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Vaccine. 2014 Feb 12;32(8):990-5. doi: 10.1016/j.vaccine.2013.12.020. Epub 2013 Dec 31.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

Author information

1
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel; Department of Epidemiology, Faculty of Health Sciences, Ben Gurion University, Be'er Sheva, Israel. Electronic address: RBalicer@clalit.org.il.
2
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel. Electronic address: ChandraCo@clalit.org.il.
3
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel.
4
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel. Electronic address: BeccaFe@clalit.org.il.
5
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel. Electronic address: Ilanbr2@clalit.org.il.
6
Pfizer Inc., Outcomes Research, 500 Arcola Road, Collegeville, PA 19301, USA. Electronic address: Craig.Roberts@pfizer.com.
7
Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Arlozorov 101, Tel Aviv, Israel. Electronic address: MosheHo@clalit.org.il.

Abstract

BACKGROUND:

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting.

METHODS:

Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting.

RESULTS:

In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia.

CONCLUSIONS:

We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international guidelines. Health care managers and policy-makers may consider the strategic potential of utilizing clinical and administrative databases for creating population-specific risk prediction models to inform vaccination campaigns.

KEYWORDS:

Pneumococcal disease; Pneumonia; Risk profiling; Vaccine strategy

PMID:
24384054
DOI:
10.1016/j.vaccine.2013.12.020
[Indexed for MEDLINE]

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