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

Abstract

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.

Fig 1. Municipalities with their incidence of Q fever in 2009–2010.

Q fever high-incidence area outlined in black (11).

Fig 2. Flow diagram of calculations leading to coverage estimates.

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

Fig 3. Population distribution of IPCI-database and of Q fever high-incidence area.

(A) population pyramids for IPCI-database (dark colours) and for Q fever high-incidence (HI)- area (light colours), according to sex. (B) Relative age frequencies for IPCI-source population (open squares), IPCI-study population (triangles) and for Q fever HI-area (open circles).

Fig 4. Flow diagram of results of IPCI-database extraction and validation.

Sex distribution (male %) included for each step.

Fig 5. Relative frequencies of risk-conditions for chronic Q fever and sex distribution in the different populations.

(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.

Fig 6. Age distribution of the defined risk conditions for chronic Q fever.

Prevalence rates (per 10,000) are estimated from the IPCI-database, per January 1^st 2011.