Temporal variation and geospatial clustering of dengue in Delhi, India 2015–2018

• Main Authors: Poornima Suryanath Singh, Himanshu K Chaturvedi
• Format: Article
• Language: English
• Published: BMJ Publishing Group 2021-02-01
• Series: BMJ Open
• Online Access: https://bmjopen.bmj.com/content/11/2/e043848.full

Objectives The study was focused on geographical mapping of dengue cases and also to identify the hotspots or high-risk areas of dengue in Delhi.Design A retrospective spatial–temporal (ecological) study. Descriptive analysis was used to know the distribution of dengue cases by age, sex, seasons and districts of Delhi. The spatiotemporal analysis was performed using inverse distance weighting and Getis-Ord Gi* statistic to know the geographical distribution and identify the hotspot areas.Settings All the confirmed and diagnosed dengue cases (IgM +ve or NS1 Antigen +ve ELISA) recorded by the Municipal Corporation of Delhi for the last 4 years (2015–2018) were collected with their local address. The location of all the dengue cases was geocoded using their address to prepare the spatiotemporal dengue database.Participants Record of all the dengue cases (4179) reported for treatment in the hospitals during the past 4 years were extracted and included in the study. Data were not collected directly from dengue patients.Results Seasonal occurrence of dengue cases (4179) shows that the cases start emerging in July, peaked in September–October and declined in December. The proportions of dengue cases were recorded high among the males 57.3% compared with females 42.6%, and differences were also recorded in all the age groups with more cases in age groups <15 and 16-30 years. Mapping of the cases reflects the spatial heterogeneity in the geographical distribution. The geomapping of cases indicates the presence of a significantly high number of cases in West, Southwest, South and Southeast districts of Delhi. High-risk areas or hotspots were also identified in this region.Conclusion Dengue occurrence shows significant association with age, sex and seasons. The spatial analysis identified the high-risk areas, which can aid health administrators to take necessary action for prevention and better disease management.