Resurgence of Diphtheria in North Kerala, India, 2016: Laboratory Supported Case-Based Surveillance Outcomes

IntroductionAs part of national program, laboratory supported vaccine preventable diseases surveillance was initiated in Kerala in 2015. Mechanisms have been strengthened for case investigation, reporting, and data management. Specimens collected and sent to state and reference laboratories for confirmation and molecular surveillance. The major objective of this study is to understand the epidemiological information generated through surveillance system and its utilization for action.MethodsSurveillance data captured from reporting register, case investigation forms, and laboratory reports was analyzed. Cases were allotted unique ID and no personal identifying information was used for analysis. Throat swabs were collected from investigated cases as part of surveillance system. All Corynebacterium diphtheriae isolates were confirmed with standard biochemical tests, ELEK’s test, and real-time PCR. Isolates were characterized using whole genome-based multi locus sequence typing method. Case investigation forms and laboratory results were recorded electronically. Public health response by government was also reviewed.ResultsA total of 533 cases were identified in 11 districts of Kerala in 2016, of which 92% occurred in 3 districts of north Kerala; Malappuram, Kozhikode, and Kannur. Almost 79% cases occurred in >10 years age group. In <18 years age group, 62% were male while in ≥18 years, 69% were females. In <10 years age group, 31% children had received three doses of diphtheria vaccine, whereas in ≥10 years, 3% cases had received all doses. Fifteen toxigenic C. diphtheriae isolates represented 6 novel sequence types (STs) (ST-405, ST-408, ST-466, ST-468, ST-469, and ST-470). Other STs observed are ST-50, ST-295, and ST-377.ConclusionDiphtheria being an emerging pathogen, establishing quality surveillance for providing real-time information on disease occurrence and mortality is imperative. The epidemiological data thus generated was used for targeted interventions and to formulate vaccine policies. The data on molecular surveillance have given an insight on strain variation and transmission patterns

The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: Results from a statewide sentinel-based population survey in Karnataka, India

Objective: To estimate the burden of active infection and anti-SARS-CoV-2 IgG antibodies in Karnataka, India, and to assess variation across geographical regions and risk groups. Methods: A cross-sectional survey of 16,416 people covering three risk groups was conducted between 3–16 September 2020 using the state of Karnataka’s infrastructure of 290 healthcare facilities across all 30 districts. Participants were further classified into risk subgroups and sampled using stratified sampling. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT) and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active and total prevalence (either IgG or active or both), while multinomial regression identified predictors. Results: The overall adjusted total prevalence of COVID-19 in Karnataka was 27.7% (95% CI 26.1–29.3), IgG 16.8% (15.5–18.1) and active infection fraction 12.6% (11.5–13.8). The case-to-infection ratio was 1:40 and the infection fatality rate was 0.05%. Influenza-like symptoms or contact with a COVID-19-positive patient were good predictors of active infection. RAT kits had higher sensitivity (68%) in symptomatic people compared with 47% in asymptomatic people. Conclusion: This sentinel-based population survey was the first comprehensive survey in India to provide accurate estimates of the COVID-19 burden. The findings provide a reasonable approximation of the population immunity threshold levels. Using existing surveillance platforms coupled with a syndromic approach and sampling framework enabled this model to be replicable