“Water, water everywhere, nor any drop to drink”

The poem “The Rime of the Ancient Mariner” takes place at sea, but the above phrase holds a strong message for public health actors. From a health perspective the absence of potable water is just one aspect of the problem. In fact, it’s the availability and accessibility to safe water that is intricately linked with health. For Neglected Tropical Diseases (NTDs), a diverse set of 20 infectious diseases, many of them parasitic and/or vector-borne, there is a growing consensus and mounting evidence that the role of WASH is vital for the long-term prevention of these diseases and for increasing health equity in affected communities (see our previous blog post on WASH and NTDS).

This is the realm where Sustainable Development Goals 3 (Good Health and Well-Being) and 6 (Clean Water and Sanitation) meet and, in 2015, the WHO released their 2015-2020 global strategy for integrating WASH and NTDs.  This year on the anniversary of the London Declaration on NTDs, 31st January, the WHO launched a practical guide on how to integrate WASH and NTDs.

Impact of WASH interventions on NTDs.

A challenge for the integration of WASH and NTD programmes is linked to measuring the impact of WASH interventions on NTDs, particularly in settings where NTD infections are being controlled using periodic large-scale population treatment (PCT).

It is logical to assume that WASH infrastructure and good practices need to be in place when scaling down an NTD control programme, in order to ensure that the control/elimination of the disease is sustained. However, financial issues are at stake and questions of “cost-effectiveness” and “measurable impact” are the at the forefront of decision making; often followed by “which WASH”, “how much WASH” and “for how long”. Measuring that impact can be difficult since there are different types of interventions and their effectiveness and uptake by the target population can vary greatly (will everyone use the provided toilet/water tap/soap, does everyone have access to that toilet/water tap/soap?).

Mathematical Modelling for WASH and NTDs.

A recent paper by Luc Coffeng and colleagues, uses mathematical model simulations of soil-transmitted helminth (STH) infections to determine the short and long-term impact of different WASH interventions on STH control and deworming programmes.

They used a previously developed and tested model for STH transmission and control called WORMSIM. The original model simulates:

  • Exposure of humans to a reservoir of STH infections.
  • Contribution of infected humans to the reservoir of infection.
  • Egg production of the female adult worm.
  • Variation of worm burden across the human population.
  • Variation of PCT treatment across age groups and individual’s willingness to participate.

The model simulations give outputs of infection levels based on parasitological diagnostic tests, namely through counting eggs in faecal samples.

Expanding STH model with environmental WASH factors.

The research team expanded this model to include simulations of how the environment is contaminated by STH eggs and how humans become infected from the environment.

They then developed the WASH intervention aspects of the model, in particular:

  • Hygiene intervention – reduction of individuals’ exposure to infection e.g. through hand washing.
  • Sanitation intervention – reduction of individuals’ contribution to the environment e.g. through use of latrines.

The simulations were run with and without the addition of deworming programmes. For deworming, the model simulations assumed PCT with albendazole kills 99% of round worms (Ascaris lumbricoides), 95% of hookworms (Necator and Ancylostoma spp) and 60% of whipworms (Trichuris trichiura). They were also run using different levels of:

  • WASH intervention uptake – proportion of the population that takes up the WASH interventions. Starting with 70% of the population and then using 95% of the population.
  • WASH intervention effectiveness – the average reduction in exposure or contribution to transmission over time, using 70% and 95% reduction in individuals that take up intervention.

The authors ran the simulations 100 times for each scenario to look at the impact of the interventions on STH transmission (short-term impact) and 1000 times to look at the probability of transmission interruptions and looked at the proportion of simulations that resulted in zero worm prevalence 50 years after stopping PCT (long-term impact).

What mathematical models tells us about the impact of WASH on STH control and elimination.

From the simulations the authors observed that:

  • PCT interventions mask the impact of WASH interventions except hygiene, where there was more of an impact in a PCT school-based setting, presumably because only treating school-aged children left a bigger reservoir of infections in the untreated adults, where hygiene interventions could have an impact.
  • The impact of WASH interventions was not uniform across all species and was higher on worms with a shorter adult lifespan (A lumbricoides and T. trichiura) than on hookworms.
  • Higher uptake of WASH is required for elimination in high prevalence settings pre-control.
  • Most importantly: There was a strong long-term impact of WASH interventions on maintaining STH control and elimination after PCT is scaled down/stopped, reducing the risk and speed of bounce-back of infections. Here, high uptake was more important than high effectiveness.

The authors highlighted some of the limitations of their model, such as the assumption that the effectiveness of WASH is the same for everybody who takes up WASH. They called for further trials focused on long-term impacts, looking at areas of high reinfection rates and disentangling interventions that prevent exposure vs. contribution to the environmental reservoir. They highight that WASH related data as well as NTD data should be carefully monitored.

Model-predicted additional impact of WASH on hookworm prevalence in settings where school-based treatment is scaled down or stopped. Lines represent the average of repeated simulations with random pre-control transmission conditions that followed a specific path through a decision tree for scaling down or stopping PCT. See https://doi.org/10.1371/journal.pntd.0006758.g003.