Optimizing sample transportation to support HIV Programme Implementation and Scaling up in Zambia

ESRI User Conference Drakensberg : 17 - 19 October 2017

Mittah Raditlhalo, Sarah Girdwood, Thomas Crompton, Gareth Jones

Brooke Nichols PhD, Dorman Chimhamhiwa PhD

Presentation Outline

1. Introduction and Background

2. Scope of work and Problem Statement

3. Data collection

4. Data Analysis Approach and Results

5. Conclusion

• An NPO working in public health sector, especially HIV/AIDS

• RTC through EQUIP, provides support in 17 PEPFAR countries with over 300 technical experts on the ground.

• Supporting HIV programme implementation in 12 African Countries

• GIS is an integral part of RTC health care programming and decision making

• GIS activities providing key programmatic implementation support in Malawi, Zambia, Indonesia, Burundi, S. Africa

Introduction & Background : Right to Care

In 2016

• 36.7 million people globally were living with HIV;

• 19.5 million were accessing antiretroviral therapy.

• 1.8 million people became newly infected with HIV;

• 1 million people died from AIDS-related illnesses.

HIV & AIDS Facts : UNAIDS (2017)

35.0 million have died

from HIV /Aids since the start

76.1 million infected

since the start of the HIV epidemic

1. UNAIDS has set a 90 - 90 - 90 goal for 2020,✓ 90% of people living with HIV to know their status

✓ 90% of people diagnosed as positive to be on ART and care

✓ 90% of people receiving treatment to be virally suppressed (reduced viral load to an undetectable level)

2. Viral Load Testing• WHO recommends HIV Viral Load (VL) testing to monitor success

of ART through viral load suppression

• Testing at 6 months then after every 12 months

UNAIDS 90:90:90 A Bold Fast-Track Strategy

1. Country Profile• Population 16.4m

✓ 42% Urban; 58% Rural

• 10 Provinces

• 104 + districts

2. HIV in Zambia• 1.2 million people living with HIV

• Only 67% of the 1.2m know their status (UNAIDS Zambia, 2017)

• Challenges with achieving the 3rd 90 (viral Suppression)

• VL testing currently done at 13 centralised labs

• Whole blood preferred option for VL testing

HIV/AIDS in Zambia

Scaling Up Viral Load Implementation in Zambia: The challenge

2015 2016 2017 2018 2019 2020

Total number of patients on ART

Total viral load testing volumes

787,840

8,548

869,732

105,000

954,144

453,492

1,041,617

745,118

1,132,699

974,720

1,227,953

1,232,533

% of patients accessing viral load 5% 10% 38% 57% 69% 80%

Source: Viral Load Scale Up Implementation plan Zambia (2015)

Zambia aims to scale up HIV treatment programmes and simultaneously increase VL testing volumes from 10% in 2016 to 80% by 2020

We are here We want to get here in 3 years

• In addition to the typical clinical and resources (financial & human) challenges; planning for VL scale up has been proved to be daunting.• no nationwide sample transport network exists.

• Government and implementing partners responsible for arranging own transportation when transport is available.

• Operational area (district) based planning instead of national

• This results in erratic and unreliable sample delivery, and the inability for health facilities to plan blood draws for viral load.

Solution

• RTC’s GIS team tasked to provide planning support and assist with analytics, geo based prioritization and targeting.

• a reliable nationwide systematic sample transport network needs to be built

Challenges

Objectives

RTC GIS unit (in collaboration with HE2RO) supports the GOZ in scaling up the Viral Load Implementation Plan by:

Assisting with development of a GIS based national specimen transport network system to aid coordinated implementation of VLSU by government and implementing partners.

• Providing GIS based guidance on placement of new VL instruments.

