Registers and CentralisedReporting

SAHIVCS Conference 2018

Ruth Lancaster – NDoH

Herbert Musariri - CHAI

Registers and CentralisedReporting

SAHIVCS Conference 2018

Ruth Lancaster – NDoH

Herbert Musariri - CHAI

PROBLEM

BACKGROUND

• 3rd line ART • Started 2013• Defined mandate• Centralised (-WC)• Algorithm-based decision making

• DRV; DTG; ETR• Captured on an Excel spreadsheet (“3rd line

database”)

BACKGROUND

• DTG: HP13 Supplementary Contract (2017)

DATABASES

• 3RD line database• 1 row = 1 application

0

100

200

300

400

500

600

2013 2014 2015 2016 2017

DRV DTG

RSA Pharma database

PROCUREMENT

0

200

400

600

800

1000

1200

1400

1600

Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18

Dolutegravir 50mg tablets 30 tablets

Drug consumptionPatient level data

DATABASES

• 3RD line database• 1 row = 1 application• Non-3rd line mandate DTG use• Patient referrals

DATABASES

Baseline – Jan-Feb 2016Intervention 1 – Mar-Dec 2016Intervention 2 – Jan-Apr 2017

Steegan. Active engagement between laboratory and

clinicians improves linkage to third-line antiretroviral

treatment

• 3RD line database• 1 row = 1 application• Non-3rd line mandate DTG use• Patient referrals

DATABASES

Sheik. A third-line ART referral process in the

Western Cape Province , South Africa: Estimating

qualification and predictors of referral

Referral criteria (adults):• > 15 y• PI > 2 y• VL not suppressed x 3

Met criteria for referral

N=947

Actually referred

N=167

Met criteria and not referredN=905

Met criteria and referred

N=42

• 3RD line database• 1 row = 1 application• Non-3rd line mandate DTG use• Patient referrals

DATABASES

• RSA Pharma database• Contract items• Procurement level data• Data inputted by suppliers• Monthly trends (orders/deliveries)

METHODOLOGY

ATC/DDD

Data smoothing

Growth estimation

METHODOLOGY

• Manually Fit Linear Growth Models (proForecaster)

ATC/DDD

Data smoothing

Growth estimation

0

200

400

600

800

1,000

1,200

1,400

1,600

Dolutegravir 50mg tablets 30 tablets

Observations Adjusted consumption

RSA Pharma database

METHODOLOGY

• Defined Daily Dose:

ATC/DDD

Data smoothing

Growth estimation

Assumed average maintenance dose per day

for a medicine used for its main indication in

Adults

Uses:

• Technical measurement that allows

measurement and comparison of volume of

medicine use

• Rough estimate of consumption, not an

exact picture of actual use

ATC StrengthStrength

UnitPackSize

Pack SizeUnit

DDDDDDUnit

Prescriptiondays/month

Dolutegravir 50mg tablets 30 tablets

J05AX12 50 mg 30 tablet 50 mg 30

METHODOLOGY

ATC/DDD

Data smoothing

Growth estimation

0

200

400

600

800

1,000

1,200

1,400

1,600

Observations Adjusted consumption Patient numbers per month based on DDD

• Estimated patient numbers(linear growth)

RESULTS

0

200

400

600

800

1,000

1,200

1,400

Patient numbers

Dolutegravir-Consumption Data (-WC) Dolutegravir 3L Database (-WC)

Dolutegravir-Consumption Data (+WC)

Patient growth estimation

HP13 Supplementary

Contract

RSA Pharma database launched

RALTEGRAVIR?

0

200

400

600

800

1000

1200

1400

Patient numbers

Dolutegravir (Consumption Data) Dolutegravir (3L Database)

Raltegravir (Consumption Data) Raltegravir (3L Database)

RSA Pharma database launched

HP13 Supplementary

Contract

Patient growth estimation

RESULTS (RAL DTG)

0

200

400

600

800

1000

1200

RAL --> DTG Switch Rate 10%

Dolutegravir (3L Database) Raltegravir (3L Database)

CONCLUSION

Herbert Musariri (Clinton Health Access Initiative)

CO-AUTHOR