Lack of Seasonal Variation in the Endoscopic Diagnoses of Crohn's Disease and Ulcerative Colitis

Oregon Health and Science University, Portland, Oregon, United States
The American Journal of Gastroenterology (Impact Factor: 10.76). 11/2005; 100(10):2233-8. DOI: 10.1111/j.1572-0241.2005.50127.x
Source: PubMed


Conflicting data have been reported about the seasonal variation of inflammatory bowel diseases (IBD). The purpose of the present analysis was to assess the occurrence of seasonal variations in the endoscopic diagnosis of Crohn's disease (CD) and ulcerative colitis (UC).
The Clinical Outcomes Research Initiative (CORI) uses a computerized endoscopic report generator to collect endoscopic data from 73 diverse practice sites throughout the United States. We utilized the CORI database to analyze the date-specific occurrence of colonoscopy, as well as the colonoscopic diagnoses of CD and UC. Time trends were analyzed by autocorrelation, linear, and nonlinear regression.
Between January 2000 and December 2003, the number of colonoscopies increased 4.1-fold. The proportion of colonoscopies with a CD diagnosis fell by 28%, and the proportion of colonoscopies with a UC diagnosis fell by 50%. The occurrence of neither CD nor UC was shaped by any clear-cut seasonal periodicity. However, the trends of the two diseases revealed strikingly similar patterns with four resembling peaks superimposed on their monthly fluctuations.
Endoscopic diagnosis of IBD is unaffected by any seasonal variation. The decline in the diagnostic rate of colonic IBD may reflect a relative increase in the utilization of colonoscopy for colon cancer screening. The similarity in the monthly fluctuations of both IBD suggests that their incidence or flare-ups may be influenced by identical exogenous risk factors.

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Available from: Amnon Sonnenberg, Jul 10, 2014
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    • "Several studies have reported seasonal variations in the flare of IBD, previously (Mee and Jewell, 1978; Rampton et al., 1983; Isgar et al., 1983; Sellu, 1986; Riley et al., 1990; North et al., 1991; Tysk and Jarnerot, 1993). However, very few studies, all performed in Western countries, investigated seasonality in the onset of IBD (Auslander et al., 2005; Evans and Acheson, 1965; Cave and Freedman, 1975; Don and Goldacre, 1984; Myszor and Calam, 1984; Sonnenberg et al., 1994; Moum et al., 1996; Aratari et al., 2006). The studies reported conflicting results with a peak from August to January for UC, and no seasonality for CD previously (Mee and Jewell, 1978; Rampton et al., 1983; Isgar et al., 1983; Sellu, 1986; Riley et al., 1990; North et al., 1991; Tysk and Jarnerot, 1993). "
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    ABSTRACT: Environmental factors are believed to trigger the onset of Inflammatory bowel disease (IBD). We aimed to evaluate the seasonal variation in the onset of symptoms in patients with IBD and health care seeking behaviour. 282 patients were chosen from the charts. Demographic features, the month and the age at the onset of presenting symptoms and delayed diagnosis term for each patient were analyzed. Cumulative monthly averages analysed by Kruskal Wallis test and Roger's test. Of the 282 patients with IBD, 181 were male (64%). Mean age was 40.1±14.7 years (median: 38, range: 14 to 79 years). The seasonal pattern showed peak in March with 57% and the lowest point in November with 36% (p <0.05). The delayed diagnosis term was 3.0 ± 2.3 months in males vs 3.2 ± 3.2 months in females (p >0.05). The seasonal pattern was not influenced by both genders and by age groups in patients with IBD or UC or CD (p >0.05). We investigated the etiologic environment of IBD and found an interaction between the etiopathogenesis of IBD and environmental risk factors. There was a delay in IBD, but no difference on the health care seeking behaviour between males and females.
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    • "We applied the Box–Jenkins approach to fit time-series data to autoregressive moving average (ARIMA) statistical models, thereby transforming the data into a series of independent, identically distributed random variables [8, 28, 29]. Because the data were seasonal, we used the seasonal ARIMA (SARIMA) extension, which includes seasonal autoregressive and moving average terms as well as a seasonal differencing operator [30, 31], to fit prescription and resistance monthly time series. After differencing each time series to render it stationary , as measured by the Dickey–Fuller unit root test [32], we constructed separate models for each prescription and resistance time series, then diagnosed them for acceptability using the Akaike information criterion (AIC) and Box–Ljung white noise test for residuals. "
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    ABSTRACT: Therapeutic antibiotic use in humans is a significant driver of antibiotic resistance. The seasonal effect of antibiotic use on antibiotic resistance has been poorly quantified because of lack of large-scale, spatially disaggregated time-series data on antibiotic use and resistance. We used time-series analysis (Box-Jenkins) on US antibiotic usage from IMS Health and on antibiotic resistance from The Surveillance Network from 1999-2007 to estimate the effect of aminopenicillin, fluoroquinolone, trimethoprim/sulfamethoxazole, and tetracycline usage on resistance of Escherichia coli to drugs within these classes. We also quantified the effect of fluoroquinolone and macrolide/lincosamide usage on resistance of methicillin-resistant Staphylococcus aureus (MRSA) to ciprofloxacin and clindamycin (which has a similar mode of action to macrolides), respectively. Prevalence of resistant Escherichia coli was significantly correlated with lagged (by 1 month) antibiotic prescriptions for aminopenicillins (0.22, P = .03) and fluoroquinolones (0.24, P = .02), which are highly prescribed, but was uncorrelated to antibiotic classes with lower prescription levels. Fluoroquinolone prescriptions were also significantly correlated with a 1-month lag with the prevalence of ciprofloxacin-resistant MRSA (0.23, P = .03). Large-scale usage of antibiotics can generate seasonal patterns of resistance that fluctuate on a short time scale with changes in antibiotic retail sales, suggesting that use of antibiotics in the winter could have a significant effect on resistance. In addition, the strong correlation between community use of antibiotics and resistance isolated in the hospital indicates that restrictions imposed at the hospital level are unlikely to be effective unless coordinated with campaigns to reduce unnecessary antibiotic use at the community level.
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