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This makes it difficult to generate reliable trends over time with the input data available, and as such, this report provides only the most recent global and regional estimates for the JME edition. Other notes on Joint Malnutrition Estimates 1. Parkhurst MD Ainsley M. A good understanding of the limitations of the data thus collected in terms of their interpretation, representativeness, accuracy and precision is crucial. Dietary management is not usually subject to professional regulation, although voluntary certification is preferred by many employers. Eastern Europe and Central Asia had the highest overweight prevalence in with Bureau of Labor Statistics.
It is well recognized that malnutrition is associated with adverse clinical outcomes. Although data vary across studies, available evidence shows that early nutrition intervention can reduce complication rates, length of hospital stay, readmission rates, mortality, and cost of care. The key is to systematically identify patients who are malnourished or at risk and to promptly intervene. We present a novel care model to drive improvement, emphasizing the following six principles: Minor differences in style may appear in each publication, but the article is substantially the same in each journal.
Malone are members of the Steering Committee of the Alliance to Advance Patient Nutrition who have been chosen by the professional organizations they represent and reimbursed for Alliance-related expenses.
Although conducted peripherally, they are generally available and used centrally. These sources, which are in principle fairly reliable, benefit from an advanced level of analysis allowing causal inference to be derived of relationships among various household indicators, and with individual indicators, such as nutritional status.
They represent a precious source when establishing a baseline and when analysing causes prior to launching an intervention. These are constructed primarily on the basis of routinely collected data from local government offices, community-based authorities. They are usually passed on as indicators or raw data to the central level, and then sent back to the decentralized levels, with varying degree of regularity, after analysis.
They are often disaggregated by district or locality, but are not always representative, since they often refer only to users of the services under consideration. They are generally grouped together at the central administrations of regions or administrative centres. The indicators relate primarily to activities that lend themselves to regular observation, either because they record activities indicators of operation or delivery of services or because they are necessary for decision-making crop forecasts, unemployment rates or for monitoring purposes market prices of staples, number of cases of diseases, etc.
They do not necessarily include indicators of the causes of the phenomena recorded and are not in principle qualitative indicators. Indicators collected at decentralized levels should meet both the needs of users on these levels and also those of users on the central level for the implementation and monitoring of programmes. If these regularly compiled indicators do not have any real use at the local level and are intended only for the national central level, there is a danger that their quality will drop over time, for lack of sufficient motivation of those responsible for collection and transmission - and gaps are therefore often found in available data sets.
Nevertheless, they are invaluable in giving a clear picture of the situation on the regional or district level, together with medium-term trends. Generally speaking, their limitation is the low level of integration of data from different sectors. A certain number of indicators, particularly those concerning the life of communities or households and not touching on the activities of the various government departments, are not routinely collected by such departments and are in any case not handed on to the regional or central offices.
They are sometimes collected at irregular intervals by local authorities, but most often by non-governmental organizations for specific purposes connected with their spheres of activity - health, hygiene, welfare, agricultural extension, etc.
Analytical capabilities are often lacking at this level, and the available raw data may not have led to the production of useful indicators. Action therefore should be taken to enhance analytical capacities or else sample surveys will have to be carried out periodically on these data in order to produce indicators.
A sound knowledge of local records and their quality is needed to avoid wasting time. New collection procedures often have to be introduced for use by local units, while being careful not to overload them or divert them from their own work. Otherwise a specific collection has to be carried out by surveying village communities targeted for analysis or intervention.
These surveys are vital for a knowledge of the situation and behaviours of individuals and households and an evaluation of their relationship with the policies introduced. In general, they offer an integrated view of the issues concerned. They may have the aim of supplying elements concerning the local situation and local analysis, in order to confirm the consensus of the population and of those in charge as to the situation and interventions to be carried out, and also to allow an evaluation of the impact of such interventions.
The participatory aspect should be emphasized rather than the precision or sophistication of data. An FAO work on participatory projects illustrates issues of evaluation, and especially the choice of indicators in the context of such projects FAO If data already collected are used or if a new survey is carried out for use on a higher level, the size and representativeness of the sample must be checked, and it must be ensured that the data can be linked to a more general set on the basis of common indicators collected under the same conditions method, period, etc.
Verification of the quality of the data is crucial. Before undertaking a specific data collection, a list of indicators and of corresponding raw data should be developed which can be used by services at all levels; it is not unusual to find that surveys could have been avoided by a better knowledge of the data available from different sources.
To track down these useful sources and judge the quality of the data available and their level of aggregation, a good understanding is needed of the goals and procedures of the underlying information system. The country had set up a monthly national information system on production estimates for 35 crops, covering information on crop intentions, areas actually planted, crop yields and quantities harvested in each state.
