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Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India's second COVID wave.

ABSTRACT: Despite an urgent need, authorities in many countries are struggling to track COVID vaccine effectiveness (VE) because standard VE measures cannot be calculated from their public health data. Here, we use regression discontinuity design (RDD) to estimate VE, motivated by such limitations in public health records from West Bengal, India. These data cover 8,755,414 COVID vaccinations (90% ChAdOx1 NCov-19, almost all first doses, until May 2021), 8,179,635 tests, and 141,800 hospitalizations. The standard RDD exploits age-based vaccine eligibility; we also introduce a new RDD-based VE measure that improves on the standard one when better data are available. Applying these measures, we find a VE of 55.2% (95% confidence interval: 44.5 to 65.0%) against symptomatic disease, 80.1% (63.3 to 88.8%) against hospitalizations, and 85.5% (24.8 to 99.2%) against intensive care/critical care/high dependency admissions or deaths. Other data-deficient countries with age-based eligibility for any vaccine-and not just COVID vaccines-can also use these easy-to-implement measures to inform their own immunization policies.

SUBMITTER: Mukherjee A

PROVIDER: S-EPMC9075799 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India's second COVID wave.

Mukherjee Abhiroop A Panayotov George G Sen Rik R Dutta Harsha H Ghosh Pulak P

Science advances 20220506 18

Despite an urgent need, authorities in many countries are struggling to track COVID vaccine effectiveness (VE) because standard VE measures cannot be calculated from their public health data. Here, we use regression discontinuity design (RDD) to estimate VE, motivated by such limitations in public health records from West Bengal, India. These data cover 8,755,414 COVID vaccinations (90% ChAdOx1 NCov-19, almost all first doses, until May 2021), 8,179,635 tests, and 141,800 hospitalizations. The s ...[more]

PMID: 35522748

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Project description:BACKGROUND:With India preparing for the next decennial Census in 2021, we compared the disability estimates and data collection methodology between the Census 2011 and the most recent population-level survey for India and its states, to highlight the issues to be addressed to improve robustness of the disability estimates in the upcoming Census. METHODS:Data from the Census 2011 and from two complementary nationally representative household surveys that covered all Indian states with the same methodology and survey instruments-the District-Level Household Survey-4 (DLHS-4, 2012-2013) and the Annual Health Surveys (AHS three rounds, 2010-11, 2011-12 and 2012-13) were used. Data from DLHS-4 and AHS 2012-13 round were pooled to generate estimates for the year 2012-13. Data collection methodology between the sources was compared, including the review of definitions of each type of disability. The overall, mental, visual, hearing, speech, and movement disability rate (DR) per 100,000 population were compared between the sources for India and for each state, and the percent difference in the respective rates was calculated. We explored the reliability of these estimates comparing yearly data from the AHS for three successive rounds. RESULTS:Survey data were collected through proxy reporting, however, it is not entirely clear whether the data were proxy- or self-reported or a mix of both in the Census. The overall DR was 25.1% higher in the Census (2,242; 95% CI 2,241-2,243) than the survey (1,791; 95% CI 1,786-1,797) per 100,000 population, with the state-level difference ranging from -64% in Tamil Nadu to 107% in Sikkim state. Despite both sources using nearly similar definitions for overall disability and disability by type, the difference in DR was 125.5%, 54.2%, -25.7%, -19.7%, and 21.9% for hearing, speech, mental, movement, and visual DR, respectively. At the state-level, the difference in disability-specific estimates ranged from -84% to 450%. The extent of variations in the disability-specific estimates in AHS successive rounds ranged from -25% to 929% at the state-level. CONCLUSIONS:There is momentum globally towards building disability measurement that is consistent with the data required for monitoring of the Sustainable Development Goals to ensure robust estimation of disability. The current estimates from the Census and surveys seem much lower than would be expected at the population level. We make recommendations that India needs to take serious note of in order to improve the validity and reliability of India's disability estimates.

Project description:BackgroundTruenat is a novel molecular assay that rapidly detects tuberculosis (TB) and rifampicin-resistance. Due to the portability of its battery-powered testing platform, it may be valuable in peripheral healthcare settings in India.MethodsUsing a microsimulation model, we compared four TB diagnostic strategies for HIV-negative adults with presumptive TB: (1) sputum smear microscopy in designated microscopy centers (DMCs) (SSM); (2) Xpert MTB/RIF in DMCs (Xpert); (3) Truenat in DMCs (Truenat DMC); and (4) Truenat for point-of-care testing in primary healthcare facilities (Truenat POC). We projected life expectancy, costs, incremental cost-effectiveness ratios (ICERs), and 5-year budget impact of deploying Truenat POC in India's public sector. We defined a strategy "cost-effective" if its ICER was <US$990/year-of-life saved (YLS). Model inputs included: TB prevalence, 15% (among those not previously treated for TB) and 27% (among those previously treated for TB); sensitivity for TB detection, 89% (Xpert) and 86% (Truenat); per test cost, $12.63 (Xpert) and $13.20 (Truenat); and linkage-to-care after diagnosis, 84% (DMC) and 95% (POC). We varied these parameters in sensitivity analyses.ResultsCompared to SSM, Truenat POC increased life expectancy by 0.39 years and was cost-effective (ICER $210/YLS). Compared to Xpert, Truenat POC increased life expectancy by 0.08 years due to improved linkage-to-care and was cost-effective (ICER $120/YLS). In sensitivity analysis, the cost-effectiveness of Truenat POC, relative to Xpert, depended on the diagnostic sensitivity of Truenat and linkage-to-care with Truenat. Deploying Truenat POC instead of Xpert increased 5-year expenditures by $270 million, due mostly to treatment costs. Limitations of our study include uncertainty in Truenat's sensitivity for TB and not accounting for the "start-up" costs of implementing Truenat in the field.ConclusionsUsed at the point-of-care in India, Truenat for TB diagnosis should improve linkage-to-care, increase life expectancy, and be cost-effective compared with smear microscopy or Xpert.

