Water quality testing is critical for guiding water safety management and ensuring public health. In many settings, however, water suppliers and surveillance agencies do not meet regulatory requirements for testing frequencies. This study examines the conditions that promote successful water quality monitoring in Africa, with the goal of providing evidence for strengthening regulated water quality testing programs. We compared monitoring programs among 26 regulated water suppliers and surveillance agencies across six African countries. These institutions submitted monthly water quality testing results over 18 months. We also collected qualitative data on the conditions that influenced testing performance via approximately 821 h of semi-structured interviews and observations. Based on our qualitative data, we developed the Water Capacity Rating Diagnostic (WaterCaRD) to establish a scoring framework for evaluating the effects of the following conditions on testing performance: accountability, staffing, program structure, finances, and equipment & services. We summarized the qualitative data into case studies for each of the 26 institutions and then used the case studies to score the institutions against the conditions captured in WaterCaRD. Subsequently, we applied fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare these scores against performance outcomes for water quality testing. We defined the performance outcomes as the proportion of testing Targets Achieved (outcome 1) and Testing Consistency (outcome 2) based on the monthly number of microbial water quality tests conducted by each institution. Our analysis identified motivation & leadership, knowledge, staff retention, and transport as institutional conditions that were necessary for achieving monitoring targets. In addition, equipment, procurement, infrastructure, and enforcement contributed to the pathways that resulted in strong monitoring performance. Our identification of institutional commitment, comprising motivation & leadership, knowledge, and staff retention, as a key driver of monitoring performance was not surprising: in weak regulatory environments, individuals and their motivations take-on greater importance in determining institutional and programmatic outcomes. Nevertheless, efforts to build data collection capacity in low-resource settings largely focus on supply-side interventions: the provision of infrastructure, equipment, and training sessions. Our results indicate that these interventions will continue to have limited long-term impacts and sustainability without complementary strategies for motivating or incentivizing water supply and surveillance agency managers to achieve testing goals. More broadly, our research demonstrates both an experimental approach for diagnosing the systems that underlie service provision and an analytical strategy for identifying appropriate interventions.

Water quality information is important for guiding water safety management and preventing water-related diseases. To assess the current status of regulated water quality monitoring in sub-Saharan Africa, we evaluated testing programs for fecal contamination in 72 institutions (water suppliers and public health agencies) across 10 countries. Data were collected through written surveys, in-person interviews, and analysis of microbial water quality testing levels. Though most institutions did not achieve the testing levels specified by applicable standards or World Health Organization (WHO) Guidelines, 85% of institutions had conducted some microbial water testing in the previous year. Institutions were more likely to meet testing targets if they were suppliers (as compared to surveillance agencies), served larger populations, operated in urban settings, and had higher water quality budgets (all p < 0.05). Our results indicate that smaller water providers and rural public health offices will require greater attention and additional resources to achieve regulatory compliance for water quality monitoring in sub-Saharan Africa. The cost-effectiveness of water quality monitoring should be improved by the application of risk-based water management approaches. Efforts to strengthen monitoring capacity should pay greater attention to program sustainability and institutional commitment to water safety.

Information about the quality of rural drinking water sources can be used to manage their safety and mitigate risks to health. Sanitary surveys, which are observational checklists to assess hazards present at water sources, are simpler to conduct than microbial tests. We assessed whether sanitary survey results were associated with measured indicator bacteria levels in rural drinking water sources in Kisii Central, Kenya. Overall, thermotolerant coliform (TTC) levels were high: all of the samples from the 20 tested dug wells, almost all (95%) of the samples from the 25 tested springs, and 61% of the samples from the 16 tested rainwater harvesting systems were contaminated with TTC. There were no significant associations between TTC levels and overall sanitary survey scores or their individual components. Contamination by TTC was associated with source type (dug wells and springs were more contaminated than rainwater systems). While sanitary surveys cannot be substituted for microbial water quality results in this context, they could be used to identify potential hazards and contribute to a comprehensive risk management approach.

This study investigated the effectiveness of Water Safety Plans (WSP) implemented in 99 water supply systems across 12 countries in the Asia-Pacific region. An impact assessment methodology including 36 indicators was developed based on a conceptual framework proposed by the Center for Disease Control (CDC) and before/after data were collected between November 2014 and June 2016. WSPs were associated with infrastructure improvements at the vast majority (82) of participating sites and to increased financial support at 37 sites. In addition, significant changes were observed in operations and management practices, number of water safety-related meetings, unaccounted-for water, water quality testing activities, and monitoring of consumer satisfaction. However, the study also revealed challenges in the implementation of WSPs, including financial constraints and insufficient capacity. Finally, this study provided an opportunity to test the impact assessment methodology itself, and a series of recommendations are made to improve the approach (indicators, study design, data collection methods) for evaluating WSPs.

Improving sanitation conditions in low-income communities is a major challenge for rapidly growing cities of the developing world. The expenses and logistical difficulties of extending sewerage infrastructure have focused increasing attention on the requirements for safe and cost-effective fecal sludge management services. These services, which are primarily provided by the private sector, include the collection and treatment of fecal waste from latrine pits and septic tanks. To determine the degree to which market forces can promote safe fecal sludge removal in low-income neighborhoods of Kisumu, Kenya, we compared household willingness-to-pay for formal pit emptying with the prices charged by service providers. Through surveys of 942 households and a real-money voucher trial with 646 households, we found that stated and revealed demand for formal emptying services were both low, with less than 20% of households willing to pay full market prices. Our results suggest that improving fecal sludge management in these neighborhoods via the private sector will require large subsides, ranging from 55.1–81.4 million KES (551,000–814,000 USD) annually, to address the gap between willingness-to-pay and market prices. Raising and administering subsidies of this scale will require the development of a city-wide sanitation master plan that includes investment, management, and regulatory procedures for fecal sludge management. In the absence of government investment and coordination, it is unlikely that the private sector will address safe sanitation needs in low-income areas of Kisumu.