Presented by Jenny Cervinskas, Bong Duke RAMP Survey < Rapid Mobile Phone-based > Changing the way we collect data in Surveys Presented by Jenny Cervinskas, Bong Duke and Laimi Onesmus Windhoek, Namibia February 7, 2012

Outline Background to the survey Preliminary results Survey report Q&A/Discussion Next steps?

With the RAMP you automate your survey Conduct surveys and enter data using a standard mobile phone Manage surveys, people and data from your web-based server

How does it work?

RAMP survey site: Caprivi region, Namibia

Site and project identification Caprivi region (north eastern Namibia) Ongoing NRC project on Communities Fighting Malaria Malaria a serious public health program Malaria prevention and control strategies being implemented RAMP malaria indicators survey provides a measure for some of the project’s key indicators, and for some of the Namibia’s core malaria indicators Mobile network coverage (MTC provider)

Survey Objectives Estimate ITN ownership Proportion of individuals with access to an ITN in the household (assuming one ITN covers two persons) Proportion of households with one or more ITNs.

Estimate ITN use Proportion of all persons in the household that slept under an ITN the previous night Proportion of children < 5 years old who slept under an ITN the previous night Percentage of ITNs that were used last night.

To assess ITN ownership and usage by household socio-economic status To measure the proportion of households with at least one ITN and/or sprayed by IRS in the past 12 months.

To measure prompt access to effective treatment and use of diagnostics Proportion of children < 5 years old with fever in the last two weeks who received anti-malarial treatment according to national policy Proportion of children < 5 years old with fever in the last two weeks who had a finger or heel stick.

Survey questionnaires Modeled after the standardized MIS (malaria indicator survey) questionnaires Household questionnaire Person roster/Treatment and diagnosis of fever in under-fives Net roster Types of bednets, source of nets, age of nets, who slept under each ne, number of people that slept under each net

Survey methods Standard survey methodology used in a RAMP survey 1st stage: standard probability-proportional-to-estimated-size (PPES) selection of clusters Master Sampling frame:, 2008 (from Bureau of Statistics) 2nd stage: PPES to segment the cluster Selection of households- simple random sampling (SRS) to choose 10 households/cluster - Estimated number I f the 300 HHs of the survey= 1500 persons, all ages (based on 5.0 pp pers HH)

300 Households 30 clusters, 10 households/cluster, total of 300 households in the survey sample

Recruitment of surveyors NRC volunteers that serve as supervisors in the CFM project (interviewers) 12 of the 18 had been involved in the 2011 RAMP survey in Caprivi Training – 4 days (January 2012) in Katima Mulilo

Adapted from the RAMP survey curriculum and guide Training Content Cellphone basics Questionnaires Informed consent Interview techniques Field procedures Field logistics/reporting Supervisor training Methods Presentations, role play, group discussion, demonstrations, field practice, energizers Adapted from the RAMP survey curriculum and guide

Red Cross volunteers carried out the interviews Six teams- two interviewers and one supervisor per team +Survey Supervisory Support Team

Fieldwork Locating the households Household interview

SENDING DATA TO THE SERVER

SENDING DATA TO THE SERVER

Real-time data editing and cleaning Data is monitored remotely Daily data editing and cleaning

Survey Team Debriefing: 1 day after last interviews Presentation and discussion of preliminary results

Award of certificates “I feel happy knowing how to collect data with the cellphone” Organizers are happy the survey was completed successfully

Preliminary Results- RAMP malaria survey Caprivi region January-February 2012 survey

Survey results bulletin & report Survey Report

Results: key indicators, HH questionnaire

Survey-estimated ITNs in HH of target pop 13,038 (52%) Access: Just 52% of ITNs needed to reach universal coverage are present. Gap is 48%. Key indicators Target population 46,727 Persons per net 1.88 ITNs needed 11,816 Survey-estimated ITNs in HH of target pop 13,038 (52%) ITN/LLIN need/gap 11,416 (48%)

% ITNs that were slept under last night 94% Results: High percentage of ITNs are being used. Use gap is due to insufficient ownership of ITNs Key indicators Point estimate % ITNs that were slept under last night 94% % ITNs that were hung last night ITN use, all ages 44% ITN use, <5 yo 55%

ITN use by age group

Age of ITNs % Cum. % Age in months <1 Year 15 1 Year 28 43 2 Years 16 58 3 Years 26 85 4 Years 6 91 >5 Years 5 96 In the HMM project, there was a distribution of LLINs in August 2009; targeting children aged x months to 5 years) - the 1st national LLIN distribution was done in 2006 (ie., about 3.5 years ago) * 97% of nets were LLINs

