mdlimodeling rf lreferral nt knetworks to maternal in ethiopia...referral network total population...
TRANSCRIPT
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M d li R f l N t k Modeling Referral Networks to Avert Maternal Death in Ethiopia
Caleb ParkerGIS AnalystBehavioral and Social SciencesFHI
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Presentation Overview
• Referral backgroundReferral background• Setting: Ethiopia• Network AnalysisNetwork Analysis
– Objectives, data and methods– Defining referral networksDefining referral networks– Model building and preliminary results
• Next steps in EthiopiaNext steps in Ethiopia
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REFERRAL BACKGROUND
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What and why referral?
• Defined: upward movement in the seeking of care within p gthe health system.– Emergency Referral
f l i k i h fi ld f l d b• Referral is key in the field of maternal and newborn health– “under‐documented, under‐researched and under‐o , u de e e c ed d u de
theorized.”
• Referral utilizes current resources through linkages in a tsystem
– Cheaper– Short to medium‐term solutions
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Time between the beginning of a complication and death
Complication Hours DaysH h Hemorrhage
Postpartum 2
Antepartum 12
Ruptured uterus 1
Eclampsia 2
Obstructed labor 3Infection 6
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The 3 Delays Model3 y
Referral has the potential to reduce all 3 delays
DELAY #1 DELAY #2 DELAY #3Recei ing
Referral has the potential to reduce all 3 delays
Deciding to seek care
Reaching a facility
Receiving adequate
care
Onset of Recovery or deathComplication
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Pyramidal structure & bypassing
Regional Hospital Receiver
District Hospital
Transport
Health center/post
p
/dispensary
Sender
Adapted from Jahn & De Brouwere, 2001Community
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Requisites of a functioning referral systemq gCommunication
F ti iTransport
Functioning referral center
Protocols for senders &receiversreceivers
S M SF P SC M t it f l t i d l i t i C t k l d d f t hSource: Murray SF, Pearson SC. Maternity referral systems in developing countries: Current knowledge and future research needs. Soc Sci & Med 62, 2006.
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SETTING: ETHIOPIASETTING: ETHIOPIA
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Setting: Ethiopia
• Population: 73 millionp 73
• Area: 1.1 million km2
Twice the size of Texas– Twice the size of Texas
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Regions of Ethiopia
hAmhara
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Tigray and Amhara – Key Indicators
Tigray Amhara NationalTigray Amhara National
Population (2007 Census) 4,400,000 17,200,000 73,900,000
Number of hospitals 14 18 112Number of hospitals 14 18 112
Percent minimum recommended (CEmOC) 55% 18% 39%
Institutional delivery rate 9% 5% 7%Institutional delivery rate 9% 5% 7%
Cesarean delivery rate 0.7% 0.2% 0.6%
Percent of health centers with desired staffing 45% 2% naPercent of health centers with desired staffing 45% 2% na
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OVERVIEW OF DATA AND OVERVIEW OF DATA AND METHODS
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Background for this activity
• Ethiopia Needs Assessment for EmONC conductedp
– 797 Healthcare facilities
Geocoded– Geocoded
• Staff saw potential GIS use
• Emergency Referral Network Analysis
– Strengthening referral systems could save lives in short and medium term, even as longer term strategies are implemented
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Objectives:
1. Model current referral system y
2. Identify facilities 2+ hours from closest receiving facility
3. Model short and medium term solutions to reduce travel time
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Data
1. Ethiopia EmONC Assessmentp2. Population Data – 2007 Census projected to 20083. Digital Elevation Model3 g4. Road Network Data
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Ethiopia Elevation
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Ethiopia Road Network
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Road Network Discrepancies
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Road Network Discrepancies
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Methods
1 Define Sender and Referral Facilities1. Define Sender and Referral Facilities
2. Model Current Referral Network
3. Model Referral Scenarios
B ild f ilit t h t – Build facility catchment areas
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Defining facilities
• Tier A (Receivers)– conducted cesarean delivery in 3 months prior to the assessment
• Tier B (Senders)– all other facilities
• Identified referral networks based on • Identified referral networks based on shortest travel time to a Tier A facility
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Model Current Referral Network
Key Variable: Travel Timel l d di l i f h iCalculated direct travel time for each Tier B
facility based on:Di t d t kDistance on road networkRoad surfaceSeason
