Literature DB >> 2799125

Correcting reported AIDS incidence: a statistical approach.

S H Heisterkamp1, J C Jager, E J Ruitenberg, J A Van Druten, A M Downs.   

Abstract

This study is motivated by the time lag between date of diagnosis of AIDS cases and date of reporting, which results in incomplete data about the epidemic. A maximum likelihood procedure has been developed to adjust the actual numbers of diagnosed AIDS cases for reporting delay. If a parametric function for describing past and future incidence is assumed, its parameters and the adjustment for reporting delay can be estimated simultaneously. Data from the WHO Collaborating Centre on AIDS, Paris, are used. Practical problems related to data collection are dealt with.

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Year:  1989        PMID: 2799125     DOI: 10.1002/sim.4780080807

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  9 in total

1.  Modeling the HIV/AIDS epidemic via survivor functions.

Authors:  G Schinaia
Journal:  Eur J Epidemiol       Date:  2000-06       Impact factor: 8.082

2.  Short-term predictions of HIV prevalence and AIDS incidence.

Authors:  J C Hendriks; G F Medley; S H Heisterkamp; G J Van Griensven; P J Bindels; R A Coutinho; J A Van Druten
Journal:  Epidemiol Infect       Date:  1992-08       Impact factor: 2.451

3.  AIDS cases in adolescents and young adults in Italy: inferences from descriptive epidemiology.

Authors:  A E Tozzi; S Salmaso; M Giuliani; D Greco
Journal:  Eur J Pediatr       Date:  1991-05       Impact factor: 3.183

4.  A change-point model for reporting delays under change of AIDS case definition.

Authors:  F Tabnak; H G Müller; J L Wang; J M Chiou; R K Sun
Journal:  Eur J Epidemiol       Date:  2000       Impact factor: 8.082

5.  Adjusting AIDS incidence for non-stationary reporting delays: a necessity for country comparisons.

Authors:  M D Gebhardt; B E Neuenschwander; M Zwahlen
Journal:  Eur J Epidemiol       Date:  1998-09       Impact factor: 8.082

6.  Methods for estimating HIV prevalence: A comparison of extrapolation from surveys on infection rate and risk behaviour with back-calculation for the Netherlands.

Authors:  H Houweling; S H Heisterkamp; L G Wiessing; R A Coutinho; J K van Wijngaarden; H J Jager
Journal:  Eur J Epidemiol       Date:  1998-10       Impact factor: 8.082

7.  The use of AIDS surveillance data for short-term prediction of AIDS cases in Madrid, Spain.

Authors:  G F Medley; V Zunzunegui; R Bueno; D Lopez Gai
Journal:  Eur J Epidemiol       Date:  1991-07       Impact factor: 8.082

8.  The denominator problem: estimating MSM-specific incidence of sexually transmitted infections and prevalence of HIV using population sizes of MSM derived from Internet surveys.

Authors:  Ulrich Marcus; Axel J Schmidt; Christian Kollan; Osamah Hamouda
Journal:  BMC Public Health       Date:  2009-06-11       Impact factor: 3.295

9.  Potential adjustment methodology for missing data and reporting delay in the HIV Surveillance System, European Union/European Economic Area, 2015.

Authors:  Magdalena Rosinska; Nikos Pantazis; Janusz Janiec; Anastasia Pharris; Andrew J Amato-Gauci; Chantal Quinten
Journal:  Euro Surveill       Date:  2018-06
  9 in total

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