Defaulting in OTP:a programme and research

perspectiveBased on Lusaka CTC November 4th, 2009

Abel Irena (M.D, MSc, DTH&M)

Presentation layout

• Objective of the presentation• Background • Implication on programme• Implication on research• Defaulter tracing

Objective

• To share problem of defaulting in Lusaka CTC• To highlight the most common reasons for

defaulting• To discuss the implication of high defaulting in

OTP– A programmatic perspective &– A research perspective

Technical support & investigation on defaulting

2005 2006 2007 2008 2009

June June/July July April + October

Feb

•Based on different studies:QualitativeQuantitative

June- July 2006• Method

– Qualitative• Reason for defaulting

– Intra-city migration– Self discharge

• Minimum 8 weeks– Illness– Labour responsibilities– Distance

• 12 health centres– Lack of understanding of RUTF, OTP

• Defaulter tracing• Done by volunteers

– Limited service availability – Wrong address

No ranking

July 2007• Method

– Qualitative• Reason for defaulting

– Economic reason– Misconceptions regarding the CTC – Unforeseen circumstances

• Illness• Motivating volunteers

– Not paid after being promised– Heavy workload?

• Relieve the role of active case finding & sensitization– Lack of recognition for their work?

• Refresher training– Rejection of children?

No ranking

April 2008

Defaulter Traced dead SAM

57 28 7 6

(49.1%) (25%) (21.5%)

•Based on 8 clinic catchments area survey•Reason for defaulting

•Busy•Stigma•Cause of child’s condition•Distance (did not exceed 45 minutes)•Traditional belief•RUTF (shortage & belief it causes disease)•Sickness •Long duration of stay in programme

Barrier to service access

Lack of knowledge about CTC 57Transfer failure 4Sick carer 2Busy carer 1Mother dead 1

Feb 2009criteria Mean SD min Max

Age (mo) 18 6.7 7 120

MUAC (cm) 11.3 1.4 8 15

Oedema 61.0% had oedema on admission52% had oedema upon defaulting

HIV status 53(60.0%) status not known at time of default12 (34.3%) HIV positive?

LOS Mean (SD)=34 (17.7) days

•Out of 88 defaulters•54 (61%) could not be located•12 (13.6%) moved out•6 (5.7%) died•17 (19.2%) return defaulter/okay

•Mortality =17.1%•Small sample size

Role of volunteers

• Over 400 volunteers• Assist in anthropometry measurement• Assist in active case finding

– when possible (GMP)

• Run OTP in the absence of nurse/nutritionist– Less frequently at the moment

• Absent and defaulter tracing– Has not been consistent

You need me to assist @ the clinic!

Pay me to leave my job!!!!!!!!!

Give me means & I will do it!!!!!!!!!

Bicycle will probably do!

Did we do anything?• Payment per defaulter traced @ the start

– Payment interrupted in late 2007• Secondment of a focal community mobilizer from

LDHMT– Provide with mentoring /training

• Refresher training for volunteers• Case finding through use of child health week• Payment made during defaulter tracing

– Only to few and others still expecting• The seconded person too busy

– 25 health centres to coordinate– Large number of absentees and defaulters to follow– Low motivation of volunteers

Implications on programme

• Won’t meet internationally agreed standards (SPHERE, CTC)

• Criticism on success of CTC

–? Majority die• The million Kwacha Q is@

– Is this defaulter trend normal for urban area?– Is this normal for programme run by Govn’t?– Should we accept and live with it?

Implications on research

• Lost to follow-up (LTFU)– Selection bias

• cohort studies• RCT

• How to manage LTFU– Outcome?– Do they have similar exposure status?

• Implications on publication on a peer reviewed journal

Scenarios for mortality among defaulters

•Under five mortality in Zambia 119/1000•202 children would have died?

•UTH mortality rate: 30-40% •152 children would have died

Criteria Total # Hypothesis

Defaulter 1701 =1701*119/1000 202

Transfer 437 =437*35/100 152

Death 116 =116+152+202 471

Total Exit 5566 =417/5566*100 8.5%