The information was obtained during monthly meetings of experts at various levels - local, regional and national. The information was then put together at the state level, and then at the national level, reviewed by a national committee of experts, and sent on to the central statistics office.
The different levels thus had some rich information at their disposal, coming from a range of local-level sources. Although it was certainly fairly reliable, being confirmed by a large number of stakeholders and experts, its precision could not be defined, in view of its diversity. The usefulness of such data varies depending on information needs and thus on the quality of the data required.
Data concentrated at the central level are probably useful primarily for analysing trends. On the other hand, apart from the figures, more general information on production systems exists at local level, and this can be useful for identifying relevant indicators of causes, or for simplifying monitoring of the situation.
We have seen that there is a great number of indicators which differ widely in quality; the availability of corresponding data is variable, and any active collection will be subject to constraints.
Therefore the choice of indicators must be restricted to the real needs of decision makers or programme planners. This implies that a method is needed for guiding the choice. The main elements that will guide choice are: Any intervention is based on an analysis of the situation, an understanding of the factors that determine this situation, and the formulation of hypotheses regarding programmes able to improve the situation.
A general framework was presented earlier see Figure , representing a holistic model of causes of malnutrition and mortality, which was endorsed by most international organizations and nutrition planners.
However, the convenient classification that it implies, for instance into levels of immediate, underlying or basic causes needs to be operationalized through further elaboration in context. The benefit of constructing such a framework, over and above the complete review of the chain of events which determine the nutritional situation, is to allow the expression, in measurable terms, of general concepts which, because of their complexity, are not always well defined.
For example, it is not enough to refer to "food security"; one should state which of the existing definitions is to be used, on which dimensions of food security the focus is placed and the corresponding indicators.
The use of conceptual frameworks when implementing programmes or planning food and nutrition is not new. Many examples have been developed, focusing on different aspects. The concept of food security is generally perceived as that of sufficient availability of food for all. However, several dozen different definitions have been proposed over these last 15 years! This concept may, for example, comprise different aspects depending on the level being related to: In the first case, analysis will focus on agricultural production, and in the second the emphasis will be on improving the resources of those who lack access to a correct diet.
This preliminary brainstorming exercise will allow a better definition of the perceived chain of causes production shortfall, excessive market prices, defective marketing infrastructures, low minimum wage, low level of education, etc. It will then be easier to consider potential indicators of the situation and its causes, or potential indicators of programme impact.
Obviously it is not so much the final diagram which is of importance as the process through which it was developed. Insofar as the relations between all the links of the chain of events or flow data, depending on the type of representation have been discussed step by step and argued with supporting facts, the framework will be adapted to the local situation and will become operational.
Methodologies have been developed for making this process effective in the context of planning, for example with the method of "planning by objectives" see ZOPP , which comprises several phases: During this planning process, all programme activities, corresponding partners, necessary inputs and resulting outputs as well as indicators for both monitoring implementation and evaluating impact of the programme will be successively identified.
The method acts as a guide for team work, encouraging intersectoral analysis and offering a simplified picture of the situation, so that the results of discussions are clear to all in the team. Let us again take the example of a problem of food security. It can be broken down into three determining sectors: A series of structural elements can be defined for each sector: These elements affect both production levels and operation of markets.
A certain number of macro-economic or specific policies will affect one or all the elements in this block. Each block can be considered in a similar way, and this will provide the groundwork for a theoretical model of how the system works see C. The final steps in order to operationalize the model are i that of defining indicators that will, in the specific context of the country, reflect the key elements of the system, and ii , once policies and programmes have been chosen, that of identifying which of these indicators are useful for monitoring trends and evaluating programme impact.
This will be the basis for an information system reflecting the overall framework of the programme and how it should work. Another method has been proposed by researchers from the Institute of Tropical Medicine in Antwerp based on their field experience in collaboration with different partners Lefèvre et al. Basically, it stresses the participatory aspect, with the aim of obtaining a true consensus on the local situation, the rationality of interventions in view of the situation, and the choice of indicators.
It includes first a phase in which a causal framework is developed with the aim of providing an understanding of the mechanisms leading to undernutrition in the context under consideration. The framework is constructed in the form of a schematic, hierarchized diagram of causal hypotheses formulated after discussions among all stakeholders.
The way it is built tends to favour a clear, "vertical" visualization of series of causal relationships, eliminating the lateral links or loops that are often the source of confusion in other representations. In a second phase, a framework is developed linking the human or material resources available at the onset inputs , the procedures envisaged activities , the corresponding results of implementation outputs , and the anticipated intermediate outcomes or final impact of each activity or of the programme.
