Socio-ecological predictors of dengue in high incidence area of Delhi, India.

Despite the amplified vector-control measures, sporadic-epidemic outbreaks of dengue incidence occurred in Delhi, intermittently. This study aimed to identify the major individual, household, and community level predictors of dengue cases in the hot-spots of Delhi. Primary survey data was collected from the selected sample of 347 households, at the South- West district of Delhi. This survey has interviewed the head of the household to collect the information of dengue incidences within last one year and household information related to socio-economic, demographic, environmental factors, such as housing pattern, density, water storage containers, drainage and garbage collection site and method, mosquito protection measures and awareness. Among 347 households, 54 households had reported dengue cases, and 69 individuals had reported dengue cases in last one year. Garbage and water collection site and methods, drainage and household type, household monthly income, indoor bamboo plants, construction site (within 500 m), presence of tertiary care hospital, were the significant predictors of dengue incidences in Delhi. In conclusion, strategic control measures and intense social interventions such as household and community awareness, promotion of healthy practices should be promoted to control the dengue incidences.

A retrospective study of environmental predictors of dengue in Delhi from 2015 to 2018 using the generalized linear model.

Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015-2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015-18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated ß coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August-November).

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

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.