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Applications of
Microbiolgical Data
Tim Sandle
Microbiology information
resource:
http://www.pharmamicroresources.com/
Introduction
 Distribution of microbiological data
 Use of trend charts
 Calculation of warning and action
levels
Introduction
 Examples from environmental
monitoring and water testing
 Broad and illustrative overview
 Written paper with more detail
Distribution of microbiological data
 Why study distribution?
• Impact on sampling
• Impact on trending
• Impact upon calculation of warning and
action levels
Distribution
 Most statistical methods are based
on normal distribution, and yet….
 Most microbiological data does NOT
follow normal distribution
Distribution
 Micro-organisms, such as those in a
typical, free-flowing water system,
follow Poisson distribution
 For example…
Distribution
S1 S2 S3
S4 S5
Where S = sample
= micro-organism
Distribution
 And microbial counts tend to be
skewed (or positive or negative
exponential distribution)
 For example, a Water-for-Injection
system…
Distribution
Typical distribution of micro-organisms in WFI
0
50
100
150
200
250
300
350
1 2 3 4 5 6 7
Count (cfu / 100 ml)
Numberofsamples
Distribution
 So, what can we do about it?
Skewed question mark
Distribution
 Well:
a) Use complex calculations and
Poisson distribution tables, or
b) Attempt to transform then data
 We’ll go for the second option
Distribution
 A general rule is:
• For low count data e.g. Grade A
monitoring and WFI systems, take the
square root
• For higher count data, e.g. Grade C and
D environmental monitoring or a
purified water system, convert the data
into logarithms
Distribution
 For example, some counts from a
WFI system:
Distribution
 When the data is examined for its
distribution, using a simple ’blob’
chart:
CI for Mean
0 2 4 6 8
Count
Distribution
 Whereas if the square root is taken:
Distribution
 We move closer to normal
distribution:
CI for Mean
0 0.5 1 1.5 2 2.5 3
Count
Distribution
 Logarithms work in a similar way for
higher counts
 Remember to add ‘+1’ to zero counts
(and therefore, +1 to all counts)
Trend Analysis
 There is no right or wrong approach
 There are competing systems
 This presentation focuses on two
approaches, both described as
‘control charts’:
• The cumulative sum chart
• The Shewhart chart
Trend Analysis
 Control charts form part of the
quality system
 They can be used to show:
• Excessive variations in the data
• How variations change with time
• Variations that are ‘normally’ expected
• Variations that are unexpected, i.e.
something unusual has happened
Trend Analysis
 Control charts need:
• A target value, e.g. last year’s average
• Monitoring limits:
 Upper limit
 Lower limit
 Control line / mean
 So the data can be monitored over time and
in relation to these limits
Trend Analysis
 Of these,
• The warning limit is calculated to represent a
2.5% chance
• The action level is calculated to represent a
0.1% chance
• So, if set properly, most data should remain
below these limits
• These assumptions are based on NORMAL
DISTRIBUTION
• Various formula can be used to set these or
validated software
Trend Analysis
 Cumulative sum chart (cusum)
• Suitable for large quantities of low count
data. It is very sensitive to small shifts
• Shows shifts in the process mean
 Shewhart chart
• Suitable for higher count data. It shows
large changes more quickly.
Trend Analysis
 Cusums
• Harder to interpret
• Displays the cumulative sum of a rolling
average of three values and plots these
in comparison with the target value
• The direction and steepness of the slope
are important
• Significant changes are called ‘steps’
• V-masks can be used as a prediction to
the future direction
Trend Analysis
 For example, a Grade B cleanroom
 Contact (RODAC) plates are
examined
 A target of 0.2 cfu has been used,
based on data from the previous
year
Trend Analysis
Trend Analysis
 Shewhart charts
• Powerful for distinguishing between
special causes and common causes
• Common causes are inherent to the
process and are long-term
• Special causes are where something has
changed and maybe of a long or short
term
Trend Analysis
 Examples of special causes:
• a) A certain process
• b) A certain outlet
• c) A certain method of sanitisation, etc.
