Smart methodology_Victoria Sauveplane_10.16.13
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Smart methodology_Victoria Sauveplane_10.16.13
- 1. SMART METHODOLOGY Standardizing Measurements of nutrition and mortality information Washington D.C, October 16th, 2013
- 2. 2 Snapshot Question Why are nutrition surveys important?
- 3. Goals of a Survey Evaluate the scope and severity of a humanitarian crisis. Determine the needs of a new program. Evaluate already existing programs. Advocacy.
- 4. When to do a Survey? 4 Surveys should take place when the data collected will answer questions that will influence activities and determine actions.
- 5. 5 Snapshot Question What are the differences between nutritional surveys, nutrition surveillance and rapid assessment?
- 6. Methods for Data Collection 6 Rapid assessment Nutrition Survey Nutrition Surveillance Rapid appraisal Medium-term appraisal Continuous appraisal Qualitative/ Quantitative/ Cross sectional snapshot Cross sectional snapshot Quantitative/ Longitudinal trends Observational / Sample with survey instruments Periodic, standardized data collection Objective Data Type Method Secondary source
- 7. Limitations of Nutrition Surveys 7 Cross-sectional in nature. Provides a snapshot at a given moment of time (not trends). Unable to establish causality. Provides insufficient information for causeeffect analysis. Cross sectional survey data should be used in conjunction with other contextual information
- 8. Summary stages of a survey 8
- 9. 9
- 10. What is SMART? 10 Nutrition Mortality A standardised and simplified field survey methodology which produces a snapshot of the current situation on the ground.
- 11. Context 11 Prior to SMART: Many established survey manuals and guidelines to gather this information, BUT…
- 12. Need for: 12 Standardized methodology - timely and reliable data. Simplified, clear guidance for non-epidemiologists/ statisticians to prioritize humanitarian assistance. Addressing key issues regularly faced by field workers. Emphasis on representative sampling AND information on the quality of the measurements.
- 13. Problem: No Standardisation 13 Data quality in disaster settings poor. Ethiopia 1999-2000: Source: Spiegel P et al, JAMA 2004 Among 67 nutrition and mortality surveys, only 6 (9%) met all 5 eligibility criteria (being valid and precise).
- 14. History 14 The SMART methodology was developed in 2006 by a panel of experts in epidemiology, nutrition, food security, early warning systems and demography. SMART was originally developed to assess acute malnutrition and mortality in emergencies. It is now used in all settings, including development and displaced populations.
- 15. Who uses SMART? 15 Today, SMART is recognised as the standard methodology by national Ministries of Health, donors, implementing partners such as international NGOs and UN agencies that wish to undertake nutrition and mortality surveys in ALL settings (emergency, development, displaced populations). SMART is also incorporated into many national nutrition protocols.
- 16. Surveys using SMART 16 Produce representative, accurate and precise estimates of: Global Acute Malnutrition (GAM). Chronic malnutrition (Stunting). Underweight. Retrospective mortality. These four indicators gathered through the SMART methodology provide the best available validated data that can be used for effective decision making and resource allocation.
- 17. Anthropometric Variables 17 Age. Sex. Height. Weight. Edema. MUAC.
- 18. Group Exercise 18 Practice MUAC measurements. Advantages of MUAC.
- 19. Why is mortality data needed? 19 Benchmark the severity of a crisis. Establish a baseline for comparison (programmatic purposes). Provide an empirical basis for advocacy purposes. Complement a surveillance system. Provides information on the demography of the target population.
- 20. What indicators to include 20 Main indicators: 1.Crude Death Rate. 2.Under-5 Death Rate. Other possible analyses: Age-specific death rate. Sex-specific death rate. (Cause-specific death rate).
- 21. Mortality & Demography Questionnaire 21
- 22. Limitations of Mortality Data 22 Data is retrospective: data on deaths will be late; deaths may have occurred months ago. Mortality is multi-causal: it is difficult to identify what specific programs have reduced mortality rate. 22
- 23. SMART can also evaluate: 23 Additional indicators, including: Anemia; water, sanitation and hygiene (WASH); coverage of blanket programs, immunization, and vitamin A distribution; food security contextual information. The number of additional indicators is recommended to be kept to a minimum to ensure high quality data.
- 24. Key SMART Innovations 1. 2. 3. 4. 5. 6. User-friendly software -ENA Software- that follows every step of a SMART survey. Flexibility in sample size calculation. Regularly updated, clear sampling guidance based on field experiences, research and best practices. Rigorous standardization test and analysis. Plausibility check to follow data quality and identify where the problems are. Improved census procedure for the Mortality component.
- 25. Increased data credibility 25 Rigorous standardisation of field procedures. Data quality checks. Standardised automated data analysis. Consistent and reliable survey data is collected and analysed.
- 26. ACF-CA: SMART Project Convenor 26 ACF-CA, a core member of the GNC, is the SMART Project Convenor, and in collaboration with the SMART Technical Advisory Group and Centers for Disease Control and Prevention (CDC Atlanta) establishes and maintains: Standardisation and translation of tools for survey managers. The SMART www.smartmethodology.org website. On-line technical forum. Partnerships with other agencies in trainings & survey support. The SMART experts at ACF-Canada in collaboration with CDC currently provide training and remote technical support in SMART.
