PhD defence A.T. (Anneke) Taal

Below is a brief summary about the dissertation:

Leprosy is a chronic infectious disease that affects the skin and peripheral nerves and which can lead to disability. It is caused by the bacterium Mycobacterium leprae, and the primary mode of transmission is human to human. People who have long and frequent contact with an infectious person are at higher risk of developing the disease. Despite decades of intensive leprosy control efforts, leprosy remains a public health problem in many countries, with globally around 210,000 new cases detected annually. Although many countries have achieved the World Health Organisation (WHO) target of elimination of leprosy as a public health problem (less than 1 patient per 10,000 population) at the national level, high-endemic areas continue to persist at sub-national level. The WHO advocates for early case detection and (preventive) treatment as essential measures for interrupting disease transmission in these high-endemic areas. The geographical distribution of leprosy is known to be heterogeneous (unevenly), and leprosy cases are concentrated in clusters or hotspots. Identifying these leprosy clusters is important as they indicate areas where transmission is likely higher and thus, where the population is at higher risk and requires preventive interventions. Geospatial analysis methods can be used to identify these clusters and understand the disease pattern in an area.

There are three types of cluster analysis methods: spatial, temporal, and space-time. Spatial cluster analysis techniques look at the spatial relationship between cases. They can be further divided into global clustering, local clustering and focused clustering. Spatial and space-time methods have been increasingly used in the last 20 years to understand the geographical distribution and trend of leprosy cases; to identify clusters or hotspots where active case finding should be implemented; and to examine their potential relationship with geographical, social-cultural, and health-system-related factors. However, these methods are mainly used for research purposes and require expertise to analyse and interpret the results. Leprosy programmes in endemic countries often do not have this expertise and thus rely on experts in geospatial methods. Moreover, most of the studies typically use methods that present clusters with large at-risk populations. As leprosy programmes do not have unlimited resources to provide active case finding and preventive interventions to the entire at-risk populations, programme managers must prioritize. Therefore, they could benefit from knowing which simple geospatial methods they can use that reveal clusters with smaller at-risk populations, allowing for a more targeted implementation of leprosy control and prevention interventions, and to improve efficiency in the use of resources.

To research how leprosy programmes can be supported in optimising leprosy control and allocating resources more efficiently, the following research questions were addressed:

1. How can geospatial methods be used to optimise leprosy control and preventive interventions?
2. To what extent can participatory mapping combining geospatial methods with public health expertise in leprosy programmes be used to identify target areas for leprosy control and preventive interventions?
3. How can populations (at risk) requiring preventive interventions be estimated?