TRADE-OFFS AND CHALLENGES FOR PRODUCT DEVELOPMENT

Transcription

1 TRADE-OFFS AND CHALLENGES FOR PRODUCT DEVELOPMENT J O E B R O W N P H D P E A S S I S T A N T P R O F E S S O R, S C H O O L O F C I V I L & E N V I R O N M E N T A L E N G I N E E R I N G J O E. B R O W C E. G A T E C H. E D U H T T P : / / B R O W N. G A T E C H. E D U

2 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost 8 Be randomly distributed in a given sample (i.e., not clumpy )

3 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost 8 Be randomly distributed in a given sample (i.e., not clumpy )

4 CEBU, PHILLIPINES E. coli/ 100 ml Diarrhoea Cases/ Populaton at Risk (%) <1 104/ / / / > / Source: Moe, C.L., et al. (1991) Bull. WHO 69:

5 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost 8 Be randomly distributed in a given sample (i.e., not clumpy )

6 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost 8 Be randomly distributed in a given sample (i.e., not clumpy )

7 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost? 8 Be randomly distributed in a given sample (i.e., not clumpy )?

8 TRADEOFF 1: IDEAL VERSUS PRACTICAL An ideal indicator should: 1 Be present whenever enteric pathogens are present, ideally in greater quantities for ease of detection and to be conservative in estimates of risk 2 Be absent when enteric pathogens are absent or at levels that pose no increased risk 3 Survive as long or longer than the most environmentally persistent enteric pathogens 4 Not proliferate independently in the environment 5 Be detectible in all types of water that may lead to human exposure 6 Be shed in the feces of species who share fecal oral pathogens with humans 7 Be reliably, rapidly, and unambiguously detectible at low cost? 8 Be randomly distributed in a given sample (i.e., not clumpy )?

9 BUT E. coli is a widely used (and historically useful) fecal indicator & process indicator whose interpretation is well (?) understood Guideline/standard: <1 E. coli in 100 ml sample Risk levels widely used in practice (E. coli per 100 ml) <1 = safe 1 10 = low risk = moderate risk 101+ = high risk Measurement not always straightforward or easy High noise : signal ratio

10 TRADEOFF 2: LIMIT OF DETECTION VERSUS TIME (OR COMPLEXITY) Culture methods can detect a single microbe in 100 ml of water (although they may often not): The Great Plate Count Anomaly H 2 O 2 from phosphate containing agars? VBNC (viable but non-culturable), other factors BUT it takes TIME MANY rapid methods can detect 10s to 100s of E. coli per ml, at least under laboratory conditions Concentrating E. coli to these levels takes time and can be complex: doubling time of E. coli under ideal culture is ~20 minutes Concentrating samples concentrates the noise as well (inhibition)

11 LIMITS OF DETECTION: REVIEW, SEN & ASHBOLT 2011 Limit of detection highly dependent on target & matrix In 1677, van Leeuwenhoek reported: 2,730,000 animalcules in a volume he estimated to be the size of a pea Water is TEEMING with activity

12 TRADEOFF 3: SMALL DATA VERSUS BIG DATA Count accuracy of targeted samples versus spatial-temporal representativeness Different tests for different applications? What is representative? Accurate counts require dilutions, replicates, controlled conditions limits to scale Scalable methods need to be inexpensive, field-deployable, but may come with lower sensitivity / specificity / limitations India has >1,000,000 multi-user water systems/ 600 regulatory labs, 300 with full capacity Many tests > few tests when the goal is characterizing risk at scale?

13 CORROLLARY: HIGH NOISE:SIGNAL RATIO

14 ON SIGNAL VARIABILITY 1: BETWEEN SAMPLES FROM THE SAME TIME/PLACE Coefficient of variation (ratio of standard deviation to mean) of ~33% acceptable for E. faecalis qpcr assay (EPA 2015) E. coli: single-laboratory tests range from 3.3% to 27.3% (Brenner et al. 1993) and 8.6% to 40.6% overall (inter-laboratory, AOAC 1989), using log-transformed data More extreme variability among duplicate samples may be the norm in LMICs Why? Particle association, inherent heterogeneity Microbes are not chemicals and do not tend toward equilibria

15 ON SIGNAL VARIABILITY 2: OVER TIME Orders of magnitude variability, especially in natural waters Re-growth, die-off, contamination events, intermittent service, intermittent treatment

16 A YEAR IN THE LIFE OF A HIGH RISK WATER SUPPLY 1000 E. coli count per liter, assume 1 liter per day Days (one year)

17 VILLAGE A: TAP WATER 2.5 Log 10 E. coli / 100 ml Surveillance point

18 VILLAGE A: TAP WATER 200 Mean E. coli / 100 ml Surveillance point

19 VILLAGE B: TAP WATER 2.5 Log 10 E. coli / 100 ml Surveillance point

20 VILLAGE B: TAP WATER 200 Mean E. coli / 100 ml Surveillance point

21 VILLAGE E: TAP WATER 2.5 Log 10 E. coli / 100 ml Surveillance point

22 VILLAGE E: TAP WATER 500 Mean E. coli / 100 ml Surveillance point

23 ON SIGNAL VARIABILITY 3: SPATIAL High variability across space, even in the same household

24 VILLAGE A: TAP WATER 200 Mean E. coli / 100 ml Surveillance point

25 VILLAGE A: STORED WATER Mean E. coli / 100 ml Surveillance point

26 VILLAGE C: TAP WATER 200 Mean E. coli / 100 ml Surveillance point

27 VILLAGE D: STORED WATER 2000 Mean E. coli / 100 ml Surveillance point

28 SO WE WANT REPRESENTATIVE TESTING DATA AT SCALE?

29 Number of samples per group HOW MANY SAMPLES DO YOU NEED? Coefficient of variation ~ 3; accounting for clustering Log 10 difference between groups

30 LOW-COST TESTS A way to generate big data that can result in accurate estimates of risk across scales? Modeling approaches may be helpful, but come with challenges

31 SUMMARY TRADEOFFS Time (or complexity) versus LOD Small sample sizes under controlled conditions versus big data that may be subject to limitations Ultimately, big data are needed to characterize the extreme variability of this signal across scales

32 THANKS

33 HAAS 1996 Holds for both exponential and linear low-dose doseresponse curves Argues that geometric means that attempt to represent central tendency will underestimate overall risk of exposure