Transcript Presentation Title

Measuring and assessing adverse events
rates for male circumcision clients in
Swaziland
Louis Apicella1, Khumbulani Moyo2, Gladys Magongo2, Jessica Greene2,
Paul C. Hewett1
1Population Council, 2Population Services International Swaziland
ICASA
Addis Ababa
December 5, 2011
© 2010 The Population Council, Inc.
Background
Male circumcision (MC) shown to reduce HIV infection (F to M):
• 3 RCTs: (Auvert B, 2005; Bailey R, 2007; Kigozi R, 2007)
– 60% reduction in risk of infection in males
– adverse event (AE) rates 1.5% to 8%
HIV epidemic in Swaziland (basics):
• 26% prevalence of HIV 15-49; 31% Female, 20% Male (DHS, 2006)
• 8% of males circumcised (DHS, 2006)
MC program in Swaziland:
• National program to circumcise 80% of men
• Approximately 22,000 MCs completed through March 2011
• Surgery conducted by MDs (forceps guided and sleeve resection)
Adverse event classification
Timing
Severity
During surgery
Mild
Immediately post-operative
Moderate
Post-operative day 1-6
Severe
Post-operative day > 7
Types of AEs include...
• bleeding/hematoma
• pain or other distress
• wound disruption
• excess swelling
• difficulty urinating
• infection
• excess skin removal/tightness of the skin
• discernible scaring/disfigurement
• sexual dysfunction/sensory changes
PEPFAR Next Generation Indicators Reference Guide, 2009 v1.1
Example of swelling with
underlying hematoma
Adverse Event Action Guide For Male Circumcision 2010
Study rationale
• MC program in Swaziland reported AE rates near 2%
• A significant proportion of these men do not return for follow-up (2010)
– 19% 2-day
– 52% 7-day
• Program AE rate calculation implicitly assumes men who do not return
for follow-up are AE free
MCs
conducted
OBSERVED
Clients Attending
Follow-up visits
AE
Recorded
No AE
NOT OBSERVED
Clients NOT
Attending
Follow-up visits
No AE
AE NOT
Recorded
Study objectives
• Measure AEs rates for a full population of clients (observed
& usually not observed)
• Compare program AE rates with study AE rates
• Assess risk factors for returning for follow-up visits
• Assess risk factors for AEs
Study design
• Study conducted Oct 2010 – Feb 2011
• MC sites contributed at least 5% of MCs Jan – July 2010
• MC clients aged 15+ from fixed and mobile outreach
• Baseline interview prior to surgery
• Those who returned for 7-day visit: “Passively followed”
were interviewed and clinically examined
• Those who did not return for 7-day visit were contacted and
considered “Actively followed”
• All interviews were conducted prior to the physical exam
• AEs tracked using program’s AE reporting system
Study sample
Baseline sample
Returned at 7 days “Passive”
%
N
-
616
36%
219
“Actively followed”
= 501
Reminder call
23%
139
Called and transport assisted
23%
143
2%
11
17%
104
Called and home visit
Lost to follow-up
= 115
Significant risk factors for being actively followed
Region of residence: Lubombo
Odds Ratios
2.4 (1.3-4.5)**
MC service site: Outreach
1.9 (1.1-3.3)*
Education: University
0.3 (0.1-0.9)*
Currently not working:
1.7 (1.1-2.6)**
Perceived low/no risk of HIV:
1.4 (1.0-2.1) †
† p < .10, * p < .05, ** p<.01
Logistic model (passive vs. active) included:
region of residence, type of service site (fixed/outreach), currently in school (no/yes), education level,
(none/primary, form 1-3, form 4-5, trade, university) currently working, (working/not working),
perceived access to clinic (easy/difficult), perceived risk of HIV infection (mod/high/DK vs. none/low)
Moderate and severe AE rates
Program calculated AE rate: AEPr = Passive / All MCs
AEs/MCs
AE Rate (%)
Oct ’10 – Feb’11
24/1,654
1.5
Study client subset
17/616
2.8*
Study AE rate: AES = Passive + Active / All MCs
AEs/MCs
AE Rate (%)
AEpassive
17/219
7.8
AEActive
7/397
1.8
AETotal
24/616
3.9**
Study AE rate: AES = Passive + Active / Observed Men
AEs/MCs
AE Rate (%)
4.8**
AEobserved
24/501
†
p < .10, * p < .05, ** p<.01
Recommendations
Improve follow-up rates: phone calls, SMS, transport
•Private public/partnership: vouchers for transport for clients
Improve training and procedures: ensuring consistent and
accurate recording of AEs
• Incomplete client record forms and broken linkages between client
record databases could be improved
• Subjective prescription of antibiotics for prophylactic purposes may
cause misclassification of infection AEs
Quality Assurance: assess pharmaceutical records and
emergency call logs for evidence of missed AEs
Reporting of AEs:
• How should programs report AEs with large lost to follow-up?
• Should they be reported as a range with different denominators?
• What about longer term AEs that are observed after 7 days?
Siyabonga Thank you Amesegënallô
Acknowledgements:
Colleagues at PSI Swaziland, Marie Stopes Swaziland, Jhpiego
Ministry of Health and Social Welfare, Kingdom of Swaziland
People of the Kingdom of Swaziland
Study Team:
Alfred Adams, Abiodun Lamina, Mdudzi Cele, Linganiso Mayimbela,
Bhekisisa Bulunga, Khethuxolo Mngomezulu, Mathokoza Sibandze,
Vumile Mdluli, Musawenkhosi Colani Mamba, Mxolisi Masilela, and
Sifiso Dlamini