Jessica King is a student at the London School of Hygiene & Tropical Medicine completing her MSc in Control of Infectious Diseases. She spent the summer working with the GAHI team, and here she talks about how she found and mapped water and sanitation data for India.
India is home to 600 million people who routinely practise open defecation, the largest number in the world. This has serious implications for a huge array of health issues, including helminth infections. Lack of access to good sanitation and water increases the likelihood of being infected with worms, and the chances of becoming re-infected after deworming. In the light of deworming projects to be rolled out across eight Indian states, the GAHI group wanted to take a closer look at how the availability of water and sanitation varied geographically, and to examine the inequality in access across the country and across states.
I started this project at GAHI in July 2014 with very little knowledge of neglected tropical diseases; my first degree was in mathematics, with particular interest in modelling the dynamics of populations and epidemics, and I had most recently been working as a maths teacher. I wanted to get some experience of epidemiological techniques such as mapping and spatial analysis before I started my MSc in Control of Infectious Diseases at the London School of Hygiene & Tropical Medicine in September 2014.
The first step was to find our data. Luckily, there had been a national census of India in 2011, which had included detailed questions on the types of water and sanitation available to each household. The census is the biggest in the world, requiring 2.7 million officials to survey the country’s 250 million households, at a cost of US$370 million. The census Digital Library is an incredible resource which contains over 50,000 tables of published data. Among these I was able to find data on access to water and sanitation in both rural and urban populations, at district and sub-district level. There are 640 districts and approximately 6000 sub-districts in the country, so this was a very high level of detail. The existence of recent census data that covered the entire population also removed the need for statistical modelling to estimate coverage.
Having found my data, I now needed to turn it into maps. For this I used ArcGIS, a geographic information system. The first maps I produced used district level data, showing access to sanitation and water, and open defecation rates, across the whole country. The results were striking: there were areas on the west coast with over 75% of the population having access to improved sanitation, but also large parts of the north east of the country where less than 25% of people have access to sanitation. There was also a clear distinction between people who lived in cities, who were much more likely to have sanitation than those living in rural areas, even in the same district.
This difference made me look into three states in more detail. Uttar Pradesh, Bihar and Orissa are all targets for deworming programmes, and so I produced sub-district level maps of these states to examine the variations within them. Again, the maps revealed some interesting patterns. In Uttar Pradesh, the largest state by population (200 million people), we can see that although there are low rates of open defecation in the north of the state and in urban centres such as Lucknow, most areas have over half of the population practising open defecation.
These maps are food for thought for those planning deworming in India, as well as anybody interested in water and sanitation and its effects on health. It is clear that there is a great deal of inequality in access to good water and sanitation, between areas in the same state and even the same district. It also seems that better sanitation in towns and cities is masking the very poor level of coverage in neighbouring rural areas, and policy makers need to consider how to address this disparity.
See and download all the maps Jess produced on water and sanitation in India (choose India from the filter)