Elsevier

Vaccine

Volume 28, Issue 39, 7 September 2010, Pages 6470-6477
Vaccine

Prevalence of high-risk indications for influenza vaccine varies by age, race, and income

https://doi.org/10.1016/j.vaccine.2010.07.037Get rights and content

Abstract

Estimates of the proportions of the population who are at high risk of influenza complications because of prior health status or who are likely to have decreased vaccination response because of immunocompromising conditions would enhance public health planning and model-based projections. We estimate these proportions and how they vary by population subgroups using national data systems for 2006–2008. The proportion of individuals at increased risk of influenza complications because of health conditions varied 10-fold by age (4.2% of children <2 years to 47% of individuals >64 years). Age-specific prevalence differed substantially by gender, by racial/ethnic groups (with African Americans highest in all age groups) and by income. Individuals living in families with less than 200% of federal poverty level (FPL) were significantly more likely to have at least one of these health conditions, compared to individuals with 400% FPL or more (3-fold greater among <2 and 30% greater among >64 years). Among children, there were significantly elevated proportions in all regions compared to the West. The estimated prevalence of immunocompromising conditions ranged from 0.02% in young children to 6.14% older adults. However, national data on race/ethnicity and income are not available for most immunocompromising conditions, nor is it possible to fully identify the degree of overlap between persons with high-risk health conditions and with immunocompromising conditions. Modifications to current national data collection systems would enhance the value of these data for public health programs and influenza modeling.

Introduction

Seasonal influenza leads to about 36,000 deaths annually in the United States [1] and more than 226,000 hospitalizations, totaling about 3.1 million hospitalized days and five billion dollars [2], [3]. Risk of these serious influenza complications is much higher for persons with a number of identified health-related risk factors than for other persons [4], [5], [6], [7], [8], [9], [10].

Historically and during times of vaccine shortage, the Centers for Disease Control and Prevention's (CDC's) Advisory Committee on Immunization Practices (ACIP) has recommended that these high-risk groups be the initial targeted recipients for vaccination. In 2009, for novel H1N1, ACIP initially recommended influenza vaccination for five high-risk groups defined by age, occupation, and health conditions [11]. The health conditions included pregnancy during the influenza season, chronic pulmonary (including asthma) or cardiovascular (except hypertension), renal, hepatic, neurological/neuromuscular, hematologic, metabolic disorders (including diabetes mellitus), and immunosuppression. (In 2010, the ACIP recommended universal vaccination, see http://www.cdc.gov/vaccines/recs/provisional/downloads/flu-vac-mar-2010-508.pdf.) The ACIP selected these health conditions because people who have them have increased risk for influenza-related complications, hospitalizations, and death.

Having accurate estimates of how many people have one or more of these health conditions is important for two main reasons. First, the estimates can help public health officials project the number of vaccine doses needed when vaccination is targeted.

Second, the estimates can lead to better projections of morbidity and mortality and the impact of interventions. Increasingly, modeling is used for planning vaccination distributions strategies [12], [13], [14] and understanding the impact of vaccination on mitigation [15], [16], [17]. Incorporating population heterogeneity, both in terms of risk of infection and risk of complications, would enhance the value of modeling for some applications. Because the main route of transmission for influenza is via respiratory droplets of coughs and sneezes [18], [19], [20], contact networks are an important feature of infectious disease models. While many models assume random mixing across the entire population, more realistic modeling is able to account for heterogeneity in the population, and capture how people with similar sociodemographic characteristics are more likely to have contact with each other than with a randomly selected individual. Therefore, understanding how the proportions of the population with high-risk medical conditions vary across sociodemographic groups affects the estimates of disease burden generated by the models.

In this report, we estimate the proportions of the population with one or more of the medically indicated conditions. Most of the conditions included in the list vary with age. We further examine whether they vary with race and socioeconomic status [21]. Since persons are more likely to live near others who are similar to themselves, such variation could help public health departments develop strategies to reach high-risk populations and could also lead to messaging that is more focused and culturally appropriate for the target population.

CDC monitors vaccination rates for high-risk conditions using definitions based on data from the National Health Interview Survey (NHIS) [22], [23]. However, these monitoring reports have limited information on individuals with immunocompromising conditions. Nor does CDC monitor how medically indicated conditions vary by sociodemographic subgroup, other than age. In this report, we develop estimates of immunocompromising conditions, propose a more inclusive strategy for defining high-risk conditions from the NHIS and other national data systems, evaluate heterogeneity, and discuss implications for health policy.

Section snippets

Health conditions

The ACIP defines high-risk health conditions that are indications for influenza vaccination [24]. The NHIS, a large, nationally representative population survey that is conducted annually, has been used to estimate prevalences of those high-risk conditions that indicate a need for influenza vaccination [11]. A multidisciplinary group of investigators in the Models of Infectious Disease Agent Study (MIDAS) reviewed NHIS questionnaires for 2006 through 2008 to identify questions that best

Results

Table 1 presents the age-specific prevalence of individuals with at least one high-risk health condition and the frequencies of immunocompromising conditions. Table 1 also lists the most frequent condition for each age group. For health conditions, the proportions increase until age 5 and then plateau until age 50, when they again increase. The proportions increase ten-fold, such that, in the population aged 65 years and older, almost half of the population has at least one of the medically

Discussion

We have used national data sources to estimate the proportion of the population with a health condition that confers high risk of influenza complications and the proportion with an immunocompromising condition that may have decreased immune response to influenza vaccination. We further have shown that these proportions vary by age, gender, race, and socioeconomic status. Our estimates should be useful both to modelers and to public health departments. For pandemic modeling that includes

Acknowledgements

This work was supported by Grant Numbers U54GM088491, U24GM087704 and U01GM087729 from the National Institute of General Medical Sciences (NIGMS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIGMS or the National Institutes of Health.

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