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Improving Quality of Geographical Coordinates Data In The Master Facility List (MFL) Using Secondary Data By Maria Kamau, MBA HIS Cordinator, USAID-AfyaInfo 2014WEAB006 AfyaInfo 26-Nov The authors views expressed in this presentation do not necessarily reflect the views of the United States Agency for International Development or the United States Government. Amref Health Africa International Conference (AHAIC, 2014) Slide 2 AfyaInfo| pg 2 AfyaInfo Project Support To GOK USAID -> Abt Associates -> AfyaInfo Project To create a unified and integrated, internet-based, host country owned and managed, National Health Information System (NHIS) Slide 3 AfyaInfo| pg 3 Unified NHIS: Three pronged approach I.Technology application / Infrastructure deployment to establish an integrated, web-based, country owned and managed, National Health Information System (NHIS) that generates quality data used at all levels to improve health service delivery. II.Learning and Knowledge Management support in the form of Capacity Building & Technical Assistance in data management, use of NHIS applications, dissemination of information products and use of information to improve the culture of information generation, knowledge and information use. III.Organisational Development and strengthening Stakeholder engagement to facilitate coordinated support to HIS in the counties, cooperation in data quality improvement through data review meetings and overall improvement in monitoring through performance review meetings Slide 4 AfyaInfo| pg 4 Major Public [Online] NHIS Applications used countrywide [Gold Std] MFL [Gold Std] Master Facility List encompassing detailed information on ALL formal health facilities providing services including public & private sector (9,600+ HFs) www.ehealth.go.ke DHIS v2.13 District Health Information Software implemented in 2011 and used to key in routine health service data, upgrade soon to v2.16 (7,000+ HFs) www.hiskenya.org MCUL Master Community Unit List (sub-system of MFL) of all registered Community Units in the country (2,900+ CUs) www.ehealth.go.ke/mcul Slide 5 AfyaInfo| pg 5 MFL Implemented 2010 www.ehealth.go.ke Search by: Code Name Type Service Location Slide 6 AfyaInfo| pg 6 MFL Primary Data Available Each Record: Unique 5-digit Code Name Type, Owner, Locational details, etc. HF Contact Services / Beds / Cus / Status Rating facility Geo-location Slide 7 AfyaInfo| pg 7 MFL: Dynamic HF Map Generation Slide 8 AfyaInfo| pg 8 Overview of MFL Geocodes Data (Before SARAM) #DescriptionMFL 1.Date of Reference26-Jul-2013 2.Total Records Reviewed 9,906 3.No. with valid geocodes 8,781 4.% with valid geocodes89% 5.No. without valid geocodes 1,125 6.% without valid geocodes11% Slide 9 AfyaInfo| pg 9 Distribution of HFs across the country Looks ok except for a few Kenya HFs (Before) Slide 10 AfyaInfo| pg 10 Kenya HFs Distinguished by county Slide 11 AfyaInfo| pg 11 On closer observation (distinguished by color): Not quite ok Slide 12 AfyaInfo| pg 12 Isolate by County e.g. Machakos Slide 13 AfyaInfo| pg 13 Isolate by County e.g. Kilifi Slide 14 AfyaInfo| pg 14 SARAM (Secondary Data) Kenya Service Availability Readiness and Assessment Mapping To support the hand over of County assets, the Health Sector is expected to have a comprehensive inventory of its assets and liabilities that need to be handed over MOH / WHO / USAID-AfyaInfo Project Countrywide: ALL facilities providing health services i.e. all types, agency run, locations, levels. Comprehensive data collection including geocodes for facilities to verify for all facilities in the MFL, using a standard approach Implemented March to April, 2013 (Nairobi & Kajiado completed in July) Slide 15 AfyaInfo| pg 15 SARAM: March July, 2013 #DescriptionSARAM 1.Date of Reference26-Jul-2013 2.Total Records Reviewed 8,059 3.No. with valid geocodes 7,926 4.% with valid geocodes98% 5.No. without valid geocodes 133 6.% without valid geocodes2% Slide 16 AfyaInfo| pg 16 SARAM: Distinguished by county Slide 17 AfyaInfo| pg 17 Data Cleaning applied in Stages Stage one used freely available QGIS v1.8.0 to Added 5-km buffer to county boundary shape file Established which points landed within their correct counties and which did not Stage two compared matching results between MFL data and SARAM data Isolated the points landing in correct county AND match AND within 200m distance apart OR landing in correct county in at least ONE of the data sets Stage three - Compared matching points by distance and used other parameters to determine correct point Stage four consolidated all findings and determined action points i.e. replace primary data with secondary data, leave as is or mark for re-collection in the field Slide 18 AfyaInfo| pg 18 County Shape files with Buffer border (5km) Slide 19 AfyaInfo| pg 19 MFL data with geocodes mapped (valid but not necessarily accurate) Slide 20 AfyaInfo| pg 20 Apply categorization feature to