全球食品消费数据标准化处理框架解析_96页_27mb

Summary of "Processing Food Consumption Data from Household Consumption and Expenditure Surveys (HCES)"

Core Content

This document provides guidelines for processing food consumption data collected through Household Consumption and Expenditure Surveys (HCES), in line with the United Nations Statistical Commission-endorsed IAEG-AG 2018 guidelines. It is designed to support national statistical offices (NSOs) and other organizations in preparing consistent, reliable, and high-quality food data for poverty, food security, and nutrition analysis.

The guidelines outline a standardized, step-by-step process to transform raw food data into usable forms, including quantities in grams, monetary values, and dietary energy (kcal). They emphasize the importance of data cleaning, consistency, and transparency to ensure that food data can be used effectively for macroeconomic and socio-economic analyses.

Main Views

1. Purpose of the Guidelines

• To standardize the processing of food data from HCES across countries.
• To ensure data is consistent, transparent, and ready for use in poverty, food security, and nutrition analysis.
• To improve the quality and reliability of statistics derived from food consumption data.

2. Scope of the Guidelines

• Focus on food consumption modules within HCES, which include:
  • In-house consumption
  • Food away from home (FAFH)
• Cover data cleaning, imputation, conversion, and aggregation.
• Include recommendations on nutrient conversion tables (NCTs) and data documentation.

3. Key Steps in the Process

• Step 1: Gathering input and auxiliary data (e.g., food composition tables, price data, non-food data).
• Step 2: Data cleaning (checking for negative values, duplicates, consistency, and validity).
• Step 3: Adjusting and merging data files.
• Step 4: Cleaning at the food item and unit level (detecting and correcting outliers).
• Step 5: Imputing monetary values for missing data.
• Step 6: Converting food quantities into grams.
• Step 7: Editing after conversion to grams.
• Step 8: Calculating dietary energy and macronutrients.
• Step 9: Imputing dietary energy for undefined or missing food items.
• Step 10: Aggregating and macro-editing data.
• Step 11: Preparing and sharing the final dataset.

4. Data Processing Principles

• Transparency and replicability are essential; each step and decision must be well-documented.
• Consistency across surveys is encouraged to facilitate time-series and cross-country comparisons.
• Standardization of data processing methods ensures harmonized results for similar indicators.
• Use of NCTs is recommended to convert food quantities into nutrient values for dietary energy calculations.

Key Information

1. Data Collection Modules

• In-house consumption: Captures food consumed at home, including quantity and source.
• Food away from home (FAFH): Includes food purchased or consumed outside the home, such as meals at restaurants or street vendors.

2. Data Cleaning

• Involves four stages: initial checks, consistency checks, outlier detection, and correction.
• Outlier identification is done using statistical methods such as box plots and median absolute deviation (MAD).
• Imputation is used to replace missing or incorrect data, particularly for monetary values and quantities.

3. Conversion to Grams

• All food quantities must be converted into grams, whether collected in standard units, volume units, or non-standard units (NSUs).
• A conversion factor library is used to assist in this process.
• Quality checks are performed to ensure the accuracy of weight data in grams.

4. Nutrient Conversion Tables (NCTs)

• NCTs are built using food composition tables (FCTs) and databases (FCDBs).
• They allow for the conversion of food quantities into nutrient values (e.g., kilocalories, macronutrients).
• NCTs are essential for dietary energy calculations and nutritional analysis.

5. Data Documentation

• The Data Documentation Initiative (DDI) is recommended for documenting the processing steps.
• All decisions and adjustments made during data cleaning and processing must be recorded for transparency and reproducibility.

6. Implementation and Collaboration

• The guidelines have been tested and implemented by organizations such as SPC, WB, FAO, and COMESA.
• They have been used successfully in Pacific Island countries (e.g., Kiribati, Vanuatu, Marshall Islands, Tonga, Palau, Tuvalu, Samoa) in Household Income and Expenditure Surveys (HIES).
• The UN-CEAG (formerly IAEG-AG) and international experts have contributed to the development of these guidelines.

Structure of the Document

1. Abbreviations

• A list of key acronyms used in the document is provided for clarity.

2. Boxes

• Box 1: Guidelines on food data collection (IAEG-AG 2018).
• Box 2: Practices for an efficient process (joint processing, full processing, consistency, and NCT generation).
• Additional boxes provide examples and explanations on specific aspects of data processing, such as outlier detection, unit conversion, and data documentation.

3. Figures

• Illustrate the flow of food data processing, survey modules, and data conversion steps.

4. Tables

• Provide examples of data entry issues, outlier correction decision matrix, final data file structure, and NCT examples.

Conclusion

These guidelines are intended to support NSOs and other organizations in preparing high-quality, standardized food data from HCES. They ensure efficient and cost-effective data processing, consistency in results, and improved statistical analysis for food security and nutrition studies. The process is modular, adaptable, and transparent, allowing for flexibility in different survey designs while maintaining a common framework for data preparation.