• d) Sampling technique
• e) Equipment malfunction e.g. pumps, UV
lamps
• f) Cross contamination in laboratory
• g) Engineering work
• h) Sanitisation frequencies
Trend Analysis
 For example, a Grade C cleanroom
• Active air-samples are examined
• A target of 1.5, based on historical data
Trend Analysis
Trend Analysis
 The previous charts were prepared
using a statistical software package
 However, MS Excel can also be used
 The next example is of a WFI system
 Notice the data has been converted
by taking the square root of each
value
Trend Analysis
Trend of WFI System over 62 weeks with trend line
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
Number of weeks
Sqrootofmeancount/
week
Trend Analysis
 Alternatives:
• Individual Value / Moving Range charts
• Exponentially Weighted Moving Average
charts (EWMA)
• These are useful where counts are NOT
expected, e.g. Grade A environments
• They look at the frequency of intervals
between counts
Trend Analysis
 Summary
Chart Type Advantage Disadvantage
Cumulative sum Cusum charts are more
sensitive to small process
shifts.
Large,
abrupt shifts are not
detected as fast as in a
Shewhart chart.
Shewhart chart Systematic shifts are
easily detected.
The probability of
detecting small shifts fast
is rather small
Limits
 Alert and action levels
 Based on PDA Tech. Report 13 (2001):
• Alert level: a level, when exceeded, indicates
that the process may have drifted from its
normal operating condition. This does not
necessarily warrant corrective action but
should be noted by the user.
• Action level: a level, when exceeded, indicates
that the process has drifted from its normal
operating range. This requires a documented
investigation and corrective action.
Limits
 Why use them?
• Assess any risk (which can be
defined as low, medium or high)
• To propose any corrective action
• To propose any preventative action
Limits
 “Level” is preferable to “Limit”
 Limits apply to specifications e.g.
sterility test
 Levels are used for environmental
monitoring
Limits
 Regulators set ‘guidance’ values e.g.
EU GMP; USP <1116>; FDA (2004)
 These apply to new facilities
 User is expected to set their own
based on historical data
• Not to exceed the published values
• Many references stating this
• Views of MHRA and FDA
Limits
 Things to consider:
• The length of time that the facility has been in
use for
• How often the user intends to use the limits for
(i.e. when the user intends to re-assess or re-
calculate the limits. Is this yearly? Two yearly?
And so on).
• Custom and practice in the user’s organisation
(e.g. is there a preferred statistical technique?)
• They be calculated from an historical analysis
of data.
• Uses a statistical technique.
Limits
 Historical data
• Aim for a minimum of 100 results
• Ideally one year, to account for
seasonal variations
Limits
 Statistical methods:
• Percentile cut-off
• Normal distribution
• Exponential distribution
• Non-parametric tolerance limits
• Weibull distribution
Recommended by PDA Technical
Report, No. 13
Limits
 Assumptions:
a) The previous period was ‘normal’
and that future excursions above the
limits are deviations from the norm
b) Outliers have been accounted for
Limits
 Percentile cut-off
• Good for low count data
• May need to use frequency tables
• May need to round up or down to
nearest whole zero or five
• Warning level = 90th or 95th
• Action level = 95th or 99th
Limits
 Percentile cut-off
• Data is collected, sorted and ranked
 90th percentile means that any future result
that exceeds this is 90% higher than all of
the results obtained over the previous year.
• Refer to PharMIG News Number 3
(2000) for excellent examples.
Limits
 Normal distribution
• Can only be used on data that is
normally distributed!
• Could transform data but inaccuracies
can creep in
• Most data will be one-tailed, therefore
need to adjust 2nd and 3rd standard
deviation
 Warning level = 1.645 + the mean
 Action level = 2.326 + the mean
Limits
 Negative exponential distribution
• Suitable for higher count data
• Warning level: 3.0 x mean
• Action level: 4.6 x mean
Limits
 For all, do a ‘sore thumb’ activity by
comparing to a histogram of the data
 Does it feel right?
Conclusion
 We have looked at:
• Distribution of microbiological data
• Trending
 Cusum charts
 Shewhart charts
• Setting warning and action levels
 Percentile cut-off
 Normal distribution approach
 Negative exponential approach
Conclusion
 Key points:
• Most micro-organisms and microbial
counts do not follow normal distribution
• Data can be transformed
• Inspectors expect some trending and
user defined monitoring levels
• Don’t forget to be professional
microbiologists – it isn’t all numbers!
