Effect of Inter-Observer Variation on the Association between Contamination Hazards and the Microbiological Quality of Water Sources: A Longitudinal Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site, Sample Design and Recruitment
2.2. Survey of Sanitary Risks at Water Sources
2.3. Rainfall
2.4. Laboratory Microbiological Methods
2.5. Analysis of Sanitary Risk Scores versus Bacterial Contamination of Water Sources
3. Results
3.1. Sampling of Water Sources
3.2. Microbiological Contamination of Water Sources
3.3. Sanitary Risk Observations and Rainfall Patterns
3.4. Hazards and Source Contamination
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Bain, R.; Cronk, R.; Hossain, R.; Bonjour, S.; Onda, K.; Wright, J.; Yang, H.; Slaymaker, T.; Hunter, P.; Pruss-Ustun, A.; et al. Global assessment of exposure to faecal contamination through drinking water based on a systematic review. Trop. Med. Int. Health 2014, 19, 917–927. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- WHO/UNICEF. 2017 Annual Report—WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene; World Health Organization: Geneva, Switzerland, 2017; p. 20. [Google Scholar]
- Bain, R.; Cronk, R.; Wright, J.; Yang, H.; Slaymaker, T.; Bartram, J. Fecal Contamination of Drinking-Water in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis. PLoS Med. 2014, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peletz, R.; Kisiangani, J.; Bonham, M.; Ronoh, P.; Delaire, C.; Kumpel, E.; Marks, S.; Khush, R. Why do water quality monitoring programs succeed or fail? A qualitative comparative analysis of regulated testing systems in sub-Saharan Africa. Int. J. Hyg. Environ. Health 2018, 221, 907–920. [Google Scholar] [CrossRef] [PubMed]
- Wright, J.; Liu, J.; Bain, R.; Perez, A.; Crocker, J.; Bartram, J.; Gundry, S. Water quality laboratories in Colombia: A GIS-based study of urban and rural accessibility. Sci. Total Environ. 2014, 485, 643–652. [Google Scholar] [CrossRef]
- Ercumen, A.; Naser, A.M.; Arnold, B.F.; Unicomb, L.; Colford, J.M.; Luby, S.P. Can Sanitary Inspection Surveys Predict Risk of Microbiological Contamination of Groundwater Sources? Evidence from Shallow Tubewells in Rural Bangladesh. Am. J. Trop. Med. Hyg. 2017, 96, 561–568. [Google Scholar] [CrossRef] [Green Version]
- Luby, S.P.; Gupta, S.K.; Sheikh, M.A.; Johnston, R.B.; Ram, P.K.; Islam, M.S. Tubewell water quality and predictors of contamination in three flood-prone areas in Bangladesh. J. Appl. Microbiol. 2008, 105, 1002–1008. [Google Scholar] [CrossRef]
- Misati, A.G.; Ogendi, G.; Peletz, R.; Khush, R.; Kumpel, E. Can Sanitary Surveys Replace Water Quality Testing? Evidence from Kisii, Kenya. Int. J. Environ. Res. Public Health 2017, 14, 152. [Google Scholar] [CrossRef] [Green Version]
- Wright, J.A.; Cronin, A.; Okotto-Okotto, J.; Yang, H.; Pedley, S.; Gundry, S.W. A spatial analysis of pit latrine density and groundwater source contamination. Environ. Monit. Assess. 2013, 185, 4261–4272. [Google Scholar] [CrossRef]
- Howard, G.; Pedley, S.; Barrett, M.; Nalubega, M.; Johal, K. Risk factors contributing to microbiological contamination of shallow groundwater in Kampala, Uganda. Water Res. 2003, 37, 3421–3429. [Google Scholar] [CrossRef]
- Godfrey, S.; Timo, F.; Smith, M.D. Relationship between rainfall and microbiological contamination of shallow groundwater in Northern Mozambique. Water SA 2006, 31, 609–614. [Google Scholar] [CrossRef] [Green Version]
- World Health Organization. Guidelines for Drinking-Water Quality. Volume 3: Surveillance and Control of Community Supplies; World Health Organization: Geneva, Switzerland, 1997; p. 250. [Google Scholar]
- Pond, K.; King, R.; Herschan, J.; Malcolm, R.; McKeown, R.; Schmoll, O. Improving Risk Assessments by Sanitary Inspection for Small Drinking-Water Supplies—Qualitative Evidence. Resources 2020, 9, 71. [Google Scholar] [CrossRef]
