BackgroundGlobally, the routinely used case-based reporting and IgG serosurveys underestimate the actual prevalence of COVID-19. Simultaneous estimation of IgG antibodies and active SARS-CoV-2 markers can provide a more accurate estimation.MethodsA cross-sectional survey of 16416 people covering all risk groups was done between 3-16 September 2020 using the state of Karnataka’s infrastructure of 290 hospitals across all 30 districts. 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, while multinomial regression identified predictors.FindingsThe overall adjusted prevalence of COVID-19 in Karnataka was 27 ·3% (95% CI: 25 ·7-28 ·9), including IgG 16 ·4% (95% CI: 15 ·1 - 17 ·7) and active infection 12 ·7% (95% CI: 11 ·5-13 ·9). 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 are good predictors of active infection. The RAT kits had higher sensitivity (68%) in symptomatic participants compared to 47% in asymptomatic.InterpretationThis is the first comprehensive survey providing accurate estimates of the COVID-19 burden anywhere in the world. Further, our findings provide a reasonable approximation of population immunity threshold levels. Using the RAT kits and following the syndromic approach can be useful in screening and monitoring COVID-19. Leveraging existing surveillance platforms, coupled with appropriate methods and sampling framework, renders our model replicable in other settings.

Background: With increase in life expectancy, adoption of newer lifestyles and screening using prostate specific antigen (PSA), the incidence of prostate cancer is on rise. Globally prostate cancer is the second most frequently diagnosed cancer and sixth leading cause of cancer death in men. The present communication makes an attempt to analyze the time trends in incidence for different age groups of the Indian population reported in different Indian registries using relative difference and regression approaches. Materials and Methods: The data published in Cancer Incidence in Five Continents for various Indian registries for different periods and/or publications by the individual registries served as the source materials. Trends were estimated by computing the mean annual percentage change (MAPC) in the incidence rates using the relative difference between two time periods (latest and oldest) and also by estimation of annual percentage change (EAPC) by the Poisson regression model. Results: Age adjusted incidence rates (AAR) of prostate cancer for the period 2005-2008 ranged from 0.8 (Manipur state excluding Imphal west) to 10.9 (Delhi) per 10 5 person-years. Age specific incidence rates (ASIR) increased in all PBCRs especially after 55 years showing a peak incidence at +65 years clearly indicating that prostate cancer is a cancer of the elderly. MAPC in crude incidence rate(CR) ranged from 0.14 (Ahmedabad) to 8.6 (Chennai) . Chennai also recorded the highest MAPC of 5.66 in ASIR in the age group of 65+. Estimated annual percentage change (EAPC) in the AAR ranged from 0.8-5.8 among the three registries. Increase in trend was seen in the 55-64 year age group cohort in many registries and in the 35-44 age group in Metropolitan cities such as Delhi and Mumbai. Conclusions: Several Indian registries have revealed an increasing trend in the incidence of prostate cancer and the mean annual percentage change has ranged from 0.14-8.6.

Objective To estimate the burden of active infection and anti-SARS-CoV-2 IgG antibodies in Karnataka state jointly and to assess variation across geographical regions and risk groups. Methods A cross-sectional survey of 16416 people covering three risk groups was done 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 were 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 Overall adjusted total prevalence of COVID-19 in Karnataka was 27.7% (95% CI: 26.1 to 29.3), IgG 16.8% (15.5 to 18.1) and active infection fraction 12.6% (11.5 to 13.8). Case-to-infection ratio 1:40, and infection fatality rate 0.05%. Influenza-like-symptoms or contact with COVID-19 positive patient are good predictors of active infection. RAT kits had higher sensitivity (68%) in symptomatics compared to 47% asymptomatic. Conclusion Our sentinel-based population survey is the first comprehensive survey to provide accurate estimates of the COVID-19 burden. Our findings provide a reasonable approximation of the population immunity threshold levels. Leveraging existing surveillance platforms, coupled with syndromic approach and sampling framework, renders our model replicable.

