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FIT SURVEILLANCE

FIT SURVEILLANCE. In Brief. intro. question. comment. teams. details. background. Presented by: Adetoun Dapo – Famodu Nutrition Section, UNICEF Abuja. Then to Now…. 2016. 2017. 2019. I. II. III. background. teams. question. comment. details.

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FIT SURVEILLANCE

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  1. FIT SURVEILLANCE In Brief intro question comment teams details background Presented by: Adetoun Dapo – Famodu Nutrition Section, UNICEF Abuja.

  2. Then to Now….. 2016 2017 2019 I II III background teams question comment details background INP INP+ FIT UNICEF, ACF, WFP Nutrition - specific £16.1m Borno and Yobe UNICEF, ACF, WFP Nutrition specific + Multisectoral pilot £33.5m Borno and Yobe UNICEF Integrated + pilot for 1 year £20.8m Borno

  3. intro teams question comment details background The FIT Project DFID - funded Flexible, integrated and timely response to nutrition-related humanitarian needs in Borno state (FIT Response) 3 years (2019 to 2022), £20.8 million, integrated for nutrition project Backbone is a routine surveillance system

  4. The FIT – Response Project Flexible Integrated Timely intro teams question comment details background

  5. The FIT – Flexible intro teams question comment details background FIT – Response Surveillance

  6. The FIT – Integrated intro teams question comment details background

  7. The FIT – Timely Timely: available contingency funds and supplies to respond to dire emergency issues in a week or two depending on the type of emergency intro teams question comment details background

  8. The FIT – Response Surveillance Architecture Data Timeline intro question comment teams details background

  9. Architecture Small – scale surveys and repeated surveys (current NFSS)

  10. Architecture Facility – based data All accessible wards in an LGA (current info:185 out of 310) Monthly Community sentinel Outbreak data 5 settlements visited monthly for collection of sentinel data 15* settlements/ward selected Total of 2,775* settlements * Might be less depending on the accessibility profile

  11. Architecture Facility – based data Community sentinel Randomly selected accessible health facilities providing OTP services in an LGA Outbreak data All 5 visited monthly for collection of sentinel data 5 health facilities per LGA (where less than 5, data will be collected from all)

  12. Architecture Facility – based data Community sentinel At LGA levels through LGA DSNOs as outbreaks are detected Links with WHO EWARS at state level Outbreak data

  13. Data Facility – based data Monthly Community sentinel Outbreak data • Proxy GAM prevalence using MUAC • Mean MUAC change • Mean weight change • Meal frequency • 7-day diarrhea recall • Recent arrivals, departures, births and deaths • Note: 6 – 23 months • Number of new SAM admissions • Mean MUAC at admission • Mean weight at admission • Note: for all new admissions • Measles outbreak • Cholera outbreak

  14. Proposed Timeline Begin first data collection from 9th to 13th September 2019 Train sample census enumerators in 2 batches Train LSCs on surveillance data collection 25 Jul – 25 Aug 2019 3 – 6 Sep 2019 8 – 16 Jul 2019 18 – 21 Jul 2019 28 – 30 Aug 2019 Train SAs on sentinel data collection in 2 batches Census data collection from selected sentinel sites Recruitment of SAs Finalize sentinel data collection tools Finalize protocol/census tools with NBS Select surveillance areas Finalize recruitment of census data collectors

  15. 01 02 intro question comment teams details background 03 04

  16. FIT to sector Sector to FIT • Review the results of the surveillance data during sector meetings monthly • Discuss recommendations made based on the surveillance data • Visit and use the info on the web-based platform • Give inputs on how the system can be made better to profit the sector • Availability of ward level sentinel* data for better programming • Data is available to all sector partners on a web-based platform that can be assessed anywhere at anytime (link will be shared when live) • Aiming for more proactive as opposed to reactive nutrition sector programming intro question comment teams details background

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