- Okotto Okotto, J.; Wanza, P.; Kwoba, E.; Yu, W.; Dzodzomenyo, M.; Thumbi, S.M.; Trajano Gomes da Silva, D.; Wright, J. An assessment of inter-observer agreement of water source classification and sanitary risk observations. Expo. Health 2020, 12, 809–822, in press. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kirkwood, B.; Sterne, A.C. Essential Medical Statistics, 2nd ed.; Wiley-Blackwell: Oxford, UK, 2003. [Google Scholar]
- Jurek, A.M.; Greenland, S.; Maldonado, G.; Church, T.R. Proper interpretation of non-differential misclassification effects: Expectations vs. observations. Int. J. Epidemiol. 2005, 34, 680–687. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sorahan, T.; Gilthorpe, M.S. Non-differential misclassification of exposure always leads to an underestimate of risk: An incorrect conclusion. Occup. Environ. Med. 1994, 51, 839–840. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zambrano, L.D.; Levy, K.; Menezes, N.P.; Freeman, M.C. Human diarrhea infections associated with domestic animal husbandry: A systematic review and meta-analysis. Trans. R. Soc. Trop. Med. Hyg. 2014, 108, 313–325. [Google Scholar] [CrossRef] [Green Version]
- Penakalapati, G.; Swarthout, J.; Delahoy, M.J.; McAliley, L.; Wodnik, B.; Levy, K.; Freeman, M.C. Exposure to Animal Feces and Human Health: A Systematic Review and Proposed Research Priorities. Environ. Sci. Technol. 2017, 51, 11537–11552. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thumbi, S.M.; Njenga, M.K.; Marsh, T.L.; Noh, S.; Otiang, E.; Munyua, P.; Ochieng, L.; Ogola, E.; Yoder, J.; Audi, A.; et al. Linking Human Health and Livestock Health: A “One-Health” Platform for Integrated Analysis of Human Health, Livestock Health, and Economic Welfare in Livestock Dependent Communities. PLoS ONE 2015, 10, e0120761. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2015, 2, 150066. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Barrell, R.A.; Hunter, P.R. Microbiological standards for water and their relationship to health risk. Commun. Dis. Public Health 2000, 8, 8–13. [Google Scholar]
- European Union. Council Directive 98/83/EC on the Quality of Water Intended for Human Consumption; European Union: Brussels, Belgium, 1998. [Google Scholar]
- International Organization for Standardization. Water Quality—Enumeration of Escherichia coli and Coliform Bacteria—Part 1: Membrane Filtration Method for Waters with Low Bacterial Background Flora; ISO 9308-1:2014; International Organization for Standardization: Geneva, Switzerland, 2014; p. 10. [Google Scholar]
- International Organization for Standardization. Water Quality—Detection and Enumeration of Intestinal Enterococci—Part 2: Membrane Filtration Method; ISO 7899-2:2000; International Organization for Standardization: Geneva, Switzerland, 2000; p. 7. [Google Scholar]
- Statacorp LLC. StataCorp Stata Statistical Software, 16th ed.; Statacorp LLC: College Station, TX, USA, 2019. [Google Scholar]
- McBride, G. Using Statistical Methods for Water Quality Management: Issues, Problems, and Solutions; John Wiley and Sons: Hoboken, NJ, USA, 2005. [Google Scholar]
- Yentumi, W.; dzodzomenyo, M.; Seshie-Doe, K.; Wright, J. An assessment of the replicability of a standard and modified sanitary risk protocol for groundwater sources in Greater Accra. Environ. Monit. Assess. 2019, 191. [Google Scholar] [CrossRef] [Green Version]