Project description:Background Failing to account for the resources required to successfully implement public health interventions can lead to an underestimation of costs and budget impact, optimistic cost-effectiveness estimates, and ultimately a disconnect between published evidence and public health decision-making. Methods We developed a conceptual framework for assessing implementation costs. We illustrate the use of this framework with case studies involving interventions for tuberculosis and HIV/AIDS in resource-limited settings. Results Costs of implementing public health interventions may be conceptualized as occurring across three phases: design, initiation, and maintenance. In the design phase, activities include developing intervention components and establishing necessary infrastructure (e.g., technology, standard operating procedures). Initiation phase activities include training, initiation of supply chains and quality assurance procedures, and installation of equipment. Implementation costs in the maintenance phase include ongoing technical support, monitoring and evaluation, and troubleshooting unexpected obstacles. Within each phase, implementation costs can be incurred at the site of delivery (“site-specific” costs) or more centrally (“above-service” or “central” costs). For interventions evaluated in the context of research studies, implementation costs should be classified as programmatic, research-related, or shared research/program costs. Purely research-related costs are often excluded from analysis of programmatic implementation. Conclusions In evaluating public health interventions in resource-limited settings, accounting for implementation costs enables more realistic estimates of budget impact and cost-effectiveness and provides important insights into program feasibility, scale-up, and sustainability. Assessment of implementation costs should be planned prospectively and performed in a standardized manner to ensure generalizability.

Project description:There is a significant challenge in responding to second waves of COVID-19 cases, with governments being hesitant in introducing hard lockdown measures given the resulting economic impact. In addition, rising case numbers reflect an increase in coronavirus transmission some time previously, so timing of response measures is highly important. Australia experienced a second wave from June 2020 onwards, confined to greater Melbourne, with initial social distancing measures failing to reduce rapidly increasing case numbers. We conducted a detailed analysis of this outbreak, together with an evaluation of the effectiveness of alternative response strategies, to provide guidance to countries experiencing second waves of SARS-Cov-2 transmission. An individual-based transmission model was used to (1) describe a second-wave COVID-19 epidemic in Australia; (2) evaluate the impact of lockdown strategies used; and (3) evaluate effectiveness of alternative mitigation strategies. The model was calibrated using daily diagnosed case data prior to lockdown. Specific social distancing interventions were modelled by adjusting person-to-person contacts in mixing locations. Modelling earlier activation of lockdown measures are predicted to reduce total case numbers by more than 50%. Epidemic peaks and duration of the second wave were also shown to reduce. Our results suggest that activating lockdown measures when second-wave case numbers first indicated exponential growth, would have been highly effective in reducing COVID-19 cases. The model was shown to realistically predict the epidemic growth rate under the social distancing measures applied, validating the methods applied. The timing of social distancing activation is shown to be critical to their effectiveness. Data showing exponential rise in cases, doubling every 7-10 days, can be used to trigger early lockdown measures. Such measures are shown to be necessary to reduce daily and total case numbers, and the consequential health burden, so preventing health care facilities being overwhelmed. Early control of second wave resurgence potentially permits strict lockdown measures to be eased earlier.

Project description:The online public food environment (OPFE) has had a considerable impact on people's lifestyles over the past decade; however, research on its exposure is sparse. The results of the existing research on the impact of the food environment on human health are inconsistent. In response to the lack of food elements in the definition of the food environment and the lack of a clear method to assess the health attributes and the impact degree of the food environment, we proposed a new analytical framework based on the latest disease burden research, combining the characteristics of China's current food environment, from the perspective of environmental science. We redefined the food environment and proposed that food and its physical space are two core elements of the food environment. Accordingly, we extracted four domains of characteristics to describe the basic components of the food environment. Using the sales records, we designed an approach by referring to the standard process of environmental health indicators, including the health attributes and the impact degree of the food environment, to measure the OPFE of takeaway food outlets. Further, we conducted a case study and extracted three domains of characteristics for more than 18,000 effective takeaway meals from 812 takeaway food outlets located in 10 administrative subdivisions in the Haidian District and Xicheng District of Beijing Municipality. The results showed that more than 60% of single meals sold by takeaway food outlets were considered as healthy, and only 15% of takeaway food outlets sold healthy meals exclusively. Additionally, there were significant differences in health effects among different types of food environments, and high-risk areas of different types of food environments can be spatially identified. Compared with the counting method in the availability of food environment, the proposed new approach can depict food environment characteristics not only in the macro-scale like the counting method but also in the meal-scale. The indicators could be useful for large-scale and long-term monitoring of food environmental changes due to their simple calculation and design depending on the food delivery platform.

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Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India's second COVID wave.

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Measuring vaccine effectiveness from limited public health datasets: Framework and estimates from India's second COVID wave.

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