Semi-parametric estimation of age-time specific infection incidence from serial prevalence data

Nagelkerke, Nico; Heisterkamp, S. H.; Borgdorff, Martien W.; Broekmans, J. F.; Houwelingen, Hans C. van

doi:10.1002/(sici)1097-0258(19990215)18:3<307::aid-sim15>3.0.co;2-z

Cited by 15 publications

(18 citation statements)

References 29 publications

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“…This is consistent with findings by Dodd et al who suggested estimates of TB infection based on surveys in children may underestimate infection incidence in adults [15]. A higher risk of infection among adolescents than among children of primary school age has also been reported elsewhere [16,17]. In addition, a golden age has previously been described where the incidence of infection goes down after 5 years of age and begins to rise in adolescence [18].…”

Section: Discussionsupporting

confidence: 90%

Estimating the annual risk of infection with Mycobacterium tuberculosis among adolescents in Western Kenya in preparation for TB vaccine trials

Nduba

Hoog

Bruijn

et al. 2019

Preprint

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Background: Adolescents are a prime target group for tuberculosis (TB) vaccine trials that include prevention of infection (POI). The BCG vaccine is given at birth and does not prevent TB infection. TB infection, a critical endpoint for POI vaccine trials would need to be documented to estimate sample sizes in target populations.Methods: Adolescents aged 12-18 years of age were enrolled in an area under continuous demographic surveillance. A tuberculin skin test (TST) survey was conducted as part of a study on TB prevalence and incidence. All adolescents got TSTs at enrolment and returned after 72 h for reading. A TST of ≥10 mm if HIV negative or ≥ 5 mm if HIV positive, was considered positive. Results: Of 4808 adolescents returning for TST readings (96% of those enrolled), mean age was 14.4 (SD 1.9), 4518(94%) were enrolled in school and 21(0.4%) gave a previous history of tuberculosis. Among adolescents with TST reactivity, the mean TST induration was 13.2 mm (SD 5.4). The overall prevalence of latent TB infection was 1544/4808 (32.1, 95% CI 29.2-35.1) with a corresponding annual risk of TB infection (ARTI) of 2.6% (95% CI 2.2-3.1). Risk factors for a positive TST included being male (OR 1.3, 95% CI 1.2,1.5), history of having a household TB contact (OR 1.5, 95% CI 1.2,1.8), having a BCG scar (OR 1.5,95% CI 1.2,1.8), living in a rural area (OR 1.4, 95% CI 1.1,1.9), and being out of school (OR 1.8, 95% CI 1.4,2.3). Conclusion:We conclude that the high TB transmission rates we found in this study, suggest that adolescents in this region may be an appropriate target group for TB vaccine trials including TB vaccine trials aiming to prevent infection.

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Section: Discussionsupporting

confidence: 90%

Estimating the annual risk of infection with Mycobacterium tuberculosis among adolescents in Western Kenya in preparation for TB vaccine trials

Nduba

Hoog

Bruijn

et al. 2019

Preprint

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“…Ades and Nokes [28] used several GLMs to model age and time dependent force of infection for a series of seroprevalence sample of toxoplasmosis. Nagelkerke et al [29] proposed a semiparametric model in which the time (the year of birth) was included as a covariate in the model and the time e ect was estimated parametrically using a proportional hazard model which was applied to a series of tuberculin surveys.…”

Section: Discussionmentioning

confidence: 99%

Modelling age‐dependent force of infection from prevalence data using fractional polynomials

Shkedy

Aerts

Molenberghs

et al. 2005

Statistics in Medicine

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The force of infection is one of the primary epidemiological parameters of infectious diseases. For many infectious diseases it is assumed that the force of infection is age-dependent. Although the force of infection can be estimated directly from a follow up study, it is much more common to have cross-sectional seroprevalence data from which the prevalence and the force of infection can be estimated. In this paper, we propose to model the force of infection within the framework of fractional polynomials. We discuss several parametric examples from the literature and show that all of these examples can be expressed as special cases of fractional polynomial models. We illustrate the method on five seroprevalence samples, two of Hepatitis A, and one of Rubella, Mumps and Varicella.

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“…Joint modelling of age and time e ects in the context of infectious disease modelling has been discussed by several authors [12,[14][15][16][17]. Our focus, however, is di erent: temporal e ects are not our primary interest.…”

Section: Introductionmentioning

confidence: 99%

Estimation of infectious disease parameters from serological survey data: the impact of regular epidemics

Whitaker

Farrington

2004

Statistics in Medicine

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Serological surveys are a useful source of information about epidemiological parameters for infectious diseases. In particular they may be used to estimate contact rates, forces of infection, the reproduction number and the critical vaccination threshold. However, these estimation methods require the assumption that the infection is in endemic equilibrium. Such equilibria seldom exist in practice: for example, many common infections of childhood exhibit regular epidemic cycles. In this paper, we investigate whether ignoring such cycles produces biased estimates. We apply the methods to data on mumps and rubella in the U.K. prior to the introduction of the combined measles, mumps, rubella (MMR) vaccine. We conclude that past epidemics have only a marginal effect on estimates, and that standard methods that do not adjust for regular epidemics are valid.

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Semi-parametric estimation of age-time specific infection incidence from serial prevalence data

Cited by 15 publications

References 29 publications

Estimating the annual risk of infection with Mycobacterium tuberculosis among adolescents in Western Kenya in preparation for TB vaccine trials

Estimating the annual risk of infection with Mycobacterium tuberculosis among adolescents in Western Kenya in preparation for TB vaccine trials

Modelling age‐dependent force of infection from prevalence data using fractional polynomials

Estimation of infectious disease parameters from serological survey data: the impact of regular epidemics

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