Estimating incidence from prevalence in generalised HIV epidemics: methods and validation. - Wikidata - awgldk - TriplyDB

Estimating incidence from prevalence in generalised HIV epidemics: methods and validation.

Estimating incidence from prevalence in generalised HIV epidemics: methods and validation.

http://www.wikidata.org/entity/Q33347813

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Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013.Estimates of HIV incidence from household-based prevalence surveys Incidence of herpes simplex virus type 2 infections in Africa: a systematic review A Comparison of South African National HIV Incidence Estimates: A Critical Appraisal of Different Methods The Incidence Patterns Model to Estimate the Distribution of New HIV Infections in Sub-Saharan Africa: Development and Validation of a Mathematical Model HIV Incidence and Spatial Clustering in a Rural Area of Southern Mozambique National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models Risk factors for HIV-1 infection in a longitudinal, prospective cohort of adults from the Mbeya Region, Tanzania.Measuring the HIV/AIDS epidemic: approaches and challenges.Reliable direct measurement of causes of death in low- and middle-income countries The effect of changes in condom usage and antiretroviral treatment coverage on human immunodeficiency virus incidence in South Africa: a model-based analysis.A general HIV incidence inference scheme based on likelihood of individual level data and a population renewal equation.Modelling the incidence and mortality of psychotic disorders: data from the second Australian national survey of psychosis.Viral and Host Characteristics of Recent and Established HIV-1 Infections in Kisumu based on a Multiassay Approach.Incorporating incidence information within the UNAIDS Estimation and Projection Package framework: a study based on simulated incidence assay data.Data Resource Profile: Network for Analysing Longitudinal Population-based HIV/AIDS data on Africa (ALPHA Network).Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections.Errors in 'BED'-derived estimates of HIV incidence will vary by place, time and age A decline in new HIV infections in South Africa: estimating HIV incidence from three national HIV surveys in 2002, 2005 and 2008 Pregnancy does not affect HIV incidence test results obtained using the BED capture enzyme immunoassay or an antibody avidity assay.Estimating HIV incidence among adults in Kenya and Uganda: a systematic comparison of multiple methods.Force of tuberculosis infection among adolescents in a high HIV and TB prevalence community: a cross-sectional observation study.Mixture models for calibrating the BED for HIV incidence testing.Using tests for recent infection to estimate incidence: problems and prospects for HIV Hepatitis C virus acquisition among injecting drug users: a cohort analysis of a national repeated cross-sectional survey of needle and syringe program attendees in Australia, 1995-2004 Estimating the HIV incidence rate: recent and future developments.Estimating HIV Incidence in Populations Using Tests for Recent Infection: Issues, Challenges and the Way Forward.Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes Stabilizing HIV prevalence masks high HIV incidence rates amongst rural and urban women in KwaZulu-Natal, South Africa.The Spectrum projection package: improvements in estimating incidence by age and sex, mother-to-child transmission, HIV progression in children and double orphans.Trends in HIV prevalence and sexual behaviour among young people aged 15-24 years in countries most affected by HIV.More on the cohort-component model of population projection in the context of HIV/AIDS: A Leslie matrix representation and new estimates.Dynamics of non-cohabiting sex partnering in sub-Saharan Africa: a modelling study with implications for HIV transmission.Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey Are Geographical "Cold Spots" of Male Circumcision Driving Differential HIV Dynamics in Tanzania?Estimating HIV incidence based on combined prevalence testing.Estimating cardiovascular disease incidence from prevalence: a spreadsheet based model.Lower-sensitivity and avidity modifications of the vitros anti-HIV 1+2 assay for detection of recent HIV infections and incidence estimation.Trends in HIV prevalence among young people in generalised epidemics: implications for monitoring the HIV epidemic.External infections contribute minimally to HIV incidence among HIV sero-discordant couples in sub-Saharan Africa.

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P2860

Estimating incidence from prevalence in generalised HIV epidemics: methods and validation.

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2008年の論文

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ALPHA Network

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Timothy B Hallett

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P2860

HIV-1 infection in rural Africa: is there a difference in median time to AIDS and survival compared with that in industrialized countries?

Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Collaborative Group on AIDS Incubation and HIV Survival including the CASCADE EU Concerted Action. Concerted

Appropriate evaluation of HIV prevention interventions: from experiment to full-scale implementation.

Public health surveillance of AIDS and HIV infections.

Estimating HIV incidence rates from age prevalence data in epidemic situations.

The empirical evidence for the impact of HIV on adult mortality in the developing world: data from serological studies.

Behavioural data as an adjunct to HIV surveillance data.

Age-specific mortality patterns in HIV-infected individuals: a comparative analysis of African community study data.

Estimating incidence of HIV infection in childbearing age African women using serial prevalence data from antenatal clinics.

HIV prevalence and incidence in rural Tanzania: results from 10 years of follow-up in an open-cohort study.

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