Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.

BACKGROUND: The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. DESIGN: We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. RESULTS: Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. LIMITATIONS: We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. CONCLUSIONS: Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making. IMPLICATIONS: Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. HIGHLIGHTS: The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Providers' knowledge of diagnosis and treatment of tuberculosis using vignettes: evidence from rural Bihar, India.

BACKGROUND: Almost 25% of all new cases of tuberculosis (TB) worldwide are in India, where drug resistance and low quality of care remain key challenges. METHODS: We conducted an observational, cross-sectional study of healthcare providers' knowledge of diagnosis and treatment of TB in rural Bihar, India, from June to September 2012. Using data from vignette-based interviews with 395 most commonly visited healthcare providers in study areas, we scored providers' knowledge and used multivariable regression models to examine their relationship to providers' characteristics. FINDINGS: 80% of 395 providers had no formal medical qualifications. Overall, providers demonstrated low levels of knowledge: 64.9% (95% CI 59.8% to 69.8%) diagnosed correctly, and 21.7% (CI 16.8% to 27.1%) recommended correct treatment. Providers seldom asked diagnostic questions such as fever (31.4%, CI 26.8% to 36.2%) and bloody sputum (11.1%, CI 8.2% to 14.7%), or results from sputum microscopy (20.0%, CI: 16.2% to 24.3%). After controlling for whether providers treat TB, MBBS providers were not significantly different, from unqualified providers or those with alternative medical qualifications, on knowledge score or offering correct treatment. MBBS providers were, however, more likely to recommend referrals relative to complementary medicine and unqualified providers (23.2 and 37.7 percentage points, respectively). INTERPRETATION: Healthcare providers in rural areas in Bihar, India, have low levels of knowledge regarding TB diagnosis and treatment. Our findings highlight the need for policies to improve training, incentives, task shifting and regulation to improve knowledge and performance of existing providers. Further, more research is needed on the incentives providers face and the role of information on quality to help patients select providers who offer higher quality care.

Palm oil taxes and cardiovascular disease mortality in India: economic-epidemiologic model.

OBJECTIVE: To examine the potential effect of a tax on palm oil on hyperlipidemia and on mortality due to cardiovascular disease in India. DESIGN: Economic-epidemiologic model. MODELING METHODS: A microsimulation model of mortality due to myocardial infarction and stroke among Indian populations was constructed, incorporating nationally representative data on systolic blood pressure, total cholesterol, tobacco smoking, diabetes, and cardiovascular event history, and stratified by age, sex, and urban/rural residence. Household expenditure data were used to estimate the change in consumption of palm oil following changes in oil price and the potential substitution of alternative oils that might occur after imposition of a tax. A 20% excise tax on palm oil purchases was simulated over the period 2014-23. MAIN OUTCOME MEASURES: The model was used to project future mortality due to myocardial infarction and stroke, as well as the potential effect of a tax on food insecurity, accounting for the effect of increased food prices. RESULTS: A 20% tax on palm oil purchases would be expected to avert approximately 363,000 (95% confidence interval 247,000 to 479,000) deaths from myocardial infarctions and strokes over the period 2014-23 in India (1.3% reduction in cardiovascular deaths) if people do not substitute other oils for reduced palm oil consumption. Given estimates of substitution of palm oil with other oils following a 20% price increase for palm oil, the beneficial effects of increased polyunsaturated fat consumption would be expected to enhance the projected reduction in deaths to as much as 421,000 (256,000 to 586,000). The tax would be expected to benefit men more than women and urban populations more than rural populations, given differential consumption and cardiovascular risk. In a scenario incorporating the effect of taxation on overall food expenditures, the tax may increase food insecurity by <1%, resulting in 16,000 (95% confidence interval 12,000 to 22,000) deaths. CONCLUSIONS: Curtailing palm oil intake through taxation may modestly reduce hyperlipidemia and cardiovascular mortality, but with potential distributional consequences differentially benefiting male and urban populations, as well as affecting food security.