Learning Lab Data dive Mobile for Nutrition. mvam for Nutrition Part I revolutionizing collection of nutrition information

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Dennis Quinn
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1 Learning Lab Data dive Mobile for Nutrition mvam for Nutrition Part I revolutionizing collection of nutrition information

Mobile Vulnerability Analysis and Mapping (mvam) PROJECT OVERVIEW Respondents are contacted on their mobile phones Respondents contact WFP through their mobile phones Live calls [Telephone operators]

2 Mobile Vulnerability Analysis and Mapping (mvam) PROJECT OVERVIEW Respondents are contacted on their mobile phones Respondents contact WFP through their mobile phones Live calls [Telephone operators] SMS surveys [Text messages] Mobile surveys IVR [Interactive Voice Response calls] Receive info on WFP [2-way communication system] Data is anonymized and cleaned Record Feedback [2-way communication system] Data is stored in a database and analyzed by a stats engine Results and data are shared as a global public good Reports Databank Humanitarian decision making process Photo: WFP / Lucia Casarin

nutrition data using remote data collection methodologies

3 mvam for Nutrition What is it? Innovation Exploring innovative ways of collecting nutrition data using remote data collection methodologies Partnership The Nutrition Division and mobile Vulnerability Analysis and Mapping unit at WFP

4 Today s outline Why mvam for Nutrition? Kenya feasibility and validation study (with partners ICRAF) Malawi pilot: data collection via SMS (MDD-W survey) Your turn! SMS demo of collecting participants dietary data Visualization of dietary information from Malawi study Closing remarks and wrap-up Questions & Answers

collection and information delivery mvam for market and food security data can it work for nutrition?

5 Why mvam for Nutrition? Convenience Data collection in hard-to-reach and insecure areas Cheaper Cheaper large-scale data collection F2F $16 CATI $5 Faster Real-time data Reduced time between data collection and information delivery mvam for market and food security data can it work for nutrition? Mobile collected information could help to provide early warning of deteriorating nutrition situations support global efforts to strengthen nutrition monitoring Mobile phone access and ownership increasing exponentially around the world Nutrition data gap (GNR 2016) mvam for Nutrition can support fill data gap Mobile data collection methodologies offer a quick and affordable way to collect data remotely

Proxy to measure the nutrient density of young children s diet at the population level MAD: Minimum Dietary Diversity (MDD) + Minimum Meal Frequency (MMF) MDD: Consume at least 4 out of 7 (core) food

6 Indicators: MDD-W & MAD Internationally validated and corporate indicators of WFP What does it measure? Definition Proxy to measure the micronutrient adequacy of WRA at the population level The proportion of WRA who consume at least 5 out of 10 (core) food groups that make up the score. Proxy to measure the nutrient density of young children s diet at the population level MAD: Minimum Dietary Diversity (MDD) + Minimum Meal Frequency (MMF) MDD: Consume at least 4 out of 7 (core) food groups MMF: Depends on how many months and if child is breastfed Calculation 10 food groups 7 food groups (MDD) + 4 frequency questions (MMF) Food groups 1. Grains, white roots and tubers, and plantains 2. Pulses (beans, peas and lentils) 3. Nuts and Seeds 4. Dairy 5. Meat, Poultry and Fish 6. Eggs 7. Dark green leafy vegetables 8. Other Vitamin A-rich fruits and vegetables 9. Other Vegetables 10. Other fruits Indicator Reporting Required Stunting prevention programme Nutrition-sensitive programmes Method Open-based 24-hour recall 1. Grains, roots, tubers 2. Legumes, nuts 3. Dairy products (milk, yoghurt, cheese) 4. Flesh foods (meat, fish, poultry, organ meat) 5. Eggs 6. Vitamin A-rich fruits and vegetables 7. Other fruits and vegetables Highly recommended Micronutrient programmes MAM prevention programmes

7 Kenya Case Study Partner and study locations Kitui and Baringo county A collaborative effort between WFP and ICRAF Feasibility and validity of collecting data on MAD and MDD-W using Computer-Assisted Telephone Interviewing (CATI)

Phase I: Formative Feasibility Study Determine feasibility of using CATI methodology for collecting women and young children dietary data Why?

factors in receiving mobile surveys - Understand cultural contexts and local diet patterns Data collection: - Women s mobile phone usage patters - Local

8 Phase I: Formative Feasibility Study Determine feasibility of using CATI methodology for collecting women and young children dietary data Why? Document understanding of gender distribution of phones as how women use their phones remains largely unknown Objective - Identify constraints and success factors in receiving mobile surveys - Understand cultural contexts and local diet patterns Data collection: - Women s mobile phone usage patters - Local diet of women and young children Method: - 17 Focus group discussions - 16 in-depth surveys - 22 key informant interviews Location: 16 sub-locations in Kitui and Baringo

Phase I: Formative Feasibility Study Determine feasibility of using CATI methodology for collecting women and young children dietary data Results Phone access

surveys) Trust: unknown numbers Gender constraints: husbands approval to participate Phone network coverage: some locations poor Recommendations Formative study

