Seasonal variations in surgical outcomes

NewsBot · Nov 7, 2007

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Medscape have the ful text of this paper from Annals of Surgery (free registration required to access)

Seasonal Variation in Surgical Outcomes as Measured by the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP).
Michael J. Englesbe, MD; Shawn J. Pelletier, MD; John C. Magee, MD; Paul Gauger, MD; Tracy Schifftner, MS; William G. Henderson, PhD; Shukri F. Khuri, MD; Darrell A. Campbell, MD

Objective: We hypothesize that the systems of care within academic medical centers are sufficiently disrupted with the beginning of a new academic year to affect patient outcomes.

Methods: This observational multiinstitutional cohort study was conducted by analysis of the National Surgical Quality Improvement Program-Patient Safety in Surgery Study database. The 30-day morbidity and mortality rates were compared between 2 periods of care: (early group: July 1 to August 30) and late group (April 15 to June 15). Patient baseline characteristics were first compared between the early and late periods. A prediction model was then constructed, via stepwise logistic regression model with a significance level for entry and a significance level for selection of 0.05.

Results: There was 18% higher risk of postoperative morbidity in the early (n = 9941) versus the late group (n = 10313) (OR 1.18, 95%, CI 1.07-1.29, P = 0.0005, c-index 0.794). There was a 41% higher risk for mortality in the early group compared with the late group (OR 1.41, CI 1.11-1.80, P = 0.005, c-index 0.938). No significant trends in patient risk over time were noted.

Conclusion: Our data suggests higher rates of postsurgical morbidity and mortality related to the time of the year. Further study is needed to fully describe the etiologies of the seasonal variation in outcomes.
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NewsBot · Sep 6, 2022

Seasonal Variation for Plantar Fasciitis: Evidence from Google Trends Search Query Data
Seok-Min Hwang,Seok Kim andSuk-Hyun Hwang
Healthcare 2022, 10(9), 1676

We aimed to determine the seasonal trends in internet searches for plantar fasciitis and related symptoms in various countries using search engine query data on Google. We used Google Trends to obtain internet search query data from January 2009 to December 2019. We collected monthly search volumes for the query terms “plantar fasciitis” and “heel pain” in the USA, Canada, the U.K., Ireland, Australia, and New Zealand. Statistical analysis of the seasonal effects on plantar fasciitis was performed using a cosinor model. The cosinor analyses confirmed statistically significant seasonal patterns in the relative search volumes for the terms “plantar fasciitis” and “heel pain” in the USA, Canada, the U.K., Ireland, and Australia, with peaks during the summer and troughs during the winter. For New Zealand, the seasonal trend was statistically significant only for the term “plantar fasciitis”, while a similar trend for the term “heel pain” was present without achieving statistical significance for seasonality. This seasonality is thought to be related to more frequent occurrence of plantar fasciitis due to increased physical activity of people during the warmer months. In this study, the search query data using the terms “plantar fasciitis” and “heel pain” on Google Trends show significant seasonal variation across several countries, with a peak in the summer and a trough in the winter.
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