Surveillance is critical for interrupting transmission of global epidemics. Research has highlighted gaps in the surveillance for tuberculosis that range from failure to collect real-time data to lack of standardization of data for informed decision-making at different levels of the health system. Our research aims to advance conceptual and methodological foundations for the development of a learning surveillance system for Tuberculosis, that involves systematic collection, analysis, interpretation, and feedback of outcome-specific data. It would concurrently involve the health care delivery system, public health laboratory, and epidemiologists. For our study, we systemically framed the cyber environment of TB surveillance as an ontology of the learning surveillance system. We validated the ontology by binary coding of dimensions and elements of the ontology with the metadata from an existing surveillance platform—GPMS TB Transportal. Results show GPMS TB Transportal collects a critical range of data for active case investigation and presumptive case screening for identifying and detecting confirmed TB cases. It is therefore targeted at assisting the Active Case Finding program. Building on the results, we demonstrate enhanced surveillance strategies for GPMS that are enumerated as pathways in the ontology. Our analysis reveals the scope for embedding learning surveillance pathways for digital applications in Direct Benefit Transfer, and Drug Resistance Treatment in National TB Elimination Programme in India. We discuss the possibilities of developing the transportal into a multi-level computer-aided decision support system for TB, using the innumerable pathways encapsulated in the ontology.

Introduction: The increasing ageing population of India has unique challenges due to changing social structure, health issues and inaccessible healthcare facilities. These challenges can adversely affect the quality of life (QOL) of older persons. Hence, this study was undertaken with the objective of assessing the QOL among older persons in an urban and rural area of Bangalore. Materials and Methods: Cross-sectional study was done among 977 older persons 60 years and above. Census enumeration blocks in urban areas and villages in rural areas were randomly selected and all older persons meeting the inclusion criteria were administered the WHOQOL-Bref questionnaire. Results: Mean QOL scores (SD) in the physical, psychological, social relationship and environmental domains were 50.5 (5.5), 49.2 (5.5), 49.4 (6.5) and 49.3 (5.1) in rural areas and 57.4 (8.9), 58.6 (8.8), 64.6 (10.8) and 60.0 (9.4) in urban areas, respectively. Compared to urban, rural older persons uniformly have lower QOL irrespective of sex, education or financial dependence. Conclusion: Inequitable health resource distribution and inadequate social support systems must be addressed to improve the QOL of older persons, especially in rural areas. Primary care providing essential services can bridge this urban–rural divide and improve QOL of older persons.

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Introduction: Diagnosis of extrapulmonary tuberculosis (EPTB) has been challenging owing to its paucibacillary nature and diverse clinical manifestations. Immunohistochemistry (IHC) on biopsy specimens has presented a new perspective toward improving tuberculosis diagnosis. MPT64 is a unique antigen that has shown high sensitivity and specificity compared to other conventional techniques in its ability to diagnose tuberculosis as well as differentiate it from nontubercular mycobacteria. In this study, we aimed to analyze the utility of anti-MPT64 in the diagnosis of EPTB. Methods: In this cross-sectional study, conducted over a period of 1 year, 52 nonrepetitive samples from 52 participants with a presumptive diagnosis of EPTB were collected and processed. The specimens were subjected to Ziehl–Neelsen staining, GeneXpert, tissue culture by mycobacterium growth indicator tube, H and E staining, and IHC with anti-MPT64. The sensitivity and specificity of anti-MPT64 was computed against a composite diagnostic criterion. Results: Fifty-two consecutive participants satisfying the study criteria were recruited. The mean age of the study population was 37.35 ± 18.71 years. Lymph node specimen accounted for majority of the specimen processed ( n = 20, 38.5%). The sensitivity of anti-MPT64 in the diagnosis of EPTB was 68.29%, specificity was 90.90%, positive predictive value was 96.55%, and negative predictive value was 43.47%, when composite criteria were considered standard for diagnosis. Conclusion: Immunohistochemical staining by anti-MPT64 is useful in establishing microbiological diagnosis of EPTB on biopsy specimens.