• Conducting vehicle routing to determine optimized transport routes

• VL costing based on material costs, equipment and staffing costs (led by HERO)

GIS Support for Viral Load Scale Up

Data Collection

Data Collection : Tools & Architecture

Tablet

ArcGIS Online Cloud Based

ArcGIS Desktop

Field workers

Data Quality

Team Leader

Field Teams

MoH Lead Contact

Tablet

• Real Time• Mobile Application based• Online/Offline

synchronization

Back endFront end

Portal for ArcGIS Server

GIS teams collect:

• GPS Coordinates of health facilities

• viral load sample transportation and collections information.

• infrastructure and communication services availability at each facility

• 2534 plus facilities in the country mapped.

• 9 vehicles, each with GPS tracking devices,

• Data was captured using the Survey 123 app deployed on our android devices

• in-country field staff used to capture site level data

• GIS Mapping was done in phases,

• some facilities were mapped during the wet season, and those that were inaccessible were mapped during the dry season

• 655 laboratories assessed by laboratory teams

• Road data collected and analysed in collaboration with Tracks4Africa

Data Collection - Field Results

Analytical Approach

Sample Transportation Analysis: Conceptual Approach

• Developed a geospatial model for the optimisation of a national sample transportation system

• Data used• projected 2020 viral load volumes • facility locations• lab infrastructure • Cost data • Zambian road network data

• Vehicle routing model aimed to maximize coverage• Taking distance, drive time & anticipated 2020 viral load volumes into

account

Two routing approaches

Daily sample transportation from high volume facilities (and transport centres) to PCR labs

1Weekly sample transportation from low volume facilities to transport centres

2

Lab

Hub

Facility

Hub

Labs

• Sample transport centres were chosen aiming to maximise coverage, taking into account sample volumes at other facilities; and within 2hours drive time of transport the centre.

• Vehicle routing done using Network Analyst, taking into account constraints• Inputs: sample volumes, distance from lab/ transport

centre to facility, drive time• Constraints: considering service time & driver working

hours

• To reach 2020 target, 33500 VL will have to be done weekly across 1591 ART treating facilities in the country

• 174 of the facilities are considered high volume sites • represent 75% of total volume

• 152 of the 174 can be reached daily

Daily routesVehicles/ motorbikes assigned to identified transport centre

High volume sites

Weekly routesVehicles/ motorbikes assigned to identified transport centre

Low volume sites

Vehicle Routing problem

Inputs: Centralised Labs

Samples routed to 13 labs

Inputs: Transport centres

Transport centres act as a central point where samples

are aggregated from surrounding low volume

facilities.

Results

Results Provincial Network

National results

Routing 1High volume

Routing 2Low volume Total % of total

Number of facilities 152 648 800 53%

Total Weekly VL 22 993 6849 29 842 91%

Total Weekly KM 37 877 22 798 60 675

Number of vehicles 29 28 57

Number of Motorbikes 12 2 14

KM/VL 1.6 3.33

Daily sample transportation from high volume facilities (and transport centres) to labs

Weekly sample transportation from low volume facilities to transport centres

70% 21%

14

57

91% 2020 viral load volume reached

$1.80Cost per sample transported

$2.2mOnce-off start up costs

$2.8mAnnual running costs

Cost of national optimisation versus district-based sample transportation

$0

$1,000,000

$2,000,000

$3,000,000

$4,000,000

$5,000,000

$6,000,000

$7,000,000

District Borderless

Year

ly c

ost

, Do

llar

$

Sample transport level of organization

Motorbike purchasing*

Additional lab personnel

Vehicle purchasing*

Driver salaries and perdiems

Recurringfuel/maintenance

$1.80vs$4.02

Save 55%

Placement of Additional Machines

•User friendly mobile application

• Secure online platform

•Real time data for easy monitoring of fieldwork activities

•Platform offers various analysis tools that have made it easy for us to present data and give health solutions

•Cost saving, use of inhouse resources

Benefits of using Esri’s products

•GIS is proving to be a tool of choice even in the NPO environment.

•Better planning with limited resources

• Targeting and prioritization

• Improved results

Conclusion

Thank You