This tool is very useful for defining all the necessary indicators. This represents the formalisation of a real conceptual scheme. While many representations of conceptual models comprise comparable elements, it is essential that a model should never be considered as directly transposable, since it must absolutely apply to the local context. A direct transposition would therefore be totally counter-productive.
While it is obvious that the conceptual analysis must ideally be carried out before the programmes are launched, it can be done or updated at any time, leading to greater coherence and a consensus on current and anticipated actions; this applies even more in a long-term perspective of sustainability.
In operational terms, establishment of a conceptual framework allows to define in a coherent way the various types of indicators to be used at each level. After defining the activities to be undertaken, status indicators referring to the target group will be identified, as well as indicators of causes that will or will not be modified by these activities, and indicators that will reflect the level or quality of the activities performed.
Lastly, indicators will be chosen to reflect the changes obtained, whether or not these are a result of the programme. Identification of precise objectives makes it possible to monitor changes in impact indicators not only vis-à-vis the original situation but also in terms of fulfilment of the objectives adopted.
During this initial phase, existing indicators are assessed, as well as those that will be taken from records or collected through specific surveys. It should be specified who needs this information, as well as who collects the data. In fact, it is important that this choice should be demand-driven, in order to be sure that the information selected is then actually used.
One might be dealing with several groups of users who do not exactly have the same needs: In this way, foundations can be laid for an information system essential for monitoring and evaluation. A proximate, often indirect, indicator will have to be sought and limitations to its validity in the context considered will have to be verified carefully which will depend on the precise objective.
For example, can a measurement of food stocks at a given moment be validly replaced in the context under consideration with a measurement of food consumption in order to assess the food insecurity situation of a target group?
Is a measurement of food diversity a good proximate indicator for micronutrient intake? Does it at least consistently classify consumers into strong and weak consumers?
Does it allow defining an acceptable level of consumption vis-à-vis recommendations? Will it allow children to be classified correctly vis-à-vis a goal of improved growth? Validity studies are sometimes available locally, otherwise specific studies can be carried out; hence the usefulness of collaborating with research groups - for example from universities - who will be able to carry out this type of validation study under good conditions.
The relationship between two variables, making them interchangeable for defining an indicator, may vary over time as a result of implementation of a programme, and this must be taken into account. For example, if there is a clear link between family size and food insecurity in a given context, the criterion of family size can simply be taken as a basis for identifying families at risk.
However, if a specific programme has been successfully carried out among these families, this indicator could lose its validity. The ideal would be to use the same indicators in all places and at all times in order to have the benefit of common experience regarding collection and analysis, so that direct comparisons can be made.
In practice, however, concepts on indicators evolve steadily with the progress of knowledge, leading to the dilemma of being unable to carry out comparisons either with older series of indicators or with what is being done elsewhere. Comparability within time is obviously a priority in the case of monitoring. Preference will thus be given to indicators that, although not necessarily identical, are comparable, in other words give a similar type of information. The issue of the comparability of data from different sources has been the subject of studies especially in the field of health indicators.
Whenever traditional indicators seem inadequate or insufficient in capturing the phenomenon or situation under consideration, the value of "innovative" and potentially promising indicators with excellent basic characteristics should not be neglected - although it is important to make sure that they have been validated for circumstances similar to those under study.
Since such innovative indicators usually have to be collected "actively", especially at the community level, the decision often depends on their technical feasibility as a guarantee of the sustainability of collection. In a context of dietary transition, an indicator expressing the structure of food consumption for example the percent of energy from fat is more subject to major changes than the average consumption level expressed in calories, while also providing important information on the future health of the population considered.
In contrast, data on food habits tend not to change rapidly, unless an education programme is specifically developed for this purpose; the repeated collection of the corresponding indicators is thus of little use for purposes of short- or medium-term monitoring of the situation.
Slowness in collection and in getting the data back to user level are key factors to be considered, for many information systems are paralyzed by this problem, while timely information is often needed for decision-making or for adjusting the programme or the intervention e. From this point of view, the nature of potential sources of data for these indicators or the direct availability of these indicators at the level where they are needed can be decisive for their selection.
In practice, data collected to produce indicators need to be compared to a reference or to a "cut-off value". These can based on an international consensus within the scientific community or the political world, thus avoiding disagreement on interpretation and allowing comparisons between countries and regional extrapolations.
