mobile Vulnerability Analysis & Mapping for nutrition: Kenya case study

Transcription

1 mobile Vulnerability Analysis & Mapping for nutrition: Kenya case study

2 Remote Mobile Data Collection Mobile Surveys Respondents are contacted on their mobile phones CATI (Computer Assisted Telephone Interviewing) SMS surveys (text messages) IVR (Interactive Voice Response) Online Surveys Data is anonymized and cleaned 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

3 Remote Mobile Data Collection Advantages: Cost Speed Flexibility Security Challenges: Biases Data Volume Questionnaire Length

4 EXPERIMENTAL DESIGN Can mobile data collection methodologies such as computer assisted telephone interviewing (CATI) be used to collect useful information on nutrition indicators? PHASE I: Formative Study FGDs, KIIs, and IDIs Women s use of mobiles Willingness to participate in a mobile survey Local diet PHASE II: Quantitative Study Test/Retest Design comparing CATI & MDDW & MAD PPI Prevalence, Score, Sampling bias, Cost, Success rate

5 FORMATIVE STUDY: Key results Access Ownership Sharing Literacy Network Charging Trust Willingness Gender

6 QUANTITATIVE STUDY: Test/Retest Method Indicator Group Round I Round II Treatment Group 1 CATI Treatment Group 2 CATI MDD-W Control Group 1 Control Group 2 (women without mobile phones) Treatment Group 1 CATI Treatment Group 2 CATI MAD Control Group 2

7 QUANTITATIVE STUDY: Analysis Does mode affect estimated population prevalence of adequate nutrition? Does mode affect individual dietary diversity or meal frequency measures? Does mode affect survey questions (food groups) differently? Does excluding people without mobile phones bias nutrition estimates? Implications for future nutrition data collection

8 % adequate POPULATION PREVALENCE Does mode affect estimated population prevalence of adequate nutrition? CATI * * 30 * MDD-W MDD MMF MAD Indicator

9 INDIVIDUAL SCORES Does mode affect individual dietary diversity or meal frequency measures? Difference (CATI - ) p MDD-W food groups 0.22 MDD +0.5 food groups < MMF meals per day <

10 FOOD GROUP RESPONSES Does mode affect survey questions (food groups) differently? MDD-W: Agreement (% respondents) Grains Pulses Dairy Higher w/ CATI Lower w/ CATI Dark Greens Vegetables Higher w/ CATI Fruits Meat Eggs Vitamin A Nuts & Seeds

11 FOOD GROUP RESPONSES Does mode affect survey questions (food groups) differently? Grains MAD: Agreement (% respondents) Pulses Milk Higher w/ CATI Mixed Other Dairy Meat Eggs Vitamin A Fruits & Vegs Higher w/ CATI Higher w/ CATI Fortified Foods Breastfed Ever Breastfed Yesterday

12 MDD-W SAMPLING BIAS? Does excluding people without mobile phones bias nutrition estimates? Women without mobile phones had fewer assets But did not necessarily have significantly lower MDD-W scores Kitui Phone No Phone Baringo

13 % Change QUANTITATIVE SUMMARY Change in Nutrition Data with CATI 20 Prevalence Score MDD-W MDD MMF MAD

14 CAN WE USE CATI FOR NUTRITION? ADVANTAGES High success rate for calls (80%) One-third the cost of per survey ($5 vs. $16) Feasible in insecure areas No change in MDD-W data (or PPI) CHALLENGES Biased towards better nutrition for MAD Social desirability bias? Consistency in bias? Comparison to data? Golden Standard? Potential sampling bias Network coverage Use in other social situations YES! But more work is needed

15 THANKS! For further information: Christine Lamanna Kusum Hachhethu

16 INDIVIDUAL SCORES Does mode affect individual dietary diversity or meal frequency measures? MDDW MDD MMF In addition to higher means for MDD and MMF, MMF in particular also shows higher variance with CATI

17 INDIVIDUAL SCORES Does mode affect individual dietary diversity or meal frequency measures?

18 SAMPLING BIAS? Does excluding people without mobile phones bias nutrition estimates? Women without access to mobile phones are Kitui Baringo 1. Age Younger** = 2. HH Members Have fewer HH members*** = 3. Education = Less educated 4. Employment Less likely to have male head Less likely to be employed Less likely to be employed 5. Number Rooms = Have fewer rooms in their house* 6. Flooring More likely to have dirt floors** More likely to have dirt floors* 7. Lighting Less likely to have electricity Less likely to have electricity 8. Iron = Less likely to own an iron* 9. Cooking Pots Have fewer cooking pots*** Have fewer cooking pots* 10. Towels Have fewer towels*** Have fewer towels*** 11. Mosquito Nets = =