Transcript zamstar - CREATE Biostats Core

ZAMSTAR
Data Management
ZAMSTAR:
from preparation to using it
…
Year 3: Kathy, Nkatya, Ab
Recap: Intervention Data
Virtual Private
Wide Web
World
Network
Source documents at
the clinic :
•TB-register
•Lab-register
•VCT-register
•HH register, HH enrolment
logs
•ECF log sheets
•TST follow up
Central Database
Data entry remote
Recap: Intervention data
 Characteristics
▪
▪
▪
▪
▪
VPN
Central SQL Server database
Web-based application: ASP.NET
Single data entry
Quality control: manual checking DB versus
source documents by ‘third’ person
Progress: Intervention data
 Progress Z+SA:
▪ TB register data 2005-june 2007: 34,000
records of TB-patients
▪ Lab-register june 2006-june 2007: 55,000
sputum lab results
▪ ECF-data: name, age , sex sputum results of
4,300 participants
▪ HH-register: data entry about to start
▪ Report functionality: Team leaders can
generate overview of ‘their’ entered data
Progress: Challenges
 Quality of record keeping
▪ Filling in records is difficult: re-training and
continuous collaboration between data team
– intervention team
▪ Interpretation of NHLS result recording vs ZTB register results
 Permanent hardware problems remote
sites
SOCS: characteristics
Secondary Outcome Cohort:
• 150 HH, 350 adults (200 contacts), 150 children
per community
• Cumulative HIV incidence, TB incidence, TB
infection incidence in children < 5
• 3 visits: 0, 18 and 36 months
Data capturing:
• Data handling centralized: paper forms
prepared, blood samples and forms reception
• SQL Server Database, VB.NET
• Dual data entry
SOCS: Progress
SOCS enrollment september - june 2007
Com m unity
1. Chawama
2. Chifubu
3. Chimwemwe
4. Chipata
5. Chipulukusu
6. George
7. Kanyama
8. Maramba
9. Dambwa
10. Makululu
11. Mansa Central
12. Ndeke
13. Ngungu
14. Pemba
15. Senema
16. Shampande
Reflect socs db on 04/09/2007
SOCS enrollment september - june 2007
Num ber of TB Num ber of
case
adults
households
w ho
w ho
consented
consented to
to the
the study (%) study(3)
(2)
175
72
82
82
60
89
167
63
28
63
52
32
61
26
71
49
314
137
155
136
92
156
230
154
51
162
124
70
136
104
104
102
Com m unity
1.8
1.9
1.9
1.7
1.5
1.8
1.4
2.4
1.8
2.6
2.4
2.2
2.2
4.0
1.5
2.1
Num ber of TB Num ber of
case
adults
households
w ho
w ho
consented
consented to
to the
the study (%) study(3)
(2)
55
63
57
61
51
60
154
56
80
135
129
169
34
102
116
265
58
308
0.2
1.8
1.5
2.0
0.4
1.8
SOCS: Challenges
• Enrolment targets
• Number of contacts versus index cases
• Quantiferon introduction
• Monthly meetings HO with remote data entry
staff
Training done
• SQL Server, .NET for 2 staff members
Zambia, 3 Staff SA
• Relational Database Design – Z
Training planned
• MS-Access hands-on for data staff (5 days)
• Structured query language for data staff (2
days)
• Biostats – Stata for Intervention Team Leaders
and scientific staff Zambart, UNZA students (5
days)
• SQL Server and .NET for 2 data staff
(outsourced)
• Web design (2-3 staff members)
What do we need (to do) …
• Staff incentives …
• More office space
• GIS:
• Map all communities (main features and
administrative area’s)
• Use satellite images as background
• Map collected research data
• Bill’s visit in november 2007: protocol preparing
• GIS specialist
TB prevalence
• 4 communities
• Enumeration area’s sampled in random
order to reach 5000 samples:
• One community: all ea sampled
• 3 communities app. 50% of the area’s
• All households visited
• Sputum samples collected + questionnaire
• TB-Cases: still pending due to
identification of positive cultures
Analysis
• Risk factor analysis
• Multivariate analysis using socio-demographic (age,
sex), HIV-status, symptoms, previous TB
• Controlling for clustering/sampling:
• Logistic regression cluster option
• GEE
• Svy command
• Risk factors are comparable, p values/standard
error/CI’s vary
• Spatial analysis