOC: Assumptions regarding the proportion of births in need of emergency obstetric care and facilities differ between guidelines. These assumptions are crucial for interpreting the benchmarks’ implications for facility size and staffing. For instance, if it is desired that 100% of births should be at facilities capable of providing EmOC, then the level of facilities and staffing required will be nearly seven times greater than if facilities only provide care for complicated births (estimated at 15%).
  • • 
    Lack of specified requirements: The UN guidelines and handbook specify neither the facility capacity (number of births that can be managed) nor the staffing of EmOC facilities. This introduces ambiguity in comparisons of actual numbers of EmOC facilities with the benchmarks. For example, countries may have apparently sufficient numbers of facilities, but these may be too small to serve all women in need. Conversely, in urban areas, a few large facilities may readily meet needs.

Comparing Sri Lanka and Zambia

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

A comparison of EmOC in Sri Lanka and Zambia has exposed the weaknesses of existing monitoring mechanisms (Gabrysch et al. 2011) and has furthered the debate about the most appropriate indicators and benchmarks. For example, despite their differences, both countries performed similarly in terms of EmOC facility density and exceeded the minimum acceptable level represented by the benchmark. The argument is persuasive – the EmOC facility density indicator is problematic as it fails to discriminate between Zambia (high maternal mortality) and Sri Lanka (low maternal mortality). Clearly, interpreting facility density in the absence of facility size is difficult, especially as size varies significantly – from mostly large facilities in Sri Lanka to Zambia’s smaller counterparts. National averages can also be misleading, and subnational data from Zambia reveal highly inequitable distributions of facilities and doctors, particularly disadvantaging rural areas.

Recommendations

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References

The inconsistencies between the UN and the WHR approaches to monitoring maternal health, combined with the limitations of the inexact and inadequate indicators used, are leading to confusion and suboptimal decisions for the provision of EmOC. These challenges could be addressed, in part, by the following actions:

  •  Ensuring EmOC indicators have discriminatory power (i.e. better performance in the indicator should be associated with lower maternal mortality);
  •  Defining required EmOC density per births instead of per population;
  •  Specifying the desired proportion of births in EmOC facilities;
  •  Defining not only required EmOC facility numbers, but also facility size, capacity and staffing; and
  • • 
     Disaggregating data at subnational level to help reveal, and therefore respond, to inequalities in service provision within and between countries.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. The monitoring challenge
  5. Limitations of existing indicators
  6. Comparing Sri Lanka and Zambia
  7. Recommendations
  8. References