Seroprevalence of chikungunya virus infection in East Delhi over a decade: A retrospective study

Introduction

Chikungunya virus (CHIKV), a positive sense RNA virus is an alphavirus belonging to the Togaviridae family responsible for sporadic outbreaks of fever. ¹ It is commonly reported from tropical and sub-tropical regions and presents with fever, headache, nausea, vomiting, myalgia and arthralgia, similar to diseases such as dengue and malaria. ² Identified in over 110 countries across Asia, Africa, Europe and the Americas, CHIKV remains unpredictable and poorly diagnosed, even though it poses a significant disease burden.

The virus has a genome size of approximately 11.8 kb, which encodes non-coding non-structural (nsP1, nsP2, nsP3, nsP4) and structural (C, E3, E2, 6K, and E1) polyproteins. The two open reading frames are flanked by 5’ and 3’ untranslated regions. ³ Mutations in these genes affect the evolution of the virus, and augment their adaptability to host and vector. Certain mutations are associated with outbreaks as they facilitate the survival of viruses inside the vector.^4–6 Though as a single serotype, CHIKV is divided into three distinct genotypes. ⁷ Infected Aedes mosquitoes, especially aegypti and albopictus, can transmit the dengue virus, yellow fever virus and are also responsible for the transmission of CHIKV in the human host.^8–10 As in other Asian countries, both vectors are responsible for the transmission of the disease in India leading to geographical clustering of cases. In order to circumvent the possibility of a vector, agent-host-environment strategies to strengthen public health measures in the most affected areas are crucial to break the transmission cycle.

CHIKV may go undiagnosed as the clinical symptoms are similar to dengue. The absence of a reliable point-of-care rapid test causes a delay in diagnosis, which further allows the disease to progress to its severe forms.^11–14

Our study was thus aimed to detect the epidemiological burden and dynamics of CHIKV transmission in East and North East Delhi during the last ten years from year 2010 to 2020, with GIS mapping to check for possible clusters of positive cases and hotspots during the given time frame for designing future improvement and focused interventions.

Method and materials

Archived data of suspected patients (identified by physicians in the hospital presenting with unexplained fever or arthralgia, screened for CHIKV IgM by ELISA), were retrieved and screened for duplicate entry. Information on age, gender, residence/location was systematically entered in MS Excel (Table 1). Further, descriptive analysis was performed using SPSS version 29 (Armonk, New York).

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Table 1.

Age-wise and gender distribution of patients tested for/positive for CHIKV IgM.

Age group (in years)	Total number of patients tested (n = 445)	Number of patients that tested positive for CHIKV IgM n (%)
0–1	26	13 (50.0)
02–4	9	3 (33.3)
5–14	61	35 (57.4)
15–24	89	49 (55.0)
25–34	78	37 (47.4)
35–44	71	45 (63.4)
45–54	56	39 (69.6)
55–64	32	20 (62.5)
65 and above	23	14 (60.9)
Gender
Male	200	119 (59.5)
Female	245	136 (55.5)

Categorical variables were described using frequencies and percentages, while continuous variables were summarised as means and standard deviations (mean ± SD). The statistical differences in sociodemographic characteristics and seasonal variations were analysed using the χ² test. To identify the associated risk factors for Chikungunya, binary logistic regression analysis was employed. Initially, crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Following this, potential confounders (variables with p-value <0.25 in the bivariate analysis) were included in the multivariable model, and adjusted odds ratios were estimated.

Delhi is divided into 11 districts, with maximum population density in the North East & East, and the study population primarily belonged to these densely populated areas (Fig. 1). The spatial distribution of positive cases was analysed using QGIS software to visualise geographic patterns and potential clustering across the study area. Prior to analysis, the dataset underwent comprehensive data cleaning, which included the removal of duplicate records, entries with missing or inaccurate geographic coordinates, and points located outside the defined study boundaries. The spatial resolution of the analysis was determined based on the scale and density of the occurrence data, with visualisation and density surfaces typically represented at a 1 km² grid level to balance detail and interpretability. However, no spatial auto-correlation test, such as Moran's I, was performed.

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Figure 1.

Map of study site depicting districts and state border.

Our data retrieval process utilised the documented records from the Department of Microbiology, of our tertiary care hospital in Delhi, keeping the identity of the patients anonymised and confidential.

During the period 2010 to 2020, serum samples of 495 patients were tested by Chikungunya IgM capture ELISA (National Institute of Virology, Pune, India), a manufacturers’ protocol. The cut-off value (COV) was calculated as the mean OD of the negative control plus 0.100. Samples were interpreted as negative (OD ≤ COV), equivocal (COV < OD < COV + 0.200), or positive (OD ≥ COV + 0.200). Samples with OD values in the equivocal range were excluded from further analysis.

Results

Of the 495 samples tested for CHIKV, 50 which were equivocal were excluded from the study; hence, the sample size recorded was 445.

IgM chikungunya serology was positive in 255 samples with seroprevalence rate of 57.3% (95% CI 55.9–66.0). The highest percentage of cases, 76.5%, was recorded in the year 2016 (Fig. 2).

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Figure 2.

Year-wise distribution of Chikungunya cases (2010–2020).

