prevalence estimation
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2022 ◽  
Vol 7 ◽  
pp. 100131
Author(s):  
Jancy Andrea Huertas-Quintero ◽  
Natalia Losada-Trujillo ◽  
Diego Alberto Cuellar-Ortiz ◽  
Harvy Mauricio Velasco-Parra

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shu Ki Tsoi ◽  
Susanna J. Lake ◽  
Li Jun Thean ◽  
Alexander Matthews ◽  
Oliver Sokana ◽  
...  

Abstract Background Scabies causes considerable morbidity in disadvantaged populations. The International Alliance for the Control of Scabies (IACS) published consensus criteria in 2020 to standardize scabies diagnosis. However, these criteria are complex, and a WHO informal consultation proposed simplified criteria for mapping, to identify regions of high prevalence as targets for mass drug administration. We aimed to investigate the accuracy of simplified criteria in determining scabies prevalence, compared to the 2020 IACS criteria. Methods We obtained data relating to demographics, relevant history and skin lesions from all-age prevalence surveys from Fiji (n = 3365) and Solomon Islands (n = 5239), as well as school-aged children in Timor-Leste (n = 1043). We calculated prevalence using the 2020 IACS criteria and simplified criteria and compared these disease estimates. Results There was no significant difference in the pooled prevalence using the two methods (2020 IACS criteria: 16.6%; simplified criteria: 15.6%; difference = 0.9, [95% CI -0.1, 2.0]). In Timor-Leste, the prevalence using simplified criteria was lower (26.5% vs 33.8%). Simplified criteria had a sensitivity of 82.3% (95% CI 80.2, 84.2) and specificity of 97.6% (95% CI 97.2, 97.9) compared to the 2020 IACS criteria. Conclusions The scabies prevalence estimation using simplified criteria was similar to using the 2020 IACS criteria in high prevalence, tropical countries. The prevalence estimation was lower in the school-based survey in Timor-Leste. Mapping using simplified criteria may be a feasible and effective public health tool to identify priority regions for scabies control. Further work assessing use of simplified criteria for mapping in a field setting should be conducted.


2021 ◽  
Author(s):  
Konstantinos Pateras

Abstract Background: Tests have false positive or false negative results, which, if not properly accounted for, may provide misleading apparent prevalence estimates based on the observed rate of positive tests and not the true disease prevalence estimates. Methods to estimate the true prevalence of disease, adjusting for the sensitivity and the specificity of the diagnostic tests are available and can be applied, though, such procedures can be cumbersome to researchers with or without a solid statistical background.Objective: To create a web-based application that integrates statistical methods for Bayesian inference of true disease prevalence based on prior elicitation for the accuracy of the diagnostic tests. This tool allows practitioners to simultaneously analyse and visualize results while using interactive sliders and output prior/posterior plots.Methods: Three methods for prevalence prior elicitation and four core families of Bayesian methods have been combined and incorporated in this web tool. |tPRiors| user interface has been developed with R and Shiny and may be freely accessed on-line.Results: |tPRiors| allows researchers to use preloaded data or upload their own datasets and perform analysis on either single or multiple population groups clusters), allowing, if needed, for excess zero prevalence. The final report is exported in raw parts either as .rdata or .png files. We utilize a real multiple-population and a toy single-population dataset to demonstrate the robustness and capabilities of |tPRiors|.Conclusions: We expect |tPRiors| to be helpful for researchers interested in true disease prevalence estimation and they are keen on accounting for prior information. |tPRiors| acts both as a statistical tool and a simplified step-by-step statistical framework that facilitates the use of complex Bayesian methods. The application of |tPRiors| is expected to aid standardization of practices in the field of Bayesian modelling on subject and multiple group-based true prevalence estimation.


2021 ◽  
pp. 183-210
Author(s):  
Jonathan D. Rosenblatt

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258705
Author(s):  
Pavel Dietz ◽  
Anne Quermann ◽  
Mireille Nicoline Maria van Poppel ◽  
Heiko Striegel ◽  
Hannes Schröter ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
pp. 18-29
Author(s):  
Rares Traian BUSTEAN ◽  
Adrian HATOS

Drug use and New Psychoactive Substances (NPS) use are a sensitive issue due to the consequences that those who admit having this habit may expect to suffer. Part of the scientific community recommends that quantitative descriptions on this topic be verified or possibly supplemented by data obtained from research activities that use more reliable data and methods. In Romania, the prevalence estimation of drug and NPS use is performed by the National Anti – Drug Agency by conducting two studies, one every three years and another conducted every four years, respectively. In order to analyze the accuracy of the results presented in the reports prepared by the National Anti – Drug Agency, in this article we compared these results with those resulted from the analysis of the indictments issued by the Directorate for Investigation of Organized Crime and Terrorism – Oradea Territorial Service in 2013 – 2019. As a result of this comparison, it was found that there are certain significant differences between the information contained in the two data.