- Daniels, M.E.; Shrivastava, A.; Smith, W.A.; Sahu, P.; Odagiri, M.; Misra, P.R.; Panigrahi, P.; Suar, M.; Clasen, T.; Jenkins, M.W. Cryptosporidium and Giardia in Humans, Domestic Animals, and Village Water Sources in Rural India. Am. J. Trop. Med. Hyg. 2015, 93, 596–600. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Daniels, M.E.; Smith, W.A.; Schmidt, W.P.; Clasen, T.; Jenkins, M.W. Modeling Cryptosporidium and Giardia in Ground and Surface Water Sources in Rural India: Associations with Latrines, Livestock, Damaged Wells, and Rainfall Patterns. Environ. Sci. Technol. 2016, 50, 7498–7507. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- ARGOSS. Guidelines for Assessing the Risk to Groundwater from Onsite Sanitation; ARGOSS: Wallingford, UK, 2001; p. 97. [Google Scholar]
- Leber, J.; Rahman, M.M.; Ahmed, K.M.; Mailloux, B.; van Geen, A. Contrasting Influence of Geology on E-coli and Arsenic in Aquifers of Bangladesh. Ground Water 2011, 49, 111–123. [Google Scholar] [CrossRef] [PubMed]
- Ferrer, N.; Folch, A.; Maso, G.; Sanchez, S.; Sanchez-Vila, X. What are the main factors influencing the presence of faecal bacteria pollution in groundwater systems in developing countries? J. Contam. Hydrol. 2020, 228, 103556. [Google Scholar] [CrossRef] [PubMed]
- Kirby, M.A.; Nagel, C.L.; Rosa, G.; Iyakaremye, L.; Zambrano, L.D.; Clasen, T.F. Faecal contamination of household drinking water in Rwanda: A national cross-sectional study. Sci. Total Environ. 2016, 571, 426–434. [Google Scholar] [CrossRef] [PubMed]
- Okotto-Okotto, J.; Okotto, L.G.; Price, H.D.; Pedley, S.; Wright, J. A longitudinal study of long-term change in contamination hazards and shallow well quality in two neighbourhoods of Kisumu, Kenya. Int. J. Environ. Res. Public Health 2015, 12, 4275–4291. [Google Scholar] [CrossRef] [Green Version]
- World Health Organization. Measuring Chlorine Levels in Water Supplies; World Health Organization: Geneva, Switzerland, 2011; p. 4. [Google Scholar]
- Thomson, P.; Bradley, D.; Katilu, A.; Katuva, A.; Lanzoni, M.; Koehler, J.; Hope, R. Rainfall and groundwater use in rural Kenya. Sci. Total Environ. 2019, 649, 722–730. [Google Scholar] [CrossRef]
- Chuah, C.J.; Ziegler, A.D. Temporal Variability of Faecal Contamination from On-Site Sanitation Systems in the Groundwater of Northern Thailand. Environ. Manag. 2018, 61, 939–953. [Google Scholar] [CrossRef]
- Bennett, H.B.; Shantz, A.; Shin, M.; Sampson, M.L.; Meschke, J.S. Characterisation of the water quality from open and rope-pump shallow wells in rural Cambodia. Water Sci. Technol. Water Supply 2010, 61, 473–479. [Google Scholar] [CrossRef]
- Lloyd, B.J.; Bartram, J. Surveillance solutions to microbiological problems in water quality control in developing countries. Water Sci. Technol. Water Supply 1991, 24, 61–75. [Google Scholar] [CrossRef]
- Gleeson, C.; Gray, N. The Coliform Index and Waterborne Disease: Problems of Microbial Drinking Water Assessment; Taylor and Francis: London, UK, 1997; p. 208. [Google Scholar]
- US Environmental Protection Agency. Review of Coliphages as Possible Indicators of Fecal Contamination for Ambient water Quality; US Environmental Protection Agency: Washington, DC, USA, 2015; p. 129.
- World Health Organization. Rainwater Collection and Storage; WHO: Geneva, Switzerland, 2020; p. 3. [Google Scholar]
n | Animal Faecal Hazard Score | Human Faecal Hazard Score | Non-Faecal Hazard Score | Protection Measures Compromised-Score | Overall Risk Score | |
---|---|---|---|---|---|---|
Surveyor | ||||||
Surveyor A | 119 | 69.0% | 46.1% | 45.8% | 29.4% | 54.8% |
Surveyor B | 130 | 51.7% | 39.4% | 42.2% | 58.0% | 44.7% |
Surveyor C | 121 | 64.5% | 43.0% | 55.5% | 21.8% | 51.6% |
Surveyor D | 54 | 40.4% | 29.5% | 40.5% | 16.9% | 40.6% |
Surveyor E | 116 | 54.0% | 27.9% | 44.0% | 18.8% | 42.3% |
Surveyor F | 131 | 35.4% | 26.3% | 39.1% | 16.9% | 36.4% |
Source type | ||||||
groundwater | 192 | 77.4% | 30.6% | 32.8% | 36.7% | 40.8% |
rainwater | 225 | 36.2% | 0.0% | 33.2% | 31.7% | 32.7% |
surface water | 254 | 50.4% | 71.7% | 64.2% | 100.0% | 59.9% |
