Coverage of the 2011 Q fever vaccination campaign in the Netherlands, using retrospective population-based prevalence estimation of cardiovascular risk-conditions for chronic Q fever

Background: In 2011, a unique Q fever vaccination campaign targeted people at risk for chronic Q fever in the southeast of the Netherlands. General practitioners referred patients with defined cardiovascular risk-conditions (age >15 years). Prevalence rates of those risk-conditions were lacking, standing in the way of adequate planning and coverage estimation. We aimed to obtain prevalence rates retrospectively in order to estimate coverage of the Q fever vaccination campaign. Methods: With broad search terms for these predefined risk-conditions, we extracted patient-records from a large longitudinal general-practice research-database in the Netherlands (IPCI-database). Afte

Coverage of the 2011 Q fever vaccination campaign in the Netherlands, using retrospective population-based prevalence estimation of cardiovascular risk-conditions for chronic Q fever

Background: In 2011, a unique Q fever vaccination campaign targeted people at risk for chronic Q fever in the southeast of the Netherlands. General practitioners referred patients with defined cardiovascular risk-conditions (age >15 years). Prevalence rates of those risk-conditions were lacking, standing in the way of adequate planning and coverage estimation. We aimed to obtain prevalence rates retrospectively in order to estimate coverage of the Q fever vaccination campaign. Methods: With broad search terms for these predefined risk-conditions, we extracted patient-records from a large longitudinal general-practice research-database in the Netherlands (IPCI-database). Afte

Performance of the Brighton collaboration case definition for hypotonic-hyporesponsive episode (HHE) on reported collapse reactions following infant vaccinations in the Netherlands.

We reviewed collapse (sudden onset of pallor, limpness and hyporesponsiveness) following the first infant (DPTP+Hib) vaccination reported to the enhanced passive surveillance system of the Netherlands in 1994-2003. All 1303 reports identified by the current RIVM (National Institute for Public Health and Environment) case definition were captured by the Brighton Collaboration (BC) case definition, with in 17 (1.3%) reports insufficient information. Over the years the proportion of the highest level of diagnostic certainty (level 1) increased due to more complete data from 70% to over 90%. We checked the BC case definition also on a sample of cases (with pallor or hyporesponsiveness) not meeting RIVM's case definition for collapse at the time. Sixty out of 200 cases were captured by BC but again rejected by RIVM. The sensitivity BC levels 2 and 3 appeared too high. We recommend a more restrict case definition by the Brighton Collaboration with certain exclusion criteria to make it more specific. Furthermore a change in the specifications for levels 2 and 3 will increase specificity and accommodate for the loss of sensitivity

Developing a vaccination evaluation model to support evidence-based decision making on national immunization programs.

Among all public health provisions national immunization programs (NIPs) are beyond doubt one of the most effective in reducing mortality, morbidity, and costs associated with major infectious diseases. To maintain their success, NIPs have to modernize in response to many new and old demands regarding efficacy, safety, availability of new vaccines, emerging and evolving pathogens, waning immunity, altered epidemiological situations, and the public's trust in the program. In this paper we present an evaluation model in the form of a checklist that may help in collecting relevant scientific information that is necessary for evaluation and decision making when considering changes in a NIP. Such a checklist points to relevant information on the vaccine-preventable disease, the pathogen causing it, the vaccine, and the cost-effectiveness ratio of the vaccine. However, the final judgment on a potential change in the NIP cannot be based on a simple algorithm, as the relevant information reflects factors of a very different kind and magnitude, to which different value judgements may be added, and which may have certain degrees of uncertainty. Because any change in the NIP may be accompanied by more or less unforeseen changes in the vaccine's efficacy, evolutionary consequences, including the antigenic composition of the pathogen, and the vaccine's safety profile, an intensive surveillance program should accompany any NIP. Elements thereof include clinical-epidemiological surveillance, surveillance of vaccination coverage, immune surveillance, surveillance of microbial population dynamics, and surveillance of adverse events and safety issues. We emphasize that the decision to introduce a vaccine in the NIP should be taken as seriously, both scientifically and ethically, as the decision to withhold a vaccine from the NIP. In the latter case one might be responsible for vaccine-preventable disease and mortality

Data from: Coverage of the 2011 Q fever vaccination campaign in the Netherlands, using retrospective population-based prevalence estimation of cardiovascular risk-conditions for chronic Q fever

