scholarly journals Prevalence estimation subject to misclassification: the mis-substitution bias and some remedies

2014 ◽  
Vol 33 (25) ◽  
pp. 4482-4500 ◽  
Author(s):  
Zhiwei Zhang ◽  
Chunling Liu ◽  
Sungduk Kim ◽  
Aiyi Liu
Keyword(s):  
Prevalence Estimation ◽  
Substitution Bias

The net environmental impact of online shopping, beyond the substitution bias

2021 ◽  
Vol 93 ◽  
pp. 103058
Author(s):  
Heleen Buldeo Rai
Keyword(s):  
Environmental Impact ◽  
Online Shopping ◽  
Substitution Bias

Estimating the Size of the Human Trafficking Problem: MSE and Other Strategies

2021 ◽  
pp. 001112872110298
Author(s):  
Sheldon X. Zhang ◽  
Jacqueline Joudo Larsen
Keyword(s):  
Human Trafficking ◽  
Cost Effective ◽  
Primary Data ◽  
Sampling Strategies ◽  
Special Issue ◽  
Multiple Systems ◽  
Estimation Techniques ◽  
Prevalence Estimation ◽  
Victim Services ◽  
Cost Effective Alternative

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.

scholarly journals $$\ell _2$$-penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency

Metrika ◽  
2021 ◽  
Author(s):  
Joscha Krause ◽  
Jan Pablo Burgard ◽  
Domingo Morales
Keyword(s):  
Mixed Model ◽  
Mean Squared Error ◽  
Routine Data ◽  
Parametric Bootstrap ◽  
Monte Carlo Simulation Study ◽  
Rank Deficiency ◽  
Prevalence Estimation ◽  
Fitting Algorithm ◽  
Individual Contributions ◽  
The Individual

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.

Bayesian non-parametric models for regional prevalence estimation

2008 ◽  
Vol 35 (5) ◽  
pp. 567-582 ◽  
Author(s):  
Adam J. Branscum ◽  
Timothy E. Hanson ◽  
Ian A. Gardner
Keyword(s):  
Parametric Models ◽  
Prevalence Estimation ◽  
Non Parametric

scholarly journals Leptin receptor deficiency: a systematic literature review and prevalence estimation based on population genetics

2020 ◽  
Vol 182 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Lotte Kleinendorst ◽  
Ozair Abawi ◽  
Hetty J van der Kamp ◽  
Mariëlle Alders ◽  
Hanne E J Meijers-Heijboer ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Early Onset ◽  
Autosomal Recessive ◽  
Leptin Receptor ◽  
Pituitary Hormone ◽  
Sequencing Data ◽  
Prevalence Estimation ◽  
Calculation Results ◽  
Number Of Individuals

Objective Leptin receptor (LepR) deficiency is an autosomal-recessive endocrine disorder causing early-onset severe obesity, hyperphagia and pituitary hormone deficiencies. As effective pharmacological treatment has recently been developed, diagnosing LepR deficiency is urgent. However, recognition is challenging and prevalence is unknown. We aim to elucidate the clinical spectrum and to estimate the prevalence of LepR deficiency in Europe. Design Comprehensive epidemiologic analysis and systematic literature review. Methods We curated a list of LEPR variants described in patients and elaborately evaluated their phenotypes. Subsequently, we extracted allele frequencies from the Genome Aggregation Database (gnomAD), consisting of sequencing data of 77 165 European individuals. We then calculated the number of individuals with biallelic disease-causing LEPR variants. Results Worldwide, 86 patients with LepR deficiency are published. We add two new patients, bringing the total of published patients to 88, of which 21 are European. All patients had early-onset obesity; 96% had hyperphagia; 34% had one or more pituitary hormone deficiencies. Our calculation results in 998 predicted patients in Europe, corresponding to a prevalence of 1.34 per 1 million people (95% CI: 0.95–1.72). Conclusions This study shows that LepR deficiency is more prevalent in Europe (n = 998 predicted patients) than currently known (n = 21 patients), suggesting that LepR deficiency is underdiagnosed. An important cause for this could be lack of access to genetic testing. Another possible explanation is insufficient recognition, as only one-third of patients has pituitary hormone deficiencies. With novel highly effective treatment emerging, diagnosing LepR deficiency is more important than ever.

Prevalence Estimation: Summary of Common Problems and Practical Solutions

1993 ◽  
Vol 23 (2) ◽  
pp. 335-343 ◽  
Author(s):  
Yih-Ing Hser
Keyword(s):  
Extensive Discussion ◽  
Estimation Techniques ◽  
Prevalence Estimation ◽  
The Common ◽  
Common Problems ◽  
Practical Guidance

Policymakers and researchers interested in prevalence estimation often seek practical guidance on how to obtain and interpret quality estimates. Although the preceding chapters have addressed aspects of these issues, it is helpful to summarize some of the common problems and to provide practical solutions. This chapter assembles a set of questions and abbreviated answers in areas of policy needs, prevalence definitions, data characteristics and availability, and estimation techniques. Readers are directed to extensive discussion in other relevant chapters.

A System Dynamics Model for Cocaine Prevalence Estimation and Trend Projection

1993 ◽  
Vol 23 (2) ◽  
pp. 251-279 ◽  
Author(s):  
Jack B. Homer
Keyword(s):  
System Dynamics ◽  
Crack Cocaine ◽  
Simulation Models ◽  
Policy Implications ◽  
Dynamics Simulation ◽  
Prevalence Estimation ◽  
National Indicator ◽  
Sensitivity Tests ◽  
Indicator Data ◽  
Alternative Scenarios

In this article the author presents a system dynamics simulation model that reproduces a variety of national indicator data reflecting cocaine use and supply over a fifteen-year period and provides detailed estimates of underlying prevalence. Sensitivity tests are conducted that clarify observed trends such as growth in the compulsive use of crack cocaine and decline in the casual use of cocaine powder. Alternative scenarios with possible policy implications are simulated and projected through the year 2002 and the results are assessed. Researchers and decisionmakers hoping to understand and anticipate the dynamics of illicit drug use may benefit from simulation models that are realistic in detail and broad in scope.

Sign in / Sign up

Export Citation Format

Share Document