2021 ◽  
Vol 12 ◽  
Author(s):  
Shalini Quadros ◽  
Shalini Garg ◽  
Rupesh Ranjan ◽  
Guruprasad Vijayasarathi ◽  
Mohammed A. Mamun

Background: Psychological stressors like panic, fear, phobia, etc., are being substantially reported during the COVID-19 outbreak. In the prior outbreaks, fear of being infected was reported as the prominent suicide stressor. Therefore, fear of infection has become a concern in the context of the COVID-19 pandemic because it worsens emotion, cognition, and behavioral responses. Understanding the extent of fear of COVID-19 infection in various cohorts would aid in gauging the mental health services, which was a remedy in the present review.Methods: Adhering to Arksey and O'Malley's framework for conducting a scoping review, a systematic search was performed in the month of September 2020 in several databases, including Scopus, PubMed, Web of Science, etc. Considering the inclusion criteria, a total of 14 articles were included in the present review.Results: All of the included studies were conducted via online platforms, whereas all but one of the studies were cross-sectional in nature (including a mixed-method study, and a comparative study). Most of the studies were conducted among the general population (n = 12), within March and May 2020 (n = 9), from Asian countries (n = 7), and considered a self-developed item for fear of COVID-19 assessment (n = 8; whereas the Fear of COVID-19 Scale was used in 6-studies). The prevalence of fear of COVID-19 was reported to be 18.1–45.2%, although no cutoff point or criteria was mentioned for such a prevalence estimation of the Fear of COVID-19 Scale. However, females, younger adults, urban residents, divorcees, healthcare workers, those in quarantine settings, those in suspicion of being infected, and those with mental health problems, etc., were found to be at an increased risk of COVID-19 fear.Conclusions: Being one of the first reviews in this context, the findings are anticipated to be helpful to predict the possible solutions for reducing fear of COVID-19 and facilitate further studies on strategies of how to alleviate such a stressful situation.


Metrika ◽  
2021 ◽  
Author(s):  
Joscha Krause ◽  
Jan Pablo Burgard ◽  
Domingo Morales

AbstractRegional prevalence estimation requires the use of suitable statistical methods on epidemiologic data with substantial local detail. Small area estimation with medical treatment records as covariates marks a promising combination for this purpose. However, medical routine data often has strong internal correlation due to diagnosis-related grouping in the records. Depending on the strength of the correlation, the space spanned by the covariates can become rank-deficient. In this case, prevalence estimates suffer from unacceptable uncertainty as the individual contributions of the covariates to the model cannot be identified properly. We propose an area-level logit mixed model for regional prevalence estimation with a new fitting algorithm to solve this problem. We extend the Laplace approximation to the log-likelihood by an $$\ell _2$$ ℓ 2 -penalty in order to stabilize the estimation process in the presence of covariate rank-deficiency. Empirical best predictors under the model and a parametric bootstrap for mean squared error estimation are presented. A Monte Carlo simulation study is conducted to evaluate the properties of our methodology in a controlled environment. We further provide an empirical application where the district-level prevalence of multiple sclerosis in Germany is estimated using health insurance records.


2021 ◽  
Author(s):  
Mario Morvan ◽  
Anna Lo Jacomo ◽  
Celia Souque ◽  
Matthew Wade ◽  
Till Hoffmann ◽  
...  

Abstract Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater (WW) can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Data from 44 sewage sites in England, covering 31% of the population, shows that SARS-CoV-2 prevalence is estimated to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, differences between sampled sites, particularly the WW flow rate, influence prevalence estimation and require careful interpretation. SARS-CoV-2 signals in WW appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that WW surveillance can be a leading indicator for asymptomatic viral infections. Wastewater-based epidemiology complements and strengthens traditional surveillance, with significant implications for public health.


2021 ◽  
pp. 001112872110298
Author(s):  
Sheldon X. Zhang ◽  
Jacqueline Joudo Larsen

The prevalence of human trafficking has remained as elusive as the method of producing its estimation is contested. There are significant variations in the way prevalence estimation is produced, with some methods garnering more attention than others. To complicate the issue further, the hidden nature of human trafficking makes it difficult to apply conventional probability-based sampling strategies, without which for reference purposes one cannot easily assess the merits of alternative estimation techniques. This special issue represents the most recent development and applications of one particular method, the multiple systems estimation (MSE) method. Although we remain biased towards primary data for prevalence estimation, MSE represents a cost-effective alternative for the purposes of advocacy, policymaking, and victim services.


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