Literature DB >> 9000488

Rate estimation from prevalence information on a simple epidemiologic model for health interventions.

R C Brunet1, C J Struchiner.   

Abstract

Health intervention control programs, such as vaccination, can be evaluated by comparing incidence rates of infection between unprotected and protected individuals in a population. The ratio of incidence rates is usually estimated by following up control and treated groups in order to collect information on person-time and cases in each group. This approach can be expensive and time consuming. An alternative approach is to use prevalence data to reconstitute incidence. Current-status are readily available or easily gathered and can be used to estimate incidence rates. Under certain assumptions of irreversibility for the outcome of interest, we discuss a simple transmission model appropriate to evaluate health interventions that confer long term protection. Rates and populations are parameter-free functions of age and calendar time. We develop general mathematical relationships that link incidence and intervention rates to prevalence which could be estimated from sampling without requiring knowledge of subpopulation demographics.

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Year:  1996        PMID: 9000488     DOI: 10.1006/tpbi.1996.0029

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  5 in total

1.  A method for estimating time dependent intervention benefits under arbitrarily varying age and exogenous components of hazard.

Authors:  R C Brunet; C J Struchiner; A Loinaz
Journal:  Lifetime Data Anal       Date:  2001-12       Impact factor: 1.588

2.  Randomization and baseline transmission in vaccine field trials.

Authors:  C J Struchiner; M E Halloran
Journal:  Epidemiol Infect       Date:  2007-02       Impact factor: 2.451

3.  Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression.

Authors:  Michael E Reichenheim; Evandro S F Coutinho
Journal:  BMC Med Res Methodol       Date:  2010-07-15       Impact factor: 4.615

4.  Population-level HIV incidence estimates using a combination of synthetic cohort and recency biomarker approaches in KwaZulu-Natal, South Africa.

Authors:  Eduard Grebe; Alex Welte; Leigh F Johnson; Gilles van Cutsem; Adrian Puren; Tom Ellman; Jean-François Etard; Helena Huerga
Journal:  PLoS One       Date:  2018-09-13       Impact factor: 3.240

5.  A general HIV incidence inference scheme based on likelihood of individual level data and a population renewal equation.

Authors:  Guy Severin Mahiane; Rachid Ouifki; Hilmarie Brand; Wim Delva; Alex Welte
Journal:  PLoS One       Date:  2012-09-12       Impact factor: 3.240

  5 in total

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