The maximum number of positive cases (69.6%) were observed in the age group between 45–54 years followed by 35–44 years (63.4%) and 55–64 years (62.5%) with, the least number of cases observed in the age group of 2–4 years (33.3%) (Fig. 3). The mean (±SD) age of positive cases was 30.73(±18.77) years. Seroprevalence amongst male and female was not significantly different (59.5% male and 55.5% female).

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Figure 3.

Age-wise distribution of CHIKV-infected patients (2010–2020).

The χ2 test revealed that the Chikungunya seropositive cases were significantly higher (p < 0.005) during the monsoon, and post-monsoon period (October to December). In the year 2016, greater number of cases were recorded from August to October with a decline thereafter (Fig. 4).

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Figure 4.

Month-wise positivity of CHIKV cases (2010–2020).

Results from multivariable binary logistic regression analysis indicated that the associated risk factors for Chikungunya included the post-monsoon season (OR = 3.6, 95% CI = 1.3–10.2, p = 0.01), being located in Uttar Pradesh (OR = 2.5, 95% CI = 1.0–6.0, p = 0.03), and being in the 35–44 age category (OR = 2.7, 95% CI = 1.0–7.2, p = 0.03). Chikungunya cases were significantly higher among individuals aged 35 to 44 years, during the post-monsoon season.

The East/North-East parts of Delhi share its borders with the western districts of the state of Uttar Pradesh (UP). During this one decade, a cluster of positive cases was observed in areas between 5 and 10 km from GTB Hospital. Significant number of positive cases (n = 22) were also observed in Ghaziabad, UP (Fig. 5). The distribution of positive cases in Delhi was highest in Dilshad Garden, G.T.B. enclave, and Dilshad colony. The other areas that showed a considerable number of cases were Shahdara district (n = 9) followed by Bhajanpura (n = 5), Meet Nagar, Nand Nagari and Old Mustafabad (each n = 4) during the year 2016. Least number of cases was reported from Bhagat Vihar, Brij Puri, Gagan Vihar, Shiv Vihar and Sonia Vihar.

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Figure 5.

GIS mapping of CKIKV positive cases from 2010–2020.

Discussion

In Asia, the first serological evidence of CHIKV was documented in 1954 while India documented its first outbreak in 1963 from Kolkata, West Bengal.^14,15 Since then, a series of outbreaks occurred at different time intervals.^16–19 Chikungunya fever is a major public health issue, with India as the epicentre of dispersal of CHIKV to neighbouring countries. According to the National Center for Vector Borne Disease Control (NCVBDC) in India, CHIKV is endemic in 24 states and 6 union territories. ¹⁷ Our ten-year data evaluated a significant disease burden with a major outbreak in the year 2016, reporting an estimated 9793 cases in Delhi. ²⁰ A genomic study in 2019 ²¹ reported five mutations in the E1 gene of CHIKV strains isolated during 2016–2018 outbreaks in Central India, where it was observed that E1 gene was under negative selection; hence, the virus tends to purge deleterious mutations. Previously, major CHIKF outbreaks occurred during the year 2005–2006 and 2010 in India, thus, representing a pattern of re-emergence of CHIKV as a potential outbreak causing pathogen. Mutation in the E1 gene was also observed in 2005–2006 ²² validating its evolutionary potential leading to genetic drifts over time with intermittent periods of quiescence probably leading to its adaptation to the environment and human host. GIS mapping identified frequent hotspots in densely populated areas facilitating its rapid transmission. Our study reported a higher risk of CHIKV infection in the state of Uttar Pradesh (neighbouring state of Delhi), indicating a high possibility of cross-border transmission due to daily human movement. ²³ Co-ordinated vector control strategies are therefore advised. Breeding behaviour of Aedes mosquitoes, socio-economic factors, use of water storage containers, poorly maintained air-conditioners and coolers and poor housing quality are all potential factors for the notable rise in cases of CHIKV in densely populated areas.

The 2016 Chikungunya outbreak reported an estimated 9793 cases in Delhi. ²⁰ Our study site reported 255 cases during past 10 year where the majority incidence was in 2016 (76.47%). Seasonal fluctuation of rainfall and humidity affects the vector density and vectorial capacity of Aedes mosquitoes. ^{10 20–25}

Besides the burden of CHIKV infection, the distribution pattern of vectors responsible for disease transmission is also crucial to combat the spread of this disease. ²⁶ The lack of any effective vaccine and specific pharmacotherapy means preventive measures are pivotal in controlling outbreaks. Knowledge of epidemiology and containment area are however important in handling the impending outbreaks by public health authorities. ²⁷ Hence, mapping of vector density and active case monitoring can reduce the burden of hospitalisation and morbidity.

The modest sample size of this study over a 10-year period under-represents the true community burden, which also is hampered by limited awareness and inconsistent testing. These factors lead to an under-estimation of the actual seroprevalence of CHIKV infection in East Delhi. Nevertheless, the long-term dataset provides an important indication of temporal trends and the persistence of chikungunya transmission in this region.

Conclusion

Chikungunya is considered as a self-limiting disease, but several studies have reported severe manifestations such as severe sepsis and septic shock, encephalitis or hyper-pigmentation. Sporadic occurrence of Chikungunya is confirmed by our study. Surveillance and routine vector inspection is crucial to understand the disease dynamics; an early diagnosis is utmost important for proper disease management. A sustainable surveillance programme with potential epidemic indicators is required to reduce the risk of virus transmission by generating early warnings.