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ZAMSTAR

ZAMSTAR. Data Management ZAMSTAR: from preparation to using it …. Year 3: Kathy, Nkatya, Ab. Recap: Intervention Data. Virtual Private Network. Source documents at the clinic : TB-register Lab-register VCT-register HH register, HH enrolment logs ECF log sheets TST follow up.

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ZAMSTAR

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

  2. Recap: Intervention Data Virtual Private 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. SOCS: Progress

  8. SOCS: Challenges • Enrolment targets • Number of contacts versus index cases • Quantiferon introduction • Monthly meetings HO with remote data entry staff

  9. Training done • SQL Server, .NET for 2 staff members Zambia, 3 Staff SA • Relational Database Design – Z

  10. 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)

  11. 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

  12. 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

  13. 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

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