Estimating incidence of bacterial meningitis with capture-recapture method, Lazio Region, Italy

Eur J Epidemiol. 2000;16(9):843-8. doi: 10.1023/a:1007650317852.

Abstract

To estimate the incidence of bacterial meningitis in the Lazio Region, including the city of Rome, and to assess the quality of the surveillance systems, we adopted a multiple-capture model by merging cases from three sources available in 1995-1996: the Notifiable Disease Surveillance (NDS) system, the Special Hospital Surveillance (SHS) system and the Hospital Discharge (HD) registry. A medical record revision was carried out to confirm the cases of bacterial meningitis. A total of 199 individuals was classified as probable or confirmed cases of bacterial meningitis in 1995-1996. In this period, the incidence of reported meningitis was 3.8/100,000 (population = 5,209,633). The log-linear model yielded a total estimated number of 236 cases (95% confidence interval (CI): 206-306), the estimate of incidence reaching the value of 4.5/100,000. Hospital Discharge registry showed the highest sensitivity (77%), the SHS system the highest positive predictive value (83%). In 1997-1998, the meningitis surveillance was integrated with an additional laboratory-based source and yielded 326 cases, with an incidence of reported cases of 6.3/100,000. Laboratory surveillance, involving 115 (92%) public hospitals and 84 (57%) private clinics, contributed 35 (27%) cases in addition to those notified to NDS (n = 130). Multiple-capture models, in our experience could estimate the bacterial meningitis incidence with a very good approximation. In order to improve both sensitivity and positive predictive value of surveillance, hospital and public health sources should be integrated with laboratory-based system.

MeSH terms

  • Case-Control Studies
  • Confidence Intervals
  • Database Management Systems*
  • Disease Notification / statistics & numerical data
  • Hospitalization / statistics & numerical data
  • Humans
  • Incidence
  • Italy / epidemiology
  • Linear Models
  • Medical Record Linkage
  • Meningitis, Bacterial / epidemiology*
  • Patient Discharge / statistics & numerical data
  • Population Surveillance / methods*
  • Predictive Value of Tests
  • Registries
  • Risk Factors
  • Sensitivity and Specificity