Socioeconomic and race/ethnic patterns in persistent infection burden among U.S. adults.
BACKGROUND
The pathophysiological mechanisms that underlie health disparities by socioeconomic status and race/ethnicity are poorly understood. Promising new research suggests that the burden of persistent infection may influence adult disease risk and mortality. This article examines how multiple persistent infections cluster within individuals and how this clustering varies by socioeconomic position and race/ethnicity in U.S. adults.


METHODS
We analyze data from the National Health and Nutrition Examination Survey III (N = 19,275) for adults aged 17-90 years. The clustering of infections within individuals is studied using tetrachoric correlations. Multiple indicator multiple cause models are used to analyze the infection burden construct as measured by seropositivity to Helicobacter pylori, cytomegalovirus, herpes simplex virus-1, and hepatitis B, focusing on the burden's distribution by socioeconomic position and race/ethnicity. The results are corroborated using ordered logistic regression for a commonly used count index of individual infections.


RESULTS
Seroprevalence of individual persistent infections is positively correlated, suggesting common factors related to exposure or susceptibility. Education, income, and race/ethnicity are strong and significant independent predictors of infection burden in U.S. adults in all models.


CONCLUSIONS
The disproportionate burden of persistent infections among disadvantaged groups across all ages may be one biologic pathway by which low socioeconomic position is related to increased rates of morbidity and mortality in the United States.
Zajacova
Anna
A
Department of Epidemiology, Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, 1214 S University Ave., Ann Arbor, MI 48104-2548, USA.
Dowd
Jennifer B
JB
Aiello
Allison E
AE
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001294:Attitude to Health; D002908:Chronic Disease; D003141:Communicable Diseases / Q000175:diagnosis* / Q000453:epidemiology* / Q000208:ethnology; D004522:Educational Status; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D054624:Health Status Disparities; D006306:Health Surveys; D054625:Healthcare Disparities / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015995:Prevalence; D011336:Probability; D018570:Risk Assessment; D012720:Severity of Illness Index; D017678:Sex Distribution; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology; D055815:Young Adult