A Bayesian approach to study the space time variation of leprosy in an endemic area of Tamil Nadu, South India

• Published in: International journal of health geographics Vol. 7; no. 1; p. 40
• Main Authors: Joshua, Vasna, Gupte, Mohan D, Bhagavandas, M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 21.07.2008; BioMed Central; BMC
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Bayes Theorem; Bayesian statistical decision theory; Child; Child, Preschool; Cohort Studies; Control; Demography; Distribution; Endemic Diseases; Health Surveys; Humans; India - epidemiology; Infant; Leprosy; Leprosy - epidemiology; Methods; Middle Aged; Social aspects; Space-Time Clustering; Spatial analysis (Statistics); Time Factors; Young Adult; India
• ISSN: 1476-072X; 1476-072X
• DOI: 10.1186/1476-072X-7-40

In leprosy endemic areas, patients are usually spatially clustered and not randomly distributed. Classical statistical techniques fail to address the problem of spatial clustering in the regression model. Bayesian method is one which allows itself to incorporate spatial dependence in the model. However little is explored in the field of leprosy. The Bayesian approach may improve our understanding about the variation of the disease prevalence of leprosy over space and time. Data from an endemic area of leprosy, covering 148 panchayats from two taluks in South India for four time points between January 1991 and March 2003 was used. Four Bayesian models, namely, space-cohort and space-period models with and without interactions were compared using the Deviance Information Criterion. Cohort effect, period effect over four time points and spatial effect (smoothed) were obtained using WinBUGS. The spatial or panchayat effect thus estimated was compared with the raw standardized morbidity (leprosy prevalence) rate (SMR) using a choropleth map. The possible factors that might have influenced the variations of prevalence of leprosy were explored. Bayesian models with the interaction term were found to be the best fitted model. Leprosy prevalence was higher than average in the older cohorts. The last two cohorts 1987-1996 and 1992-2001 showed a notable decline in leprosy prevalence. Period effect over 4 time points varied from a high of 3.2% to a low of 1.8%. Spatial effect varied between 0.59 and 2. Twenty-six panchayats showed significantly higher prevalence of leprosy than the average when Bayesian method was used and it was 40 panchayats with the raw SMR. Reduction of prevalence of leprosy was 92% for persons born after 1996, which could be attributed to various intervention and treatment programmes like vaccine trial and MDT. The estimated period effects showed a gradual decline in the risk of leprosy which could be due to better nutrition, hygiene and increased awareness about the disease. Comparison of the maps of the relative risk using the Bayesian smoothing and the raw SMR showed the variation of the geographical distribution of the leprosy prevalence in the study area. Panchayat or spatial effects using Bayesian showed clustersing of leprosy cases towards the northeastern end of the study area which was overcrowded and population belonging to poor economic status.