Notas do Editor
- Notes for trainers: There are various definitions of a survey. In the Emergency Nutrition Assessment (2004), Save the Children defines a survey as “a method of gathering information about a large number of people by talking to, or measuring only, a sample of them; a way to collect information on people’s needs, behaviour, attitudes, environment and opinions, as well as on personal characteristics such as age, nutrition status, income and occupation.”
- Notes for trainers: If you are using a survey to evaluate a program impact, you should pay close attention to the confounding factors that might have played a role in the improvement or deterioration of the situation where the intervention took place. A nutritional survey, for example, can partially measure the resulting change in the nutritional status of the population. We can only evaluate partially the efficiency and effectiveness of these interventions, since we must also take into consideration any other factor that could have influenced the nutritional status. The Sphere Minimum Standard requires that an assessment of nutritional status is conducted when a targeted nutrition program is implemented (Sphere Project, 2004), but this does not mean that in an emergency situation, the program is delayed until a survey is completed. Finally, a survey, for example, also serves as an advocacy tool (addressed to governments or other funding agencies, to be able to lobby for support, raise awareness of a situation.)
- Notes for trainers: Are health and nutrition surveys relevant to “rapid assessments” ? Answer: Yes – while there is usually no time to implement an actual survey (primary data collection), rapid assessments will often require reviewing secondary data or survey reports. Furthermore, a rapid assessment will assist in deciding whether or not a more in-depth survey is required as well as the type, objectives and geographical coverage of the survey. Looking at the table: A survey is usually implemented for decision-making in the short to medium term. Given the fact that a survey requires significant planning and resources, it usually requires several weeks for planning and implementation. Quantitative data is usually collected which provides a cross sectional picture of the nutritional status of the population at a single point in time. Data is collected at the household level and individuals within that household. A representative sample is taken using a survey instrument or tool. A survey is more valid than a rapid assessment, especially when the findings are compared with a previous survey.
- Many useful manuals and guidelines, but Many humanitarian agencies suffered from a critical lack of specialized manpower in these areas. Key issues: How to handle children with disabilities; what to do if a household as no children; what if the child was at school or in the clinic.
- Problems in Ethiopia included: 1. Non-probabilistic sampling, 2. Sample Size < 500 children aged 6-59 months, 3. < 25 clusters, 4. Weight for Height not measured/poorly measured as anthropometric index.
- The listed variable are the absolute minimum that are required to determine the prevalence of protein-energy malnutrition. However, these alone will not provide all the potentially useful information. Your rapid assessment, secondary information source, and the objectives of the survey are important to consider when deciding what other information you wish to collect in the survey. This is an example for anthropometry. The same kind of work needs to be done whatever the indicators assessed (e.g., death rates).
- Advocacy: Death rates are the most compelling for advocacy purposes. It is powerful to use for decision makers and policy makers. Benchmark: It is difficult to attribute change in mortality to any one intervention but as a general baseline it is very useful information. Surveillance: Mortality is best measured through surveillance (real time) to track weekly trends. In stable countries we refer to mortality surveillance as “vital statistics” (i.e., counting deaths from death certificates). Surveys are less valuable because they are retrospective. But in some contexts we do not have mortality surveillance (vital statistics). Where we don’t have surveillance, surveys may be the only option for gathering this information. ---- An elevated death rate can indicate that there is a health problem in a population BUT cannot determine its cause. Usually, public health workers at the start of an emergency have to rely on cross-sectional surveys to determine current nutrition status and death rates in the recent past. Ideally, surveys complement a functioning surveillance system to estimate acute malnutrition, verify surveillance data, and answer specific questions that the surveillance system is not providing or about areas that the surveillance system is not covering.
- The crude death rate and the under-five mortality rate are the most frequently used measures used in emergency contexts. Note: It we often do not get enough sample size to have strong confidence in the U5DR. Age specific: U5 or any other age group.
- Multi-causal: could be anything from outbreak of disease to violent death. Cannot well attribute mortality to any one cause. Can’t be used well to measure affect of mortality trends to any specific program. The members of a survey team must be able to answer the community leader’s and the family’s questions, and provide them with necessary explanations. They must understand the reasons for the mortality survey. Identify the most vulnerable groups. A death rate can point out a health problem but not establish a cause-and-effect relationship. Moreover, investigating causes/ location of death poses numerous methodological challenges very difficult to deal with in emergency and transition situations. Thus, SMART does not recommend to collect these variables when it is not objectively necessary.
- Notes to trainers: The basis of SMART methodology relies on many manuals and guidelines, as that of Save the Children Fund: Emergency nutrition Assessment: Guidelines for field workers (2004). Some elements come from organizations such as MSF, FANTA, ACF, WHO, but also epidemiological and statistical norms. With SMART, there is a manual that comes with the software. Simplifies data collection and completion of reports from specific data sets. SMART is added to established and approved methods. Other software exists such as EpiInfo, EpiNut, Anthro, Excel, SPSS, EPI/ENA, etc.