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

Recap: Intervention Data Central Database Data 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 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 Z- TB 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: 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