Calculated adjusted travel time based on:Motorized transportation available on siteCommunication (if no transportation)
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Key Variable – Travel Time
Factors Tier B Facility #1 Tier B Facility #2Factors Tier B Facility #1 Tier B Facility #2Direct travel time (min) to closest Tier A
48 118
‐ Road Surface (in GIS) (in GIS)
‐ Season Dry Dry
Transportation on site No (48x2=96) Yes (118x1=118)Communication No (96+30=126) N/AAdj t d t l ti 6 8Adjusted travel time (min)
126 118
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Percentage of facilities within 60 and 120 minutes to closest Tier A facilityminutes to closest Tier A facility
C l i i Cl CCumulative Minutes to Closest Tier A Facility
Count Percentage
Under 60 77 35.8%Under 60 77 35.8%61 to 120 64 29.8%More than 120 74 34.4%3
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Model building
• Build Catchment AreasM d l 0 St t Q• Model 0: Status Quo
• Model 1: All facilities have motorized transportation and communicationand communication
• Model 2: Upgrading strategic Tier B facilities to Tier A facilitiesfacilities
• Model 3: Improved road surfaces• Optimal model: what is the best outcome with the • Optimal model: what is the best outcome with the
lowest input (additional phones/vehicles)
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Creating Catchment Areas
• An area that “should” be served by a facilityAn area that should be served by a facility• Cost Distance Raster in ArcGIS
– SlopeSlope– Rivers/terrain
• Once created, calculated populationOnce created, calculated population
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Facility Catchment Areas with Road Network
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Referral Networks with Facility Catchment Areas
Referral Networks
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Population Density by Catchment Area
Population Density (sq km)
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Model building
• Build Catchment Areas• Model 0: Status Quo• Model 1: All facilities have motorized transportation
and communication• Model 2: Upgrading strategic Tier B facilities to Tier A
facilities
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M d l 0 STATUS QUO
Facility Catchment Areas Beyond 2-hour Travel Time
Model 0: STATUS QUO Total Population
Population Currentlywith Access
10,019,395 5,626,099 56.2%
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Model building
• Build Catchment AreasBuild Catchment Areas• Model 0: Status Quo• Model 1: All facilities have motorized transportation Model 1: All facilities have motorized transportation
/communication• Model 2: Upgrading strategic Tier B facilities to Tier A Model 2: Upgrading strategic Tier B facilities to Tier A
facilities
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M d l 0 STATUS QUO
Facility Catchment Areas Beyond 2-hour Travel Time
Model 0: STATUS QUO Total Population
Population Currentlywith Access
10,019,395 5,626,099 56.2%
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Facility Catchment Areas Beyond 2-hour Travel Time
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Model 1: Increase in population served by ensuring that every Tier B facility has a motorized vehicle
Total Population Currently Population with Overall Increase in Referral Network Population with Access* Access in Model 1 Population Access
1412 – Pawe 1,338,664 646,721 48.3% 646,721 48.3% ‐ 0.0%
1505 – Debre Tabor 2 723 313 2 182 465 80 1% 2 447 996 89 9% 265 531 9 8%1505 Debre Tabor 2,723,313 2,182,465 80.1% 2,447,996 89.9% 265,531 9.8%
1543 – Felege Hiwot 2,156,109 1,151,324 53.4% 1,994,981 92.5% 843,657 39.1%
1623 – Debre Markos 3,801,309 1,645,589 43.3% 2,660,041 70.0% 1,014,452 26.7%
TOTAL 10,019,395 5,626,099 56.2% 7,749,739 77.3% +2,123,640 21.2%
*Only for persons at the Tier B facility
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Facility institutional delivery rates
Institutional delivery rate
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M d l 0 STATUS QUO
Facility Catchment Areas Beyond 2-hour Travel Time
Model 0: STATUS QUO
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Facility Catchment Areas Beyond 2-hour Travel Time
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Model 2: Increase in population served by upgrading Fenote Selam District Hospital to a Tier A
Total Tier B Facilities in New Tier A
Referral Network
Total Population in New Referral Network
Population with Access to their Current Tier A
Population with Access to New Tier A
Overall Increase in Population with
Access
16 1 945 023 293 895 15 1% 781 640 40 2% 487 745 25 1%16 1,945,023 293,895 15.1% 781,640 40.2% +487,745 25.1%
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Next Steps in Ethiopia
Further modelingg• Combinations of models (ex. Motorized vehicles plus upgrading)• Improved road surfaces• Optimal model: what is the best outcome with the lowest input • Optimal model: what is the best outcome with the lowest input
(additional phones/vehicles)• Costing
Federal MoHWorking Group on Referral• Site selection for new ambulancesSite selection for new ambulances• Testing new management models• Transfer of methods and validation of assumptions