Even so, the information is still sometimes insufficient; moreover, there are no international references for several categories of indicators. In such cases, the value of the same variable at a previous date will be taken as a point of reference. Interpretation of changes in an indicator can be carried out only on the basis of our knowledge of the original situation; knowing a baseline therefore forms part of the information value of a number of indicators.
For instance, was it better or worse before? The only information it supplies as such is the difference from a reference situation in a country without any major problem of undernutrition defined as a prevalence of 2.
The impact of a programme cannot be measured without knowledge of the situation at baseline. The existence of chronological series for an indicator will be considered when choosing among several indicators, because such series allow a rapid interpretation of impact in terms of trends. When previous data are old, an effort is made to assess their present level by projection, as is usually done for major demographic or economic indicators. In a certain number of cases, a preliminary survey is needed in order to establish the present level of various indicators.
Many countries undertook national surveys of their nutritional situation prior to establishing their policies and programmes, so that they could decide on the type or scope of the programme, and could subsequently evaluate the impact. In , more than half of all wasted children lived in South Asia and about one quarter in sub-Saharan Africa, with similar proportions for severely wasted children.
Under-five wasting and severe wasting are highly sensitive to change. Thus, estimates for these indicators are only reported for current levels In almost all countries with available data, stunting rates are higher among boys than girls.
While analyses to determine underlying causes for this phenomenon are underway, an initial review of the literature suggests that the higher risk for preterm birth among boys which is inextricably linked with lower birth weight is a potential reason for this sex-based disparity in stunting.
Analysis is based on a subset of 92 countries with recent data by wealth quintile groupings covering 69 per cent of the global population. Children from the poorest 20 per cent of the population have stunting rates that are double the rate in comparison with the richest quintile.
In South Asia, the absolute disparities between the richest and poorest children in regard to stunting are greater than in any other region. While the overall rates are lower, the relative disparities are greatest in Latin America and the Caribbean where the rate among the poorest is more than 4 times higher than among the richest. An analysis of 54 countries with comparable trend data between around and around shows that gaps between the poorest 20 per cent and richest 20 per cent of children under five have closed by at least 20 per cent in the majority of upper-middle-income countries.
However, in almost all low income countries, this gap has either remained the same or increased. Blencowe H et al. Preterm birth—associated neurodevelopmental impairment estimates at regional and global levels for Pediatric Research Volume No s1, December Please note that some children can suffer from more than one form of malnutrition — such as stunting and overweight or stunting and wasting.
There are currently no joint global or regional estimates for these combined conditions, but UNICEF has a country-level dataset with country level estimates, where re-analysis was possible. Prevalence of stunting, wasting and overweight among children under 5 is estimated by comparing actual measurements to an international standard reference population. The new standards are the result of an intensive study project involving more than 8, children from Brazil, Ghana, India, Norway, Oman and the United States.
Overcoming the technical and biological drawbacks of the old reference population, the new standards confirm that children born anywhere in the world and given the optimum start in life have the potential to reach the same range of height and weight.
The new standards should be used in future assessments of child nutritional status. It should be noted that because of the differences between the old reference population and the new standards, prevalence estimates of child anthropometry indicators based on these two references are not readily comparable.
It is essential that all estimates are based on the same reference population preferably the new standards when conducting trend analyses. Before conducting trend analyses of child nutritional status, it is important to ensure that estimates from various data sources are comparable over time.
For example, household surveys in some countries in the early s only collected child anthropometry information among children up to 47 months of age — or even up to only 35 months of age. Prevalence estimates based on such data only referred to children under 4 or under 3 years of age and are not comparable to prevalence estimates based on data collected from children up to 59 months of age.
Some age adjustment needs to be applied to make these estimates based on non-standard age groups comparable to those based on the standard age range. For more information about age adjustment, please click here to read a technical note. In addition, prevalence estimates need to be calculated according to the same reference population.
For more information about the difference between the two references and its implications, please click here to read a series of questions and answers. When data collection begins in one calendar year and continues into the next, the survey year assigned is the one in which most of the fieldwork took place. For example, if a survey was conducted between 1 September and 28 February , the year would be assigned, since the majority of data collection took place in that year i. This method has been used since the edition prior to that, the latter year was used by default — e.
As of the edition, the country-level dataset used to generate the global and regional joint malnutrition estimates is based only on final survey results. Preliminary survey results are no longer included in the dataset since the data are sometimes retracted or change significantly when the final version is released.
Country-level progress in reducing undernutrition prevalence is evaluated by calculating the average annual rate of reduction AARR and comparing this to the AARR needed in order to achieve targets.
Estimation of regional and global trends is based on a multilevel modelling method see de Onis et al. For the most recent trend analysis, a total of data points from countries over the period to were included in the model.