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5 results for “Disease prevalence estimates”
A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
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Data from: A new way to estimate neurologic disease prevalence in the United States
Objective: Considerable gaps exist in knowledge regarding the prevalence of neurologic diseases, such as multiple sclerosis (MS), in the United States. Therefore, the MS Prevalence Working Group sought to review and evaluate alternative methods for obtaining a scientifically valid estimate of national MS prevalence in the current health care era. Methods: We carried out a strengths, weaknesses, opportunities, and threats (SWOT) analysis for 3 approaches to estimate MS prevalence: population-based MS registries, national probability health surveys, and analysis of administrative health claims databases. We reviewed MS prevalence studies conducted in the United States and critically examined possible methods for estimating national MS prevalence. Results: We developed a new 4-step approach for estimating MS prevalence in the United States. First, identify administrative health claim databases covering publicly and privately insured populations in the United States. Second, develop and validate a highly accurate MS case-finding algorithm that can be standardly applied in all databases. Third, apply a case definition algorithm to estimate MS prevalence in each population. Fourth, combine MS prevalence estimates into a single estimate of US prevalence, weighted according to the number of insured persons in each health insurance segment. Conclusions: By addressing methodologic challenges and proposing a new approach for measuring the prevalence of MS in the United States, we hope that our work will benefit scientists who study neurologic and other chronic conditions for which national prevalence estimates do not exist.
Estimating the Prevalence of Postpartum Anxiety and Depression in the Context of the Coronavirus Disease (COVID-19) Pandemic
ClinicalTrials.gov study NCT04852757. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: A new way to estimate neurologic disease prevalence in the United States
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