Spatial and temporal trends in new case detection of leprosy in India.

Background: India achieved the goal of ‘leprosy elimination’ by reducing the burden of leprosy to less than one case per 10,000 inhabitants in 2005. Sustained and committed efforts by national programmes have led to a decline in the burden of leprosy to a great extent.

Purpose: To examine the spatial clustering of leprosy case detection and spatiotemporal trend using Bayesian space period model.

Materials: The National Leprosy Eradication Programme (NLEP) data of annual new case detection of leprosy in 34 districts of Maharashtra for eight data years 2007–08 to 2014–15.

Methods: The presence of spatial dependency was assessed using the case detection rate for each of the eight data years spanning from 2007–2015 using Moran’s I statistic and the variation over space and time was modeled using the Bayesian Space Period model.

Results: The Moran’s I value was found to be statistically significant for each of the time period. The period effect was significantly higher than the average in the year 2007(4%), 2009(5%), 2011(6%), 2013(18%) and significantly lower than the average in 2008(7%), 2010(4%), 2012(11%), 2014(9%). The spatial effects varied between 0·579 and 1·52. There was a higher risk of leprosy (50% or more) found in districts of Garhchiroli, Raigad and Warda. The lowest risk of 0·579 was observed in the Nagpur district.

Conclusion: The period effect of new case detection of leprosy using the SP model, measured in terms of relative risk shows a seesaw effect at work in districts of Maharashtra. The alternate jump in the risk of leprosy given by the model could be the actual scenario or due to expended activities in the study area. Further in depth investigation needed to ascertain the facts. Observing the spatial Bayesian effect districts Garhchiroli, Raigad and Warda are at greater risk and need priority care.