differentiate different county points by colors e.g. below (Nyandarua County seems to have many visitors i.e. points belonging to other counties) Slide 21 AfyaInfo| pg 21 Data Cleaning: STAGE ONE (cont.) To assess accuracy of the geocodes Use QGIS join by location feature applied to the 3 key shapefiles (i.e. County, 5 km buffer & Lake) to identify: Slide 22 AfyaInfo| pg 22 Process demo: E.g. Kirinyaga County Slide 23 AfyaInfo| pg 23 iii. Geocodes positioning: 5km Buffer gives room for county shape file errors External buffer is considered part of county in focus Geocodes land in A A = Correct County B B = Correct 5 km Buffer (Kirinyaga) C C = Wrong County or buffer in wrong county Slide 24 AfyaInfo| pg 24 E.g. Homa Bay County in Focus A A = Correct County B B = Correct Buffer (Homa Bay) C C = Wrong County or buffer in wrong county D D = Lake E E = Out of Country Slide 25 AfyaInfo| pg 25 Latitude is degrees north (+) or south (-) of the Equator Numbers representing a unique location on Earth Latitude & Longitude Kenya Geocodes delimitations: Equator, North and South Counties 0.0 e.g. 1.22997, 36.89238 e.g. -1.22997, 36.89238 Slide 26 AfyaInfo| pg 26 Data Cleaning: Database Comparison Stage ONE RESULTS Slide 27 AfyaInfo| pg 27 Sample Data Results Comparing MFL data with SARAM NB: #N/A means the MFL code was not found in the SARAM data set Data Cleaning: Record Comparison Stage TWO REVIEW Slide 28 AfyaInfo| pg 28 Use QGIS points2one add in feature Applying excel distance calculator formula MFL_codeCounty_1 Distance apart (km) Distance apart (mts) 11776Mombasa 1 1,333 15823Busia 0 287 14181Nakuru 17 17,310 14182Kajiado 48 48,373 17582Baringo 8 8,003 14183Nakuru 26 25,703 12442Embu 54 54,003 19651Kitui 0 281 19162Garissa 97 96,533 11919Tharaka Nithi 1 1,342 Data Cleaning: Accuracy check Stage THREE REVIEW Slide 29 AfyaInfo| pg 29 STAGE THREE REVIEW / ANALYSIS ILLUSTRATED: Kirinyaga County (For each set, one is on 5 km buffer; other is in correct county) Slide 30 AfyaInfo| pg 30 STAGE THREE REVIEW / ANALYSIS ILLUSTRATED: Kiringaya County (For each BLUE set, one is on 5 km buffer; other is in correct county; For each PINK set, both are in 5 km buffer OR both in correct county) Slide 31 AfyaInfo| pg 31 Data Cleaning: Stage FOUR RESULTS Slide 32 AfyaInfo| pg 32 Outcome after all efforts Total geocodes updated by Saram 2,600+ (30%) records in MFL updated Mapping much more accurate Most facilities now located within home county though in-county correctness still needs to be assessed Data updating & cleaning is a continuous process Slide 33 AfyaInfo| pg 33 After clean-up using SARAM (Secondary) Data Kenya HFs (After) Slide 34 AfyaInfo| pg 34 HFs Per County Distinguished by colour Slide 35 AfyaInfo| pg 35 1. Machakos 2. Kilifi Slide 36 AfyaInfo| pg 36 GIS PRODUCTS: HF Maps Slide 37 AfyaInfo| pg 37 GIS Products: County Fact Sheet Slide 38 AfyaInfo| pg 38 GIS Products Distribution of CUs Slide 39 AfyaInfo| pg 39 Service Availability (Per Service) Facilities offering services related to: NTDs PMTCT HIV & STI prevention Slide 40 AfyaInfo| pg 40 Hospital Service Availability (Combined) Facilities offering ALL 3 services: Inpatient Diagnostics General Operations Slide 41 AfyaInfo| pg 41 Service Availability (Per Service) Health facilities per county offering : Immunisation Child Health Services Antenatal Care Slide 42 AfyaInfo| pg 42 CHALLENGES About 500 records still have unresolved data quality issues missing, incomplete, wrongly located in both MFL & SARAM Unavailable / Restricted / Incomplete data sources: Google maps which includes details of roads, landmarks, etc. are helpful in urban areas but incomplete in rural areas (details are scanty) Even where details on google maps are extensive, most health facilities are not named or identified No authoritative source of location shape files and some are not updated e.g. boundaries vary depending on source of county files, updated ward or sub-location files not available or restricted access; Updated population, infrastructure (e.g. roads) and natural resources (e.g. rivers, forest coverage), etc. shape files not easily available or accessible Slide 43 AfyaInfo| pg 43 Summary Collecting data is resource-intensive on time, finances and expertise in data management Using secondary data can also be tedious and time-consuming, as well as raise many more questions depending on quality, but more often considerably less resource-intensive than collecting it Secondary data from SARAM was vital for updating existing MFL data and highlighting gaps to be addressed moving forward Maps cannot be applied in all areas of analysis but are vital for complementing visual understanding of health service availability Related gaps therein SARAM is a significant resource to help counties update other data vital for counties in planning Updating service availability and health facility details to better reflect reality on the ground priority (underserved) areas Slide 44 AfyaInfo| pg 44 Thank you!