Number of persons sleeping under ITN last night Number of persons under a ITN last night Number of persons sleeping under ITN last night %, nets 1 person 33 2 persons 48 3 persons 16 4 persons 2

Children Tested and Treated for Malaria Key indicators % Children with Fever within the last two weeks 49 % who received a heel or finger stick 25 % who received ACT 29 % who received ACT within 24hrs of fever 22 % who received any anti-malarial 46 - Denominator for all indicators was % of children <5y with fever in the previous two weeks

The # ITNs in the whole survey domain= 52% of the need A main message… There is an ITN gap The # ITNs in the whole survey domain= 52% of the need The gap is 15,965 nets (11,816+4,149)

So, does the RAMP “work”? RAMPS Namibia February 2012 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

RAMP has made surveying EASY!!! “I feel happy knowing how to collect data with the cellphone” Organizers are happy the survey was completed successfully

Lessons learned Take time to prepare cellphones prior to the survey Establish strong working partnerships Develop and implement a capacity building plan, with dedicated human resources Data entry: worked well, all teams were able to collect data using the cellphone and send to server Red Cross volunteers with secondary school education can collect data in the field

What’s next? Extract lessons learned from Namibia and apply in next survey Implement RAMP malaria and other health surveys in Africa and other continents Finalize and disseminate the RAMP survey technical manual and the training manual Continue developing strategies for technical support in order to gradually reduce external support Test the RAMP in other sectors and disciplines if appropriate Continue searching for innovative ways to collect data in a timely fashion in order to better serve the communities we work in

Thank you Any questions?

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THANK YOU

Thank you!

Extra Photos & Information

Purpose of the RAMP survey To provide a survey methodology in which Red Cross and Red Crescent National Societies, governments and other partners can conduct health surveys at reduced cost, with limited external technical assistance achieve high standards of survey design and quality To dramatically decrease the time that data is available for decision making To use mobile phones and a web-based, freely accessible software domain as a data collection technique to conduct health surveys.

Key Features of the RAMP survey Web based questionnaire design using EpiSurveyor Questionnaire forms are uploaded to standard mobile phones Data is collected using low cost, familiar and widely available mobile phones (e.g. Nokia, Samsung) Has an accompanying training manual, technical manual, and tools adaptable to your setting/your environment Data can be exported to Microsoft Excel, as a text file, and in Mdb format Allows for rapid analysis and reporting of survey results

Traditional Paper and Pencil Questionnaire The time and monetary costs of data collection can be substantially reduced if mobile phone data collection is used in place of the traditional paper and pencil method that has been the best practice in health surveys for decades

Web Based Server Create a free account using EpiSurveyor software Access your web based server from a web browser anywhere in the world Design your questionnaire with single or multiple languages Monitor, manage and communicate with your team Export data and analyze results in real-time.

Why use mobile phones to collect data? Real-time data entry on cell phones Limitless upload of data from cell phone over cell network to internet database Real-time data monitoring and data quality checks Real-time data cleaning and analysis Rapid production of survey results within minutes of last interview

Stakeholder benefits Decision Makers No software licensing or subscriptions Optimizes resource usage and reduces environmental impact Maintain data security and respondent confidentiality Scalable solution for teams and studies of varying sizes Evaluators/Researchers Incorporate a multitude of question types with custom logic and validation Manage and upload surveys in multiple languages Monitor staff work rate, productivity and quality Export data for custom analysis with your favourite statistical analysis package Fieldworkers Conduct surveys anywhere, even in areas with no network coverage Use standard and familiar mobile phones Minimal training requirements No more paper to collect, transport or return Automated submission of data when network reception is available

RAMP malaria survey: Namibia provides leadership

Cellphone-based Surveys: Summary Points REAL-TIME DATA AVAILABILITY AND ANALYSIS Via your web-based server, responses may be viewed, monitored and exported instantly IMPROVED DATA INTEGRITYThe removal of paper from the research process reduces the number of points at which error can be introduced FIELDWORKER MONITORING/MANAGEMENT Monitor the productivity and quality of research conducted by field staff (GPS, time and date stamp) ENHANCED MOBILITY Do not need network coverage to conduct surveys, responses are stored securely on the mobile phone, thus can reach even the most remote communities OPTIMISED RESOURCE USAGE Save on survey printing, distribution and collection costs

49% of households are headed by women

Morning briefing (“quality round”) A day’s schedule Morning briefing (“quality round”) Locating the chosen cluster and selecting the households to be interviewed Conduct interviews at HH level Supervisor sends data to server Data cleaning and analysis