BackgroundOverweight/obesity increased dramatically among Indian women since 2000. We evaluated the independent contributions of economic and nutrition context to the changing distribution of overweight/obesity among women from 1998 to 2016 across India.MethodsIndividual-level data from 473 912 ever married Indian women aged 18–49 in the National Family Health Surveys (1998–1999, 2005–2006, 2015–2016) were merged with year-matched state-level economic and nutrition context indicators. Cross-classified generalised linear mixed models were estimated to quantify associations of contextual characteristics with overweight/obesity (body mass index ≥25 kg/m2) across survey rounds.ResultsBetween 1998 and 2016, age-standardised prevalence of overweight/obesity increased from 13.9% to 27.5% nationally at an annual growth rate of 0.8%. After accounting for a woman’s age, parity and social class, the adjusted OR (aOR) for overweight/obesity was 2.02 times higher for every unit of state log per capita gross domestic product (GDP) (95% credible interval (CrI) 2.00 to 2.03). Yet, the association of state GDP with overweight/obesity generally decreased over survey round. Women in states with higher per capita daily oil (aOR 1.02 per gram; 95% CrI 1.01 to 1.03) and sugar (aOR 1.05 per gram; 95% CrI 1.04 to 1.05) consumption were more likely to be overweight/obese, while women in states with higher cereal consumption were less likely to be overweight/obese (aOR 0.93 per 10 gram; 95% CrI 0.93 to 0.93).ConclusionsIndicators of state economic development and nutrition transition were independently associated with a woman’s likelihood of being overweight/obese. The impact of state wealth waned over survey round, suggesting that risks for overweight/obesity may be increasingly shaped by individual factors as economic development expands in India.
A
bstract
Background
Confirmation of sepsis by standard blood cultures (STD) is often inconclusive due to slow growth and low positivity. Molecular diagnostics (MOL) are faster and may have higher positivity, but test performance can be inaccurately estimated if STD methods are used as comparators. Bayesian latent class models (LCMs) can evaluate diagnostic methods when there is no “gold standard.” Intensive care unit studies that have used LCMs to combine and compare STD and MOL method performance and estimate the prevalence of sepsis have not been described.
Patients and methods
Results from an ICU sepsis study that used both tests simultaneously were analyzed. Bayesian LCMs combined prior prevalence of sepsis, prior diagnostic characteristics of the two methods, and the study results to estimate the posterior prevalence and diagnostic characteristics. Sensitivity analyses were performed using objective (published studies) and subjective (expert opinion) prior parameters. Positive predictive values (PPVs) of the prevalence of sepsis were estimated for all combinations of test results.
Results
The range of posterior estimates was: sepsis prevalence (0.38–0.88), sensitivities (STD: 0.2–0.35, MOL: 0.56–0.86), and specificities (STD: 0.87–0.99, MOL: 0.72–0.95). The PPV (sepsis) of both tests being positive was (0.72–0.99).
Conclusion
LCMs combined two imperfect methods to estimate prevalence, PPV, and diagnostic characteristics. The posterior estimates (STD sensitivity < MOL and STD specificity > MOL) seem to reflect the clinical experience appropriately. The high PPV when both methods show positive results can be useful for ruling in disease.
How to cite this article
Sampath S, Baby J, Krishna B, Dendukuri N, Thomas T. Blood Cultures and Molecular Diagnostics in Intensive Care Units to Diagnose Sepsis: A Bayesian Latent Class Model Analysis. Indian J Crit Care Med 2021;25(12):1402–1407.
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