Background: In 2011, a unique Q fever vaccination campaign targeted people at risk for chronic Q fever in the southeast of the Netherlands. General practitioners referred patients with defined cardiovascular risk-conditions (age >15 years). Prevalence rates of those risk-conditions were lacking, standing in the way of adequate planning and coverage estimation. We aimed to obtain prevalence rates retrospectively in order to estimate coverage of the Q fever vaccination campaign. Methods: With broad search terms for these predefined risk-conditions, we extracted patient-records from a large longitudinal general-practice research-database in the Netherlands (IPCI-database). After validation of these records, obtained prevalence rates (stratified for age and sex) extrapolated to the Q fever high-incidence area population, gave an approximation of the size of the targeted patient-group. Coverage calculation addressed people actually screened by a pre-vaccination Q fever skin test and serology (coverage) and patients referred by their general practitioners (adjusted-coverage) in the 2011 campaign. Results: Our prevalence estimate of any risk-condition was 3.1% (lower-upper limits 2.9-3.3%). For heart valve defects, aorta aneurysm/prosthesis, congenital anomalies and endocarditis, prevalence was 2.4%, 0.6%, 0.4% and 0.1%, respectively. Estimated number of eligible people in the Q fever high-incidence area was 11,724 (10,965-12,532). With 1330 people screened for vaccination, coverage of the vaccination campaign was 11%. For referred people, the adjusted coverage was 18%. Coverage was lowest among the very-old and highest for people aged 50–70 years. Conclusion: The estimated coverage of the vaccination campaign was limited. This should be interpreted in the light of the complexity of this target-group with much co-morbidity, and of the vaccine that required invasive pre-vaccination screening. Calculation of prevalence rates of risk-conditions based on the IPCI-database was feasible. This procedure proved an efficient tool for future use, when prevalence estimates for policy, implementation or surveillance of subgroup-vaccination or other health-care interventions are needed

Etiologies for seizures around the time of vaccination

OBJECTIVES: This study was an assessment of the incidence, course, and etiology of epilepsy with vaccination-related seizure onset in a population-based cohort of children. METHODS: The medical data of 990 children with seizures after vaccination in the first 2 years of life, reported to the National Institute for Public Health and Environment in the Netherlands in 1997 through 2006, were reviewed. Follow-up data were obtained of children who were subsequently diagnosed with epilepsy and had had seizure onset within 24 hours after administration of an inactivated vaccine or 5 to 12 days after a live attenuated vaccine. RESULTS: Follow-up was available for 23 of 26 children (median age: 10.6 years) with epilepsy onset after vaccination. Twelve children developed epileptic encephalopathy, 8 had benign epilepsy, and 3 had encephalopathy before seizure onset. Underlying causes were identified in 15 children (65%) and included SCN1A-related Dravet syndrome (formerly severe myoclonic epilepsy of infancy) or genetic epilepsy with febrile seizures plus syndrome (n = 8 and n = 1, respectively), a protocadherin 19 mutation, a 1qter microdeletion, neuronal migration disorders (n = 2), and other monogenic familial epilepsy (n = 2). CONCLUSIONS: Our results suggest that in most cases, genetic or structural defects are the underlying cause of epilepsy with onset after vaccination, including both cases with preexistent encephalopathy or benign epilepsy with good outcome. These results have significant added value in counseling of parents of children with vaccination-related first seizures, and they might help to support public faith in vaccination programs

Risk-conditions chronic Q fever vaccination datasets

The IPCI-dataset contains all extracted patients from a 5% sample of the population based general practice database, based on selection criteria described in the article. Patients age, sex and risk conditions are supplied with level of diagnostic certainty. The Q fever vaccination dataset contains accepted referrals and registered chronic Q fever risk conditions with sex, age and living area of patients. Code books are included

Relative frequencies of risk-conditions for chronic Q fever and sex distribution in the different populations.

<p>(A) Comparison between all screened people of the Q fever (QF)-vaccination campaign, screened people from the high-incidence (HI)-area, and cases from the IPCI-study population. (B) Sex distribution for different risk-conditions in screened people from QF-HI-area and cases from IPCI-study population. The IPCI-study population includes cases with definite and probable diagnostic certainty.</p

Flow diagram of calculations leading to coverage estimates.

<p>For calculations, prevalence of risk-conditions with definite and probable diagnostic certainty from IPCI-study population has been used, overall and for subgroups.</p

Risk-conditions for chronic Q fever of screened patients in the vaccination campaign in 2011.

<p>Only patients included from the high-incidence area, stratified according to age groups and sex.</p><p>Risk-conditions for chronic Q fever of screened patients in the vaccination campaign in 2011.</p