Skip to main content
Log in

Recovering incidence from repeated measures of prevalence: the case of urinary tract infections

  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

Objective

To study the relationships between prevalence and incidence in the case of nosocomial infections of the urinary tract, and to evaluate if repeated prevalence measures may be useful to obtain an estimate of incidence.

Methods

Methodology is based on a simple and reasonable assumption on the infection dynamics: starting from a difference equation modeling the evolution of hospital population, it is obtained a set of equations allowing to calculate the incidence by means of the knowledge of prevalence.

Results

The numerical validation of the model done by computer simulations, shows that the model obtains a better estimate of incidence than the approach given by the classical rule prevalence = incidence × duration.

Conclusions

The proposed strategy permits to forecast the incidence of the urinary tract nosocomial infections by using repeated measures of prevalence. It is hence possible to estimate the incidence from cross-sectional prevalence data with sufficient accuracy to monitor and estimate the time dynamics of these infections.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Rhame FS, Sudderth WD. Incidence and prevalence as used in the analysis of the occurrence of nosocomial infections. Am J Epidemiol. 1981;112:1–11.

    Google Scholar 

  2. Podgor MJ, Leske MC. Estimating incidence from age-specific prevalence for irreversible diseases with differential mortality. Stat Med. 1986;5:573–8.

    Article  CAS  PubMed  Google Scholar 

  3. Leske MC, Ederer F, Podgor M. Estimating incidence from age- specific prevalence in glaucoma. Am J Epidemiol. 1986;121:606–13.

    Google Scholar 

  4. Newman SC, Bland RC. Estimating the morbidity risk of illness from survey data. Am J Epidemiol. 1989;129:430–8.

    CAS  PubMed  Google Scholar 

  5. Keiding N. Age-specific incidence and prevalence: a statistical perspective. J R Stat Soc Ser A. 1991;154:371–412.

    Article  Google Scholar 

  6. Keiding N, Bergtrup K, Scheike TH, Hasibeder G. Estimation from current-status data in continuous time. Lifetime Data Anal. 1996;2:119–29.

    Article  CAS  PubMed  Google Scholar 

  7. Marschner IC. Fitting a multiplicative incidence model to age- and time-specific prevalence data. Biometrics. 1996;52:492–9.

    Article  CAS  PubMed  Google Scholar 

  8. Marschner IC. A method for assessing age-time disease incidence using serial prevalence data. Biometrics. 1997;53:1384–98.

    Article  CAS  PubMed  Google Scholar 

  9. Ades AE, Nokes DJ. Modeling age- and time-specific incidence from seroprevalence: toxoplasmosis. Am J Epidemiol. 1993;137:1022–34.

    CAS  PubMed  Google Scholar 

  10. Moro ML. Infezioni ospedaliere: prevenzione e controllo. Torino: Centro Scientifico Editore; 1993.

    Google Scholar 

  11. Kampf G, Gastmeier P, Wischnewski N, Schlingmann J, Schumacher M, Daschner F, Rüden H. Analysis of risk factors for nosocomial infections—results from the first national prevalence survey in Germany (NIDEP Study, Part 1). J Hosp Infect. 1997;37:103–12.

    Article  CAS  PubMed  Google Scholar 

  12. Nichelatti M, Lomolino G, Montomoli C. Nosocomial infections in an Italian hospital: prevalence and risk factors. Biomed Stat Clin Epidemiol. 2007;1:39–45.

    Google Scholar 

  13. Little JDC. A proof for the queuing formula L = λW. Oper Res. 1961;9:383–7.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cristina Montomoli PhD.

Additional information

Salvarani F, Nichelatti M, Montomoli C. Recovering incidence from repeated measures of prevalence: the case of urinary tract infections.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Salvarani, F., Nichelatti, M. & Montomoli, C. Recovering incidence from repeated measures of prevalence: the case of urinary tract infections. J Clin Monit Comput 24, 269–277 (2010). https://doi.org/10.1007/s10877-010-9244-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-010-9244-2

Keywords

Navigation