Uncertainty and Sensitivity Analysis to Advance the Use of Quantitative Microbial Risk Assessment (QMRA) in an Informal Settlement in Kampala, Uganda

Transcription

1 Uncertainty and Sensitivity Analysis to Advance the Use of Quantitative Microbial Assessment (QMRA) in an Informal Settlement in Kampala, Uganda Diana M. Byrne Stephanie A. Houser, Kerry Hamilton, Muwonge Mubasira, David Katende, John T. Trimmer, Hannah A.C. Lohman, Charles Haas, Noble Banadda, Jeremy S. Guest November 1, 2018

2 Acknowledgements Collaborators: Community Integrated Development Initiatives (CIDI) Uganda Rural Community Support Foundation (URCSF) Department of Agricultural and Biological Engineering, Makerere University, Kampala Department of Sociology, UIUC Funding Social and Behavioral Sciences Initiative and Campus Research Board through the Office of the Vice Chancellor for Research Ethical Approvals University of Illinois Makerere University Uganda National Council for Science and Technology

3 Challenges of prioritizing WaSH interventions in resource-limited settings Hazard identification Coupling quantitative microbial risk assessment (QMRA) with uncertainty and sensitivity analyses Exposure assessment characterization Dose response management Using results to identify key exposure pathways and data collection needs

4 Potential Exposure to Fecal Contamination: Drinking water Hands Soil Food Flies Flooding Drainage ditches Garbage Sanitation systems Challenge: In a resource-limited community, how can an organization (like CIDI) prioritize water, sanitation, and hygiene efforts? Kampala, Uganda

5 Quantitative microbial risk assessment (QMRA) could be an informative tool for decision-making in these settings Identification of pathogens based on likelihood and severity Hazard identification Quantify the dose of pathogen a person is exposed to through each exposure pathway Exposure assessment Dose response Mathematical models that describe relationship between dose and response Estimation of probability of harm given exposure and dose characterization management Work with stakeholders to communicate and mitigate risks

6 QMRAs have been conducted in resource-limited settings, many have focused on ingestion of contaminated water.

7 Measured Location However, QMRA is often challenging to apply in resourcelimited communities which lack detailed pathogen data. Monitoring Health s Water sources Increasing complexity and cost Fecal indicators Ratios Household water and other pathways Pathogens Quantitative microbial risk assessment (QMRA) connects pathogen data to health risks Objectives: Determine if insights can be gained from this approach despite uncertainty Identify major sources of uncertainty and highlight data collection needs

8 Challenges of prioritizing WaSH interventions in resource-limited settings Hazard identification Coupling quantitative microbial risk assessment (QMRA) with uncertainty and sensitivity analyses Exposure assessment characterization Dose response management Using results to identify key exposure pathways and data collection needs

9 QMRA was performed following the 5 step framework and coupled with uncertainty and sensitivity analysis. Exposure assessment Hazard identification Dose response characterization management Uncertainty and sensitivity analyses Pathogens: Rotavirus, Campylobacter, Cryptosporidium using measured E. coli and E. coli to pathogen ratios Exposure pathways: Drinking water, hand to mouth contact, soil ingestion (exploratory ingestion, child only) Dose response models: Previously developed pathogenspecific models developed from epidemiological data characterization: Modeled as risk (probability) of infection from each pathogen management: Collaboration with local NGO (Community Integrated Development Initiatives) Uncertainty and sensitivity: Monte Carlo analysis (10,000 runs) and Spearman s rank correlation

10 Household surveys were conducted with the help of students from Makerere University Secion 1 Section 2 zones 1, 2, 3 zones 4, 5, 6 demographics child health zones 7, 8, 9 Section 3 water sanitation

11 Water, hand rinse, and soil samples were collected and analyzed for total coliforms and E. coli WHIRL PAK Source water WHIRL PAK Household water WHIRL PAK Hand rinse Membrane filtration Plates for total coliforms and E. Coli WHIRL PAK Soil

12 Pathogen dose was calculated from E. coli concentrations, ratios, and exposure parameters for each pathway Drinking water Hand to mouth contact Soil dose = E. coli concentration pathogen to E. coli ratio volume of water dose = E. coli concentration pathogen to E. coli ratio frequency of hand to mouth contact time awake area of hand in contact with mouth transfer efficiency dose = E. coli concentration pathogen to E. coli ratio frequency of soil ingestion mass of soil put in mouth during contact

13 Uncertainty analysis was performed by developing probability distributions for each parameter Uniform Minimum, maximum Triangular Minimum, maximum, most probable Normal Mean, standard deviation Weibull Shape, scale Empirical From raw data Probability distributions assigned to each QMRA model parameter Literature Volume of water ingested multiple QMRAs in resource-limited settings E. coli to pathogen ratios multiple QMRAs in resource-limited settings Area of hands in contact with mouth AuYeung et al., 2007; Mattioli et al., 2015 Frequency of hand to mouth contacts Xue et al., 2007; Mattioli et al., 2015 Time awake during the day USEPA, 2011; Mattioli et al., 2015 Transfer efficiency from hands to mouth Julian et al., 2009; Mattioli et al., 2015 Dose response model parameters Multiple studies, modeled by Kyle S. Enger Survey Frequency of soil ingestion Mass of soil ingested Sampling E. coli concentrations

14 Challenges of prioritizing WaSH interventions in resource-limited settings Hazard identification Coupling quantitative microbial risk assessment (QMRA) with uncertainty and sensitivity analyses Exposure assessment characterization Dose response management Using results to identify key exposure pathways and data collection needs

15 Household survey results can provide for an initial screening of potential exposure pathways Drinking Water Handwashing 89% of respondents reported washing hands more than 3 times per day Soil Ingestion 40% of children were reported to have put soil in their mouth in the past 7 days

16 Escherichia coli (E. Coli) Count piped (city) water piped (city) water + rainwater springs/wells E. coli data indicate evidence of potential fecal contamination on hands, in household water, and in soil. child TNTC; too numerous to count caregiver Hand Rinse per 2 hands Water per 100 ml Soil per g dry soil Source Water per 100 ml Household Samples Community Samples

17 Probability of Infection Hand to mouth contact was shown to have the greatest median probability of infection, though results are uncertain 4x x10-2 3x x10-2 2x x10-2 1x10-2 5x10-3

18 Sensitivity analysis reveals pathogen data as the largest influence on results for risk of infection Pathogen Data Rotavirus Campylobacter Cryptosporidium Exposure Assessment Dose Response

19 Measured Location Future work will use pathogen data to consider of benefits of data collection for health decision-making Monitoring Health s Quantitative microbial risk assessment (QMRA) connects pathogen data to health risks Water sources Increasing complexity and cost Household water and other pathways Quantitative polymerase chain reaction (qpcr) to quantify pathogens Ratios Fecal indicators Pathogens

20 This approach identified the major exposure pathway; however, pathogen data may be necessary to reduce uncertainty for estimated health risks Hazard identification Exposure assessment Dose response characterization management Uncertainty and sensitivity analyses