Prior engagement with men/husbands - especially in areas where gender can be a barrier Community consultations to understand optimal times and days to reach

9 Phase I: Formative Feasibility Study Determine feasibility of using CATI methodology for collecting women and young children dietary data Results Phone access and usage Access: high Ownership: high (60-90%) Sharing: inter- & intra household Mode: primarily calling Potential barriers Willingness: strong (phone and diet surveys) Trust: unknown numbers Gender constraints: husbands approval to participate Phone network coverage: some locations poor Recommendations Formative study to inform the design of CATI survey with women. Community sensitization to identify and address potential trust issues. Prior engagement with men/husbands - especially in areas where gender can be a barrier Community consultations to understand optimal times and days to reach respondents. Scheduling times for phone calls in advance - in areas with limited phone network. Account for the need to make multiple phone calls at different times of the day and on different days of the week.

Phase II: Mode Experiment Most rigorous test

Face-to-Face interview Cost per survey F2F =

10 Phase II: Mode Experiment Most rigorous test of this technology ever done Mode experiment measured the accuracy of data collected on MAD and MDD-W using CATI versus traditional Face-to-Face interview Cost per survey F2F = $16 (16 enumerators x 2) CATI = $5 (8 operators)

11 Phase II: Mode Experiment Test/Re-test design Experimental Design: Each indicator survey consists of three main groups. Treatment group 1 and Treatment group 2 randomize data collection mode (CATI and F2F) across the two sampling rounds. Control group 1 is a control for treatment effects, while Control group 2 in MDD-W is used to assess subpopulation bias.

12 Phase II: Mode Experiment Proportion above and below the threshold score by mode CATI N (%) F2F N (%) Differences (CATI F2F) Agreement (%) P-value MDD-W 208 (26.4%) 196 (24.9%) 2% 74.4% 0.44 MDD 225 (38.9%) 122 (21.1%) 18% 67% < MMF 409 (70.8%) 338 (58.5%) 12% 65.5% < MAD 171 (29.6%) 71 (12.3%) 17% 72% < MDD-W via CATI compares well with F2F. Bigger differences are noted with MAD

13 Phase II: Mode Experiment Change in MDD-W and MAD with CATI For trend analysis, CATI can be used as a cost-effective method for collecting both MAD and MDD-W Point estimates for MAD need more research

14 Learning Lab Data dive Mobile for Nutrition mvam for Nutrition Part 2 Malawi study and SMS data collection demo

2017) Indicator: Minimum Dietary Diversity for Women (MDD-W) MDD-W: proxy for micronutrient intake

15 Trial and Error How we collected women's dietary information via SMS in Malawi Method and Design Large-scale (national) feasibility testing Study site: Malawi Mode: SMS 5 rounds (Oct 2016 April 2017) Indicator: Minimum Dietary Diversity for Women (MDD-W) MDD-W: proxy for micronutrient intake of women years of age Near-real time data enabled optimization of methodology throughout rounds

Trial and Error How we collected women's dietary information via SMS in Malawi Lessons learned Using a mix of open-ended and list-based questions to help

to take the survey in their preferred language; Pre-stratifying and pre-targeting to ensure representativeness; Post-calibrating to produce estimates that are

16 Trial and Error How we collected women's dietary information via SMS in Malawi Lessons learned Using a mix of open-ended and list-based questions to help respondents better understand; Keeping questions simple; in some cases splitting questions to make it easier for the respondent to answer; Allowing respondents to take the survey in their preferred language; Pre-stratifying and pre-targeting to ensure representativeness; Post-calibrating to produce estimates that are more comparable to face-to-face surveys. More information on:

17 MDD-W survey via SMS used in Malawi

18 Demo: collecting MDD-W via SMS Now it s your turn to record nutrition information through SMS!

19 Malawi data visualizations Data visualization in Tableau - WFPs corporate data visualization platform Here

mvam for Nutrition Future opportunities Future opportunities Further testing on MAD for point estimates Early warning nutrition indicators for surveillance systems Research on other modes (SMS)

20 mvam for Nutrition Future opportunities Future opportunities Further testing on MAD for point estimates Early warning nutrition indicators for surveillance systems Research on other modes (SMS) Strengthen capacities & technical support F2F training online learning to scale-up methodology Advanced statistical methods to make adjustments for mode effects and sub-population bias. Management and visualisation of data

More information on mvam for Nutrition Visit the WFP mvam blog for more information on the initiative mvam for Nutrition http://mvam.org/2016/08/11/monitor-nutrition/ http://mvam.

21 More information on mvam for Nutrition Visit the WFP mvam blog for more information on the initiative mvam for Nutrition

22 Thank You! World Food Programme

mobile Vulnerability Analysis & Mapping for nutrition: Kenya case study

mobile Vulnerability Analysis & Mapping for nutrition: Kenya case study Remote Mobile Data Collection Mobile Surveys Respondents are contacted on their mobile phones CATI (Computer Assisted Telephone Interviewing)