UC Berkeley

The purpose of this dissertation research is to develop methods and methodologies for estimating microbial exposures and health risks in low-resource settings. Specifically, we focus on evaluating occupational exposures and pathogenic health risks to sanitation workers, a globally understudied and historically marginalized population. Sanitation workers are vital to the function of sanitation systems which separate society from their hazardous waste. However, little is understood about the health risks sanitation workers encounter during the collection and processing of human waste streams. As a result, sanitation workers remain largely invisible in sanitation infrastructure planning and process design.

Quantitative risk assessments are necessary to develop meaningful standards and bring visibility to the health risks of invisible workers. However, there is a current gap in the methods used to estimate microbial risks in low-resource settings.

In this dissertation, we explore different strategies for measuring pathogen exposures and estimating health risks in low-resource contexts. Using the methods developed herein, microbial exposures and risks to workers are evaluated at each stage in a waste-to-fuel process in Kigali, Rwanda. Specifically, worker exposure and risk to inhaled endotoxin, ingested adenovirus, and ingested Cryptosporidium are estimated.

Throughout the dissertation, a combination of environmental sampling methods, behavioral observations, laboratory analyses, and mathematical modeling techniques are used. A specific emphasis is placed on a stochastic modeling approach in order to overcome the variability and uncertainty of conducting risk assessment in low-resource settings.

Chapter 1 is an introduction to onsite sanitation systems and the existing gaps in measuring microbial risks in low-resource settings. In chapter 2, a model is constructed using a combination of empirical measurements and literature reported values. Concentrations of indicator organisms are measured in surface waters while parameters such as the frequency of exposure, exposure volume, and the ratio between indicators and pathogens are derived from literature reported values of other exposure scenarios and country contexts. Although this approach is common, the use of assumptions from the literature introduces a high degree of model uncertainty. Thus, in chapter 3 and chapter 4, an attempt to reduce the uncertainty of using ratios is made by collecting site-specific data on pathogen and bioaerosol concentrations in environmental and personal samples. Site-specific observations of individual worker behavior (including exposure activities, exposure frequency, and duration) were also completed incorporated into models estimating worker risk along the ingestion (chapter 3) and inhalation (chapter 4) routes of exposure. Chapter 5 discusses the gaps in sanitation global goal frameworks which perpetuate sanitation worker marginalization and exclusion from sanitation intervention benefits, and highlights areas of research and action that may bring visibility and voice to sanitation workers worldwide.