mVAM for Nutrition Part I revolutionizing collection of nutrition - PowerPoint PPT Presentation

Learning Lab Data dive – Mobile for Nutrition mVAM for Nutrition Part I revolutionizing collection of nutrition information

Mobile Vulnerability Analysis and Mapping (mVAM) PR PROJ OJEC ECT T OVER ERVIE VIEW Respondents are contacted Respondents contact WFP on their mobile phones through their mobile phones Live calls [Telephone operators] SMS surveys [Text messages] IVR [Interactive Voice Response calls] Mobile Receive info on WFP surveys [ 2-way communication system] Record Feedback Data is anonymized and cleaned [ 2-way communication system] Data is stored in a database and analyzed by a ‘stats engine ’ Results and data are Reports shared as a global public good Databank Humanitarian decision making process Photo: WFP / Lucia Casarin

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

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

Why mVAM for Nutrition? Faster Cheaper Real-time data Convenience Cheaper large-scale Data collection in data collection Reduced time between hard-to-reach and data collection and insecure areas F2F $16 information delivery CATI $5  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

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

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

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 Recommendations Formative study to inform the design of CATI Phone access and usage survey with women. Access : high Ownership : high (60-90%) Community sensitization to identify and Sharing : inter- & intra household address potential trust issues. Mode : primarily calling Prior engagement with men/husbands - Potential barriers especially in areas where gender can be a barrier Willingness : strong (phone and diet surveys) Community consultations to understand Trust : unknown numbers optimal times and days to reach Gender constraints : respondents. husbands approval to participate Scheduling times for phone calls in advance - in areas with limited phone network. Phone network coverage : some locations poor 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 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)

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.

Phase II: Mode Experiment Proportion above and below the threshold score by mode CATI F2F Differences Agreement P-value N (%) N (%) (CATI – F2F) (%) MDD-W 208 (26.4%) 196 (24.9%) 2% 74.4% 0.44 MDD 225 (38.9%) 122 (21.1%) 18% 67% < 0.0001 MMF 409 (70.8%) 338 (58.5%) 12% 65.5% < 0.0001 MAD 171 (29.6%) 71 (12.3%) 17% 72% < 0.0001 MDD-W via CATI compares well with F2F. Bigger differences are noted with MAD

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

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

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 15-49 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 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: http://mvam.org/2017/06/06/trial-and-error- how-we-found-a-way-to-monitor-nutrition- through-sms-in-malawi/

MDD-W survey via SMS used in Malawi

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

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) • 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.org/2017/01/09/can-we-reach-rural-women-via-mobile-phone-kenya-case-study/  http://mvam.org/2017/05/10/mvam-for-nutrition-findings-from-kenya/  http://mvam.org/2017/06/06/trial-and-error-how-we-found-a-way-to-monitor-nutrition-through-sms-in- malawi/

Thank You! World Food Programme World Food Programme