Purpose
Viral conjunctivitis is a highly contagious condition that affects all age groups. Little data exists regarding the timing and location of outbreaks of this condition. The purpose of this study is to determine whether there are seasonal and socio-demographic trends in viral conjunctivitis diagnoses in the United States (US) and how they vary over time.
Methods
Using eleven years (2002-2012) of longitudinal data from a nationwide US healthcare claims database, all healthcare encounters that included an ICD-9-CM diagnosis code suggestive of viral conjunctivitis (0773, 37200, 37202) were identified for enrollees age ≤21. Spotfire data visualization software was used to plot seasonal variation of conjunctivitis diagnoses by variables including year of diagnosis, patient age, sex, household income, race, and geographic region of the US to identify trends for further analysis.
Results
During the 11 years, there were 671,086 eligible encounters with a patient ≤21 years old receiving a diagnosis code of probable viral conjunctivitis. The monthly rate of diagnosis of viral conjunctivitis per 100,000 enrollees increased from 150.0±27.8 in 2002 to 206.8±63.3 in 2012; p=0.0005. We identified a cyclical pattern of spikes in viral conjunctivits diagnoses which varied regionally across the country. The South and West regions of the US typically have a peak in diagnosis rate in March and April, whereas the Midwest and Northeast have an additional distinct peak during December of most years. Over the course of the study, the diagnosis rates in the South and West regions were most tightly correlated (r=0.92) whereas the rates in the Northeast and West were the least tightly correlated (r=0.71). Overall, other demographic factors including sex, household income, and race did not have a substantial influence on the seasonal variation.
Conclusions
From year to year, we observe cyclical patterns of spikes in viral conjunctivitis diagnoses. In a given year, there is a rise in viral conjunctivitis cases in southern and western states during March and April, followed by spikes in cases in midwestern and northeastern states. Researchers can use this data to build predictive models of the timing and location of future outbreaks and coordinate efforts with public health officials to educate the public of ways to protect themselves from the spread of this condition.