Risk Factor | Univariate Odds Ratio (Confidence Intervals) | p Value | Odds Ratio Adjusted for Source Type (Confidence Intervals) | p Value |
---|---|---|---|---|
Source type (reference: piped water) | ||||
Groundwater | 4.89 (1.84–13.01) | 0.001 | ||
Rainwater | 1.73 (0.70–4.27) | 0.236 | ||
Surface water | 30.63 (16.74–89.38) | <0.001 | ||
Rainfall in 7 days preceding sampling (mm) | 1.00 (0.99–1.01) | 0.336 | ||
Sanitary risk score Overall (%) | 1.05 (1.03–1.08) | <0.001 | 1.01 (0.99 to 1.04) | 0.363 |
Human faecal hazard (%) | 1.01 (1.00–1.02) | 0.007 | 1.00 (0.99–1.01) a | 0.915 |
Animal faecal hazard (%) | 1.02 (1.01–1.03 | <0.001 | 1.02 (1.00–1.03) | 0.014 |
Non-faecal hazard (%) | 1.02 (1.01–1.04) | 0.006 | 1.01 (0.99–1.02) | 0.521 |
Protection measures compromised (%) | 1.03 (1.02–1.05) | <0.001 | 1.00 (0.98 to 1.03) b | 0.747 |
Risk Factor | Univariate Odds Ratio (Confidence Intervals) | p Value | Multivariate Odds Ratio (Confidence Intervals) | p Value |
---|---|---|---|---|
Source type (reference: piped water) | ||||
Groundwater | 5.94 (2.00 to 17.63) | 0.001 | ||
Rainwater | 3.64 (1.37 to 9.69) | 0.010 | ||
Surface water | 32.49 (9.95 to 106.1) | <0.001 | ||
Rainfall in 7 days preceding sampling (mm) | 1.01 (1.00 to 1.02) | 0.008 | ||
Sanitary risk score Overall (%) | 1.04 (1.01 to 1.06) | 0.002 | 1.01 (0.98 to 1.04) | 0.648 |
Human faecal hazards (%) | 1.02 (1.01 to 1.03) | <0.001 | 1.00 (0.99 to 1.02) a | 0.830 |
Animal faecal hazard (%) | 1.02 (1.00 to 1.03) | 0.010 | 1.02 (1.00 to 1.03) | 0.031 |
Non-faecal hazard (%) | 1.01 (1.00 to 1.03) | 0.080 | 1.00 (0.98 to 1.01) | 0.697 |
Protection measures compromised (%) | 1.03 (1.01 to 1.04) | <0.001 | 1.00 (0.97 to 1.04) b | 0.942 |
E. coli | Intestinal Enterococci | |||
---|---|---|---|---|
Risk Factor | Odds Ratio (Confidence Intervals) | p Value | Odds Ratio (Confidence Intervals) | p Value |
Source type (reference: groundwater) | ||||
Rainwater | 0.38 (0.11 to 1.29) | 0.122 | 0.85 (0.28 to 2.63) | 0.779 |
Surface water | 11.17 (3.01 to 41.42) | <0.001 | 6.82 (1.65 to 28.14) | 0.008 |
Rainfall in 7 days preceding sampling (mm) | 1.02 (1.00 to 1.04) | 0.106 | ||
Sanitary risk score Overall (%) | 0.97 (0.94 to 1.01) | 0.081 | 0.99 (0.96 to 1.03) | 0.625 |
Surveyor interaction with risk score (reference: Surveyor A) | ||||
Surveyor B | 1.02 (0.99 to 1.05) | 0.122 | 1.01 (0.99 to 1.03) | 0.470 |
Surveyor C | 1.02 (0.99 to 1.04) | 0.193 | 1.00 (0.98 to 1.03) | 0.753 |
Surveyor E | 1.01 (0.99 to 1.04) | 0.429 | 1.00 (0.97 to 1.02) | 0.854 |
Surveyor F | 1.01 (0.99 to 1.04) | 0.293 | 1.00 (0.98 to 1.03) | 0.869 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Okotto-Okotto, J.; Trajano Gomes da Silva, D.; Kwoba, E.; Thumbi, S.M.; Wanza, P.; Yu, W.; Wright, J.A. Effect of Inter-Observer Variation on the Association between Contamination Hazards and the Microbiological Quality of Water Sources: A Longitudinal Study. Int. J. Environ. Res. Public Health 2020, 17, 9192. https://doi.org/10.3390/ijerph17249192
Okotto-Okotto J, Trajano Gomes da Silva D, Kwoba E, Thumbi SM, Wanza P, Yu W, Wright JA. Effect of Inter-Observer Variation on the Association between Contamination Hazards and the Microbiological Quality of Water Sources: A Longitudinal Study. International Journal of Environmental Research and Public Health. 2020; 17(24):9192. https://doi.org/10.3390/ijerph17249192
Chicago/Turabian StyleOkotto-Okotto, Joseph, Diogo Trajano Gomes da Silva, Emmah Kwoba, Samuel.M Thumbi, Peggy Wanza, Weiyu Yu, and Jim A. Wright. 2020. "Effect of Inter-Observer Variation on the Association between Contamination Hazards and the Microbiological Quality of Water Sources: A Longitudinal Study" International Journal of Environmental Research and Public Health 17, no. 24: 9192. https://doi.org/10.3390/ijerph17249192