Presented by Kioko Kiilu (KRCS) Jenny Cervinskas (IFRC) Nairobi, February 1 st, 2011

 Introduction to Rapid Mobile Phone-based (RAMP) survey  RAMP experience with KRCS volunteers ◦ Site and project identification ◦ Survey methods, training, fieldwork ◦ Lessons learnt  Preliminary results  Plenary

 To provide a survey methodology and operations protocol so that governments and NGOs can: ◦ conduct health surveys at reduced cost ◦ with limited external technical assistance ◦ and achieve high standards of data quality  Dramatically decrease the time from data collection to having data available for decision making

 Technical Reference Manual  Standardized questionnaires for malaria  Questionnaires designed on the internet using EpiSurveyor  Data collected using cell phones  Training manual and tools adaptable to local settings  Standard survey methods  Rapid analysis and reporting of results

Mobile technology can drastically reduce the time between data collection and action. Weeks to Months (sometimes continuous) Weeks to Months to Years Weeks to Months to Years (or never) Weeks to Months (sometimes continuous) Questionnaire Design Data Entry Data Analysis Data Reporting ACTION Data Collection

 EpiSurveyor has: ◦ eliminated the need for data entry and is now automating many analysis and reporting functions ◦ shortened the time and reduced the costs between collection and action  Anyone can create a username and password at and start using these tools for free Questionnaire Design Data Entry Data Analysis Data Reporting ACTION Data Collection

 Questionnaire design in Episurveyor (internet)  Real-time data entry on cell phones  Daily upload of data from cell phone over 2G cell network to internet database  Real-time data cleaning  Real-time data analysis  Rapid production of preliminary survey results bulletin within 24 hours of last interview  Rapid production of preliminary feedback survey report in 72 hours

 Ongoing operational research project in malaria  Hard-to-reach areas/Long data cycle  Mobile network coverage  Project: Home Management of Malaria (HMM) in Malindi district, Coast province

 1 st stage: standard probability-proportional-to- estimated-size (PPES) selection of PSUs ◦ Sampling frame: 106 villages of the HMM project  2 nd stage: segmentation of PSU; choose 1 segment using PPES  SRS to choose 10 households  Precision:  +/- 6% for each key indicator from household questions  +/- 3% using roster/individual data  30 PSUs, 10 households per PSU, 1500 persons, all ages

 Household questionnaire ◦ Usual household characteristics (wealth asset questions, distance to health facility, etc.) ◦ Summary questions (innovation)  Duplicated nearly all key indicators that are in the person & net register  Eg., no. of persons: all ages & children <5 yo  No. of any nets, ITNs  No. of persons/children <5 yo slept under ITN last night  Person roster  Net roster ◦ Number of persons that slept under each net

 HMM volunteers (Interviewers)  HMM Coaches /MOH Public Health Officers (PHOs) (Supervisors)  Training – 4 days (January 19-22, 2011)

 Content ◦ Cellphone basics ◦ Questionnaires ◦ Informed consent ◦ Interview techniques ◦ Field procedures ◦ Field logistics/reporting ◦ Supervisor training  Methodology ◦ Presentations, role play, group discussion, demonstrations, field tests (2)

 Survey teams: ◦ 6 teams  1 Team supervisor and 2-4 interviewers/team)  Survey supervisory team (KRC, IFRC, WHO, MOH, DataDyne): ◦ Planning, logistic & financial responsibilities, field support, daily “quality” rounds, and remote monitoring of data quality

 Morning briefing (“quality round”)  Meeting with community leaders, reviewing sketch maps, segmentation, selection of HHs  Conduct interviews at HH level  Supervisor will send data to server  Debriefing at day’s end with support team in Malindi  Data cleaning and analysis

 Data entry: worked well, all teams were able to collect data using the cellphone and send to server  Survey conducted with reasonable adherence to correct field procedures  KRC volunteers were able to prepare the sketch maps, carry out segmentation, and apply SRS to select HHs  Preliminary results were available within 24 hrs. of the return of the last team from the field

Key indicators Target population Persons per net2.47 ITNs needed Survey-estimated ITNs in HH of target pop (68%) ITN/LLIN need/gap8 904 (32%)

Key indicatorsPoint estimate % ITNs that were slept under last night87% % ITNs that were hung last night86% ITN use, all ages55% ITN use, <5 yo65% * 47% of nets had 3 or 4 persons sleeping under them

Age in monthsCumulative % <12 months months months80 Age of ITNs * 88% of nets were LLINs

Number of persons sleeping under a single net last night %, nets 1 person15 2 persons39 3 persons32 4 persons15 Key results from roster-only data

Key indicators% Treated ACT, <5 yo77 Treated ACT within 24 hours, <5 yo69 Received finger/heal stick for blood14 - Denominator for all indicators was % of children <5y with fever in the previous two weeks

 No major problems: all cellphones were operational  No calls to the Datadyne “hotline”  Data entry: worked well  Data was sent to the server by all teams, every day  Daily/immediate upload of data if 2G/GPRS available  Potential difficulties: initial connection of cell phone to data network

 Internet database  Core STATA code for daily data cleaning & analysis  Early identification & feedback of data quality issues  Enabled reasonably-clean data within 24 hours of the last interview

 Conducted by secondary-school graduates with no previous survey experience  Survey was completed within two weeks ◦ 1 week training, 4.5 days field work  Daily data cleaning accomplished  Preliminary survey results bulletin finished within 24 hours  Preliminary report finished within 72 hours  Provided excellent management information on the key indicators

Cost componentUSD Local operational costs (e.g., personnel costs: avg. $40 per interviewer & supervisor/day * 20 persons * 10 days, training hall, stationary) Phones + accessories5 416 Transport (drivers, fuel)3 950 Total Approx Kshs1.7m Analysis + ReportFree (WHO)

Kenya Red Cross Volunteers Kenya Ministry of Public Health and Sanitation IFRC Datadyne WHO Kenya Bureau of Statistics A special thanks to the survey team and the many families who agreed to be interviewed for this survey

Thank-you for your attention