Just a thought…
 This has been a broad over-view
 If there is merit in a more ‘hands on’
training course, please indicate on
your post-conference questionnaires.
Thank you

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Application of microbiological data

  • 1. Applications of Microbiolgical Data Tim Sandle Microbiology information resource: http://www.pharmamicroresources.com/
  • 2. Introduction  Distribution of microbiological data  Use of trend charts  Calculation of warning and action levels
  • 3. Introduction  Examples from environmental monitoring and water testing  Broad and illustrative overview  Written paper with more detail
  • 4. Distribution of microbiological data  Why study distribution? • Impact on sampling • Impact on trending • Impact upon calculation of warning and action levels
  • 5. Distribution  Most statistical methods are based on normal distribution, and yet….  Most microbiological data does NOT follow normal distribution
  • 6. Distribution  Micro-organisms, such as those in a typical, free-flowing water system, follow Poisson distribution  For example…
  • 7. Distribution S1 S2 S3 S4 S5 Where S = sample = micro-organism
  • 8. Distribution  And microbial counts tend to be skewed (or positive or negative exponential distribution)  For example, a Water-for-Injection system…
  • 9. Distribution Typical distribution of micro-organisms in WFI 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 Count (cfu / 100 ml) Numberofsamples
  • 10. Distribution  So, what can we do about it? Skewed question mark
  • 11. Distribution  Well: a) Use complex calculations and Poisson distribution tables, or b) Attempt to transform then data  We’ll go for the second option
  • 12. Distribution  A general rule is: • For low count data e.g. Grade A monitoring and WFI systems, take the square root • For higher count data, e.g. Grade C and D environmental monitoring or a purified water system, convert the data into logarithms
  • 13. Distribution  For example, some counts from a WFI system:
  • 14. Distribution  When the data is examined for its distribution, using a simple ’blob’ chart: CI for Mean 0 2 4 6 8 Count
  • 15. Distribution  Whereas if the square root is taken:
  • 16. Distribution  We move closer to normal distribution: CI for Mean 0 0.5 1 1.5 2 2.5 3 Count
  • 17. Distribution  Logarithms work in a similar way for higher counts  Remember to add ‘+1’ to zero counts (and therefore, +1 to all counts)
  • 18. Trend Analysis  There is no right or wrong approach  There are competing systems  This presentation focuses on two approaches, both described as ‘control charts’: • The cumulative sum chart • The Shewhart chart
  • 19. Trend Analysis  Control charts form part of the quality system  They can be used to show: • Excessive variations in the data • How variations change with time • Variations that are ‘normally’ expected • Variations that are unexpected, i.e. something unusual has happened
  • 20. Trend Analysis  Control charts need: • A target value, e.g. last year’s average • Monitoring limits:  Upper limit  Lower limit  Control line / mean  So the data can be monitored over time and in relation to these limits
  • 21. Trend Analysis  Of these, • The warning limit is calculated to represent a 2.5% chance • The action level is calculated to represent a 0.1% chance • So, if set properly, most data should remain below these limits • These assumptions are based on NORMAL DISTRIBUTION • Various formula can be used to set these or validated software
  • 22. Trend Analysis  Cumulative sum chart (cusum) • Suitable for large quantities of low count data. It is very sensitive to small shifts • Shows shifts in the process mean  Shewhart chart • Suitable for higher count data. It shows large changes more quickly.
  • 23. Trend Analysis  Cusums • Harder to interpret • Displays the cumulative sum of a rolling average of three values and plots these in comparison with the target value • The direction and steepness of the slope are important • Significant changes are called ‘steps’ • V-masks can be used as a prediction to the future direction
  • 24. Trend Analysis  For example, a Grade B cleanroom  Contact (RODAC) plates are examined  A target of 0.2 cfu has been used, based on data from the previous year
  • 26. Trend Analysis  Shewhart charts • Powerful for distinguishing between special causes and common causes • Common causes are inherent to the process and are long-term • Special causes are where something has changed and maybe of a long or short term
  • 27. Trend Analysis  Examples of special causes: • a) A certain process • b) A certain outlet • c) A certain method of sanitisation, etc. • d) Sampling technique • e) Equipment malfunction e.g. pumps, UV lamps • f) Cross contamination in laboratory • g) Engineering work • h) Sanitisation frequencies
  • 28. Trend Analysis  For example, a Grade C cleanroom • Active air-samples are examined • A target of 1.5, based on historical data
  • 30. Trend Analysis  The previous charts were prepared using a statistical software package  However, MS Excel can also be used  The next example is of a WFI system  Notice the data has been converted by taking the square root of each value
  • 31. Trend Analysis Trend of WFI System over 62 weeks with trend line -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 Number of weeks Sqrootofmeancount/ week
  • 32. Trend Analysis  Alternatives: • Individual Value / Moving Range charts • Exponentially Weighted Moving Average charts (EWMA) • These are useful where counts are NOT expected, e.g. Grade A environments • They look at the frequency of intervals between counts
  • 33. Trend Analysis  Summary Chart Type Advantage Disadvantage Cumulative sum Cusum charts are more sensitive to small process shifts. Large, abrupt shifts are not detected as fast as in a Shewhart chart. Shewhart chart Systematic shifts are easily detected. The probability of detecting small shifts fast is rather small
  • 34. Limits  Alert and action levels  Based on PDA Tech. Report 13 (2001): • Alert level: a level, when exceeded, indicates that the process may have drifted from its normal operating condition. This does not necessarily warrant corrective action but should be noted by the user. • Action level: a level, when exceeded, indicates that the process has drifted from its normal operating range. This requires a documented investigation and corrective action.
  • 35. Limits  Why use them? • Assess any risk (which can be defined as low, medium or high) • To propose any corrective action • To propose any preventative action
  • 36. Limits  “Level” is preferable to “Limit”  Limits apply to specifications e.g. sterility test  Levels are used for environmental monitoring
  • 37. Limits  Regulators set ‘guidance’ values e.g. EU GMP; USP <1116>; FDA (2004)  These apply to new facilities  User is expected to set their own based on historical data • Not to exceed the published values • Many references stating this • Views of MHRA and FDA
  • 38. Limits  Things to consider: • The length of time that the facility has been in use for • How often the user intends to use the limits for (i.e. when the user intends to re-assess or re- calculate the limits. Is this yearly? Two yearly? And so on). • Custom and practice in the user’s organisation (e.g. is there a preferred statistical technique?) • They be calculated from an historical analysis of data. • Uses a statistical technique.
  • 39. Limits  Historical data • Aim for a minimum of 100 results • Ideally one year, to account for seasonal variations
  • 40. Limits  Statistical methods: • Percentile cut-off • Normal distribution • Exponential distribution • Non-parametric tolerance limits • Weibull distribution Recommended by PDA Technical Report, No. 13
  • 41. Limits  Assumptions: a) The previous period was ‘normal’ and that future excursions above the limits are deviations from the norm b) Outliers have been accounted for
  • 42. Limits  Percentile cut-off • Good for low count data • May need to use frequency tables • May need to round up or down to nearest whole zero or five • Warning level = 90th or 95th • Action level = 95th or 99th
  • 43. Limits  Percentile cut-off • Data is collected, sorted and ranked  90th percentile means that any future result that exceeds this is 90% higher than all of the results obtained over the previous year. • Refer to PharMIG News Number 3 (2000) for excellent examples.
  • 44. Limits  Normal distribution • Can only be used on data that is normally distributed! • Could transform data but inaccuracies can creep in • Most data will be one-tailed, therefore need to adjust 2nd and 3rd standard deviation  Warning level = 1.645 + the mean  Action level = 2.326 + the mean
  • 45. Limits  Negative exponential distribution • Suitable for higher count data • Warning level: 3.0 x mean • Action level: 4.6 x mean
  • 46. Limits  For all, do a ‘sore thumb’ activity by comparing to a histogram of the data  Does it feel right?
  • 47. Conclusion  We have looked at: • Distribution of microbiological data • Trending  Cusum charts  Shewhart charts • Setting warning and action levels  Percentile cut-off  Normal distribution approach  Negative exponential approach
  • 48. Conclusion  Key points: • Most micro-organisms and microbial counts do not follow normal distribution • Data can be transformed • Inspectors expect some trending and user defined monitoring levels • Don’t forget to be professional microbiologists – it isn’t all numbers!
  • 49. Just a thought…  This has been a broad over-view  If there is merit in a more ‘hands on’ training course, please indicate on your post-conference questionnaires.