Analysing vaccine efficacy evaluated in phase 3 clinical trials carried out during outbreaks.

In this paper we examine several definitions of vaccine efficacy (VE) that we found in the literature, for diseases that express themselves in outbreaks, that is, when the force of infection grows in time, reaches a maximum and then vanishes. The fact that the disease occurs in outbreaks results in several problems that we analyse. We propose a mathematical model that allows the calculation of VE for several scenarios. Vaccine trials usually needs a large number of volunteers that must be enrolled. Ideally, all volunteers should be enrolled in approximately the same time, but this is generally impossible for logistic reasons and they are enrolled in a fashion that can be replaced by a continuous density function (for example, a Gaussian function). The outbreak can also be replaced by a continuous density function, and the use of these density functions simplifies the calculations. Assuming, for example Gaussian functions, one of the problems one can immediately notice is that the peak of the two curves do not occur at the same time. The model allows us to conclude: First, the calculated vaccine efficacy decreases when the force of infection increases; Second, the calculated vaccine efficacy decreases when the gap between the peak in the force of infection and the peak in the enrollment rate increases; Third, different trial protocols can be simulated with this model; different vaccine efficacy definitions can be calculated and in our simulations, all result are approximately the same. The final, and perhaps most important conclusion of our model, is that vaccine efficacy calculated during outbreaks must be carefully examined and the best way we can suggest to overcome this problem is to stratify the enrolled volunteer's in a cohort-by-cohort basis and do the survival analysis for each cohort, or apply the Cox proportional hazards model for each cohort.

Pertussis epidemiological pattern and disease burden in Brazil: an analysis of national public health surveillance data.

Objective: We described pertussis epidemiological trends in Brazil between 2010 and 2015. We also assessed tetanus, diphtheria and acellular pertussis (Tdap) vaccine coverage among pregnant women from 2014, the year of the introduction of Tdap maternal immunization recommendation in Brazil, to 2016.Methods: Epidemiological data for incidence, prevalence, hospitalization, mortality, and maternal vaccination coverage were calculated based on the Brazilian public surveillance databases.Results: The epidemiological data analysis results showed that the pertussis average incidence rate (IR) was 2.19/100,000 inhabitants for all ages, with a peak in 2014 (4.03/100,000 inhabitants) and highest incidence in <1-year-old children (IR = 175.20/100,000). 97.6% of pertussis deaths (405/415) were in <1-year-old children. Maternal immunization coverage was 9.2% in 2014, 40.4% in 2015, and 33.8% in 2016.Conclusions: Pertussis incidence and pertussis-related deaths increased in Brazil from 2010 to 2014 and decreased in 2015. In the two years, 2015 and 2016 that followed the NIP recommendation, Tdap vaccination coverage of pregnant women was low and varying from region to region. More efforts and national plans would help increase awareness and maternal immunization coverage.

[Epidemiologic surveillance of measles and rubella in Campinas (SP), Brazil: the reliability of the data]. / Vigilância epidemiológica do sarampo e da rubéola no Município de Campinas (SP), Brasil: confiabilidade dos dados.

OBJECTIVE: To evaluate and validate the information concerning measles and rubella from the Brazilian National Disease Notification System (BNDNS) (Sistema Nacional de Informação de Agravos de Notificação, or SINAN) for Campinas, a large city in the state of São Paulo, Brazil, using as a reference the data from a control system, the Syndromic Surveillance System for Fever and Exanthem (SSSFE) (Sistema de Vigilância Sindrômica de Febre e Exantema, or VigiFEx), which operated from May 2003 through June 2004. METHOD: In our study we compared: (1) annual data from BNDNS for the years 1999 through 2003 and (2) data from BNDNS and data from SSSFE for the period of June 2003 through May 2004. We analyzed the rate of completion for key fields (record number, date of notification, and city of notification) as well as for name of disease, date of first symptoms, name of patient, birth date and age, sex, city of residence, date of investigation, immunization history, presence of exanthem, date at start of exanthem, presence of fever, suspected cases among pregnant women, signs and symptoms, date of collection of first sample, results with the sample, virus isolation, final classification, criteria for confirmation/exclusion of cases, diagnosis of excluded patients, development of the case, and date of closure. The level of agreement between the recorded cases in the two data banks was also analyzed. RESULTS: From June 2003 through May 2004, 211 suspected cases of measles or rubella were identified in SSSFE and 275 in BNDNS. All the records had complete information concerning the three key fields. The rate of completion was also 100% for patient name, disease, and city of residence. The completion rate was higher than 95% for date of investigation, measles vaccine, measles and rubella vaccine, and rubella vaccine. A lower completion rate was found for other vaccination variables (number of doses and date of last dose) and for exanthem, fever, and date of start of exanthem. The two information systems were not completely consistent, particularly in terms of variables related to epidemiologic background, clinical data, and case closure. The quality of the SSSFE data was higher. CONCLUSIONS: Epidemiologic surveillance, immunization, and laboratory information systems need to undergo routine evaluation to ensure that the data are reliable and can support the planning of public health efforts.