Abstract
Vaccine-preventable diseases among high-risk patients are a public health priority in high-income countries. Most national immunization programs have included vaccination recommendations for these population groups but they remain hard-to-reach and coverage data are poorly available. In a pilot study, we developed and tested an automated approach for identifying individuals with underlying medical conditions to feed an immunization information system (IIS). We reviewed published recommendations on medical conditions that indicate vaccination against influenza, pneumococcal disease, meningococcal disease, hepatitis A, and hepatitis B. For each medical condition, we identified the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedure codes, the user fee exempt codes and the Anatomical Therapeutic Chemical Classification System codes and we reported these data in correspondence tables. Using these tables, we extracted three lists of patients recorded in three current data sources between 2001 and 2010 in the Apulia region of Italy: the hospital discharge registry, the user fee exempt registry, and the drug prescription registry. Using a unique personal identification number, we linked these three lists of patients with the regional IIS (2012 database), obtaining a list of patients with chronic diseases eligible for vaccination. We tested completeness, sensitivity, and positive predictive value (PPV) of this approach by asking a sample of 28 general practitioners (GPs) to evaluate the matching between a sublist of patients with clinical recommendations for influenza vaccination and the GPs individual subjects medical records. We included a total of 1,204,496 subjects with underlying medical conditions eligible to receive any of the aforementioned vaccinations. Of these, 9% were identified in all three data sources, 18% in two sources, and 73% in one source. The completeness of this automated process in identifying GPs high-risk patients eligible for influenza vaccination was 88.9% [95% confidence intervals (95% CI): 88.1-89.8%], with a sensitivity of 69.2% (95% CI: 67.7-70.6%) and a PPV of 85.7% (95% CI: 84.4-86.8%). The high completeness of the methodology used for identifying high-risk patients in current data sources encouraged us to apply this approach for feeding the regional IIS.
Highlights
In the past few decades, the availability of a growing number of new vaccines and their inclusion in immunization programs has provided the opportunity to cover the whole life course
We obtained a list of patients with chronic diseases eligible for vaccination [chronic patients list (CPL)]
Each data source examined (HDR, User fee exempts registry (UFER), and Drugs prescription registry (DPR)) contributed differently to create the CPL: 9% of patients were identified in all three sources, 18% in two sources (HDR and UFER; or Hospital discharge registry (HDR) and DPR; or UFER and DPR), and 73% in one source (Figure 1)
Summary
In the past few decades, the availability of a growing number of new vaccines and their inclusion in immunization programs has provided the opportunity to cover the whole life course. Despite several high-income countries have included vaccination recommendations for subjects with chronic diseases in their national immunization schedules [2, 3], these population groups remain extremely hard-to-reach and vaccine coverage data are poorly available. A specific tool for data collection in those subjects with chronic medical conditions is used in England where the “ImmForm survey” makes monthly available provisional data of seasonal influenza vaccine uptake among general practitioners (GPs) patients [5]. Vaccine-preventable diseases among high-risk patients are a public health priority in high-income countries. Most national immunization programs have included vaccination recommendations for these population groups but they remain hard-to-reach and coverage data are poorly available. We developed and tested an automated approach for identifying individuals with underlying medical conditions to feed an immunization information system (IIS)
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