THRio

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• Antonio G F Pacheco

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• Outline
• Database setup
• Creating a master table with main outcomes
• Mortality recovery with linkage
• Issues and differences between units

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Database
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• We needed to evaluate the intervention
• Intervention itself is training professionals and
  facilitate guidelines implementation
• Request TST for eligible patients
• Give IPT for eligible patients
• First approach
• Percentages
• Given eligible patients for TST
• Given eligible patients for IPT
• There are problems with this approach

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• Issues
• There is a lead time between training and
  following guidelines
• Thats variable for each clinic
• Frequency with which patients return to clinic
• Logistic problems within the clinic
• TST is not placed every day
• To start IPT, TB has to be ruled out
• It could take a long time to get a chest X-ray!!!

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• We thought we would have to take time into
  account!
• Instead of percentages, rates
• The process a patient goes through is pretty
  complex
• There are dynamics issues involved
• We tried to understand the dynamics first

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• Understanding the dynamics of patients
• Patients may go through several states
• Events of interest are all dated
• It is possible to calculate transition rates
• It would be useful for process analysis
• Taking time into account
• Lets see it schematically

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Dynamics
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• Main table generated by the system
• Based on the schematic part only
• Takes info from several tables
• Lots of programming involved
• 9 SQL views
• Delphi (Pascal) programming
• gt 1000 lines of code
• Computationally-intensive
• About 40 min in a AMD 2 x 1.6 GHz with 2Gb RAM

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• Other outcomes included
• TB outcomes
• IPT outcomes
• 20 different codes (with dates)
• Long format database
• Lets see an example with some fake data

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• Actually now it is easy to extend it
• Implemented in Python
• Mainly date functions
• Could easily be extended in other languages (e.g.
  SAS)
• Extra info from patients
• HAART
• CD4
• VL
• Extra info from study
• Intervention status

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• Lets see one script

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• Now we can calculate rates
• Can present data as a survival analysis
• Compare pre- and post-intervention
• Calendar x non-calendar analysis
• Dynamics of the study
• Dynamics of the intervention
• Can be presented by clinic as well

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Death Rates
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• Death rates over time in our cohort
• How many deaths are we missing?
• With linkage we are able to improve the numbers
• But how much?
• Is our death rate reasonable?
• Are there differences over time?
• Are there differences across units?

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• Patients known to be dead at data abstraction
• Between Sep 03 and Sep 05
• Abstracted as inactive
• In the beginning not even after Sep 05
• We started recovering them
• Since Sep 03
• No data abstracted if patients did not have a
  visit after Sep 03

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• Problem
• These patients are not included in the analyses
• Potential biases on results
• Linkage with main database would fail
• If we dont even have names or DOBs
• Main biases
• Outcomes unrelated with deaths
• Outcomes associated with deaths
• Death as an outcome

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• Overall death rates
• From Sep 03-Aug 05
• 1.95/100 pys
• From Sep 05-Mar 07
• 3.49/100 pys
• The problem is there is no reason to believe the
  rates are increasing
• If we are missing during the study, it is much
  worse before it began!
• Lets see the rates per year

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• To better understand whats going on
• Rates per 4-month periods from Jan 03-Mar 07
• Number of deaths
• Person-years contribution
• There are at least 3 things to be explained

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• What about differences among units?
• Lets try to see the issues of person-time and
  deaths per units
• Starting with the person-years

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• The mean contribution is lower for half of the
  units
• This is an operational issue of the way data is
  collected in this study
• For the 10th and 11th periods, it doesnt seem
  that bad
• For deaths, if we exclude the 1st, 2nd and last
  periods, we can compare the rates per unit

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• Lets see the death rates
• Excluding the 1st, 2nd periods
• Using 9th, 10th and 11th periods as the standard
  death rate
• Rates and 95 CIs per unit
• A little underestimated
• Lets compare the death rates in the other
  periods per unit
• How it is evolving over time
• 6th and 7th periods problem

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• In fact some units caught up earlier
• Majority did not
• Even the ones that are within the CIs are
  consistently lower than the reference rate
• 7 units have similar rates
• Problem
• Some units remove charts from archives soon after
  the patient is known to be dead
• Lets look at those periods

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• Lets try to see all of them over time

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• So far, it looks a bad idea to use the time
  period before the study began to study mortality
• What could be done to improve that?
• Run linkage with inactive patients
• We wouldnt have all the info
• But could at least learn about vital status
• Would help for Sep 03 to Dec 05

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• What about the mortality after the study began?
• My guess is that we will have about 3.6/100 pys
• Lets see where it comes from

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• Further steps
• Compare that rate with rates in the literature
• Stratify them by HAART use and CD4 counts
• See if rates per stratum are reasonable
• Also compare with other studies