In May 2006, the Ministers of Health of all African countries, at a special session of the African Union, undertook to institutionalise efficiency monitoring within their respective national health information management systems. The specific objectives of this study were: (i) to assess the technical efficiency of National Health Systems (NHSs) of African countries for measuring male and female life expectancies, and (ii) to assess changes in health productivity over time with a view to analysing changes in efficiency and changes in technology. The analysis was based on a five-year panel data (1999-2003) from all 53 countries. Data Envelopment Analysis (DEA) − a non-parametric linear programming approach − was employed to assess the technical efficiency. Malmquist Total Factor Productivity (MTFP) was used to analyse efficiency and productivity change over time among the 53 countries' national health systems. The data consisted of two outputs (male and female life expectancies) and two inputs (per capital total health expenditure and adult literacy). All the 53 countries' national health systems registered improvements in total factor productivity, attributable mainly to technical progress. Over half of the countries' national health systems had a pure efficiency index of less than one, signifying that those countries' NHSs pure efficiency contributed negatively to productivity change.
Monitoring equity and research policy
This report begins by identifying six problems which make developing the evidence base on the social determinants of health potentially difficult. In order to overcome these difficulties a number of principles are described which help move the measurement of the social determinants forward. The report proceeds by describing in detail what the evidence based approach entails including reference to equity proofing. The implications of methodological diversity are also explored. A framework for developing, implementing, monitoring and evaluating policy is outlined. At the centre of the framework is the policy-making process which is described beginning with a consideration of the challenges of policies relating to the social determinants.
Most people in Africa and Asia are born and die without leaving a trace in any legal record or official statistic. Absence of reliable data for births, deaths, and causes of death are at the root of this scandal of invisibility, which renders most of the world's poor as unseen, uncountable, and hence uncounted. This situation has arisen because, in some countries, civil registration systems that log crucial statistics have stagnated over the past 30 years. Sound recording of vital statistics and cause of death data are public goods that enable progress towards Millennium Development Goals and other development objectives that need to be measured, not only modelled. Vital statistics are most effectively generated by comprehensive civil registration. Now is the time to make the long-term goal of comprehensive civil registration in developing countries the expectation rather than the exception. The international health community can assist by sharing information and methods to ensure both the quality of vital statistics and cause of death data, and the appropriate use of complementary and interim registration systems and sources of such data.
Nosocomial transmission of XDR strains seems to have contributed to a major outbreak in HIV-positive individuals in Tugela Ferry, South Africa. To better understand how to control XDR tuberculosis, this issue of the Lancet presents a report of a new mathematical model, developed by Sanjay Basu and colleagues, of the transmission of tuberculosis in this region. Their model builds on previous tuberculosis models, and was corroborated by independently collected epidemiological data for the area. Such mathematical models of tuberculosis can be useful instruments for policymaking because they incorporate a representation of the natural history and transmission of infection and disease, and are the only way to rigorously explore the effects of policies before they are field-tested.
Notions of equity are fundamental to, and drive much of the current thinking about global health. Health inequity, however, is usually measured using health inequality as a proxy - implicitly conflating equity and equality. Unfortunately measures of global health inequality do not take account of the health inequity associated with the additional, and unfair, encumbrances that poor health status confers on economically deprived populations. Using global health data from the World Health Organization's 14 mortality sub-regions, a measure of global health inequality (based on a decomposition of the Pietra Ratio) is contrasted with a new measure of global health inequity. The inequity measure weights the inequality data by regional economic capacity (GNP per capita). The least healthy global sub-region is shown to be around four times worse off under a health inequity analysis than would be revealed under a straight health inequality analysis. In contrast the healthiest sub-region is shown to be about four times better off. The inequity of poor health experienced by poorer regions around the world is significantly worse than a simple analysis of health inequality reveals.
This article covers the opening address of the Tanzanian Minister of Health and Social Welfare at the 22nd Annual Joint Scientific Conference of the National Institute for Medical Research, Arusha Tanzania, 7 March 2007.
As reducing socio-economic inequalities in health is an important public health objective, monitoring of these inequalities is an important public health task. The specific inequality measure used can influence the conclusions drawn, and there is no consensus on which measure is most meaningful. The key issue raising most debate is whether to use relative or absolute inequality measures. Our paper aims to inform this debate and develop recommendations for monitoring health inequalities on the basis of empirical analyses for a broad range of developing countries.
WHO publishes the first internationally agreed upon classification code for assessing the health of children and youth in the context of their stages of development and the environments in which they live. The International Classification of Functioning, Disability and Health for Children and Youth (ICF–CY) confirms the importance of precise descriptions of children's health status through a methodology that has long been standard for adults. Viewing children and youth within the context of their environment and development continuum, the ICF–CY applies classification codes to hundreds of bodily functions and structures, activities and participation, and various environmental factors that restrict or allow young people to function in an array of every day activities.
Measurement of individuals' costs and outcomes in randomised trials allow uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomising clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. The authors used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1) joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2) joint modeling of costs and effects with Bayesian hierarchical models and 3) linear regression of net benefits at different willingness to pay levels using a) least squares regression with Huber-White robust adjustment of errors, b) a least squares hierarchical model and c) a Bayesian hierarchical model. All five methods produced similar results, with greater uncertainty than if cluster randomisation was not accounted for. Cost effectiveness analyses alongside cluster randomised trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software.
This issue of the Lancet publishes two papers of critical interest to child survival. Unfortunately, both have stirred concerns about misuse of data by UN agencies. Here, they review the allegations and try to draw lessons about the place of independent scientific inquiry in the arena of global health policymaking. Greg Fegan and colleagues report the success of an expanded insecticide-treated bednet programme in Kenya . The full paper reveals the strengths and limitations of the study, and provides important estimates of uncertainty. No such statistical caution was expressed in the WHO statement about these data, released on Aug 16. Indeed, WHO claimed that this finding "ends the debate about how to deliver long-lasting insecticidal nets". Yet communications between the Kenyan research team and WHO suggest an ill-considered rush by WHO against the advice of wiser scientific minds.