Seasonal variation of peptic ulcer disease, peptic ulcer bleeding, and acute pancreatitis: A nationwide population-based study using a common data model

Seasonal and Geographic Variation in Peptic Ulcer Disease and Associated Complications in the United States of America

BACKGROUND

Hospitalization for peptic ulcer disease (PUD) has been described outside of North America as peaking in the fall and winter. However, no recent literature has so far investigated the seasonal fluctuations and complications of PUD in the USA. Study Design: Cross-sectional population database review.

METHODS

Patients with a diagnosis of either acute gastric or acute duodenal ulcers from January 1, 2015, through December 31, 2017, were identified in the Healthcare Cost and Utilization Project's National Inpatient Sample. The proportion of admissions with either hemorrhage or perforation was determined for each season and further subdivided into geographic regions.

RESULTS

Of 18829 hospitalizations for PUD, admissions were the highest in the fall (25.9%) while being the lowest in the summer (23.9%). Complications, hemorrhage or perforation, were the highest and the lowest in the fall and spring, respectively (75.7% vs. 73.6%; P=0.060 for comparing all 4 seasons). Geographically, the West had the highest rate of peptic ulcer hemorrhage (64.5%, P=0.004), while the northeast had the highest rate of perforation (14.3%, P=0.003). Hemorrhage was more common in males, those who used aspirin, nonsteroidal anti-inflammatory drugs, or anticoagulants, and diabetics (P<0.05). Perforation was less common in males, those with diabetes, obesity, or hypertension (HTN), or those using aspirin or anticoagulants (P<0.05). Helicobacter pylori infection was more associated with perforation in the fall and winter months.

CONCLUSION

Seasonal and regional trends in hospitalizations due to PUD may help identify modifiable risk factors, which can improve diagnostic and treatment outcomes for patients by allowing for more targeted identification of vulnerable populations.

Deprescribing Strategies: A Prospective Study on Proton Pump Inhibitors

Proton pump inhibitors (PPIs) are among the most controversially prescribed drugs in polypharmacy. This observational prospective study assessed the PPI prescriptive trend during hospitalization before and after implementation of a prescribing/deprescribing algorithm in a real-life hospital setting and the related clinical-economic benefit at discharge. PPI prescriptive trends were compared between three quarters of 2019 (9 months) and the same period of 2018 by a chi-square test with a Yate's correction. The proportions of treated patients in the two years (1120 discharged patients in 2018 and 1107 in 2019) were compared by the Cochran-Armitage trend test. DDDs (defined daily doses) were compared between 2018 and 2019 by the non-parametric Mann-Whitney test and normalizing DDD/DOT (days of therapy) and DDD/100 bd (bed days) for each patient. Multivariate logistic regression was performed on PPI prescriptions at discharge. The distribution of patients with PPIs at discharge was significantly different in the two years (p = 0.0121). There was a downward trend in the number of PPI prescriptions (29.9%) in the third trimester of 2019 compared to the others of the same year (first trimester: 34.1%, second trimester: 36.0%) and by contrast with the same periods of 2018 (29.4, 36.0, and 34.7%) (p = 0.0124). DDDs/patient did not differ between 2018 and 2019 nor across the three trimesters. However, both DDD/DOT and DDD/100 bd showed a decrease in the third trimester of 2019, with a marked difference