Data Management ZAMSTAR: from preparation to using it … Year 3: Kathy, Nkatya, Ab ZAMSTAR.

Data Management Data Management ZAMSTAR: ZAMSTAR:

from preparation to using it from preparation to using it ……

Year 3: Kathy, Nkatya, AbYear 3: Kathy, Nkatya, Ab

ZAMSTARZAMSTAR

Recap: Intervention DataRecap: Intervention Data

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World Wide Web

Central DatabaseData entry remote

Source documents at the clinic :

•TB-register

•Lab-register

•VCT-register

•HH register, HH enrolment logs

•ECF log sheets

•TST follow up

Virtual Private Network

Recap: Intervention dataRecap: 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 dataProgress: 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: ChallengesProgress: 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 Z-TB register results

Permanent hardware problems remote sites

SOCS: characteristicsSOCS: characteristicsSecondary 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: ProgressSOCS: ProgressSOCS enrollment september - june 2007Community Number of TB

case households

who consented to the study (%)

(2)

Number of adults who

consented to the

study(3)

1. Chawama 175 314 1.82. Chifubu 72 137 1.93. Chimwemwe 82 155 1.94. Chipata 82 136 1.75. Chipulukusu 60 92 1.56. George 89 156 1.87. Kanyama 167 230 1.48. Maramba 63 154 2.49. Dambwa 28 51 1.810. Makululu 63 162 2.611. Mansa Central 52 124 2.412. Ndeke 32 70 2.213. Ngungu 61 136 2.214. Pemba 26 104 4.015. Senema 71 104 1.516. Shampande 49 102 2.1

Reflect socs db on 04/09/2007SOCS enrollment september - june 2007Community Number of TB

case households

who consented to the study (%)

(2)

Number of adults who

consented to the

study(3)

55 154 34 0.263 56 102 1.857 80 116 1.561 135 265 2.051 129 58 0.460 169 308 1.8

SOCS: ChallengesSOCS: Challenges

• Enrolment targets

• Number of contacts versus index cases

• Quantiferon introduction• Monthly meetings HO with remote data entry

staff

Training doneTraining done

• SQL Server, .NET for 2 staff members Zambia, 3 Staff SA

• Relational Database Design – Z

Training plannedTraining 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) …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 prevalenceTB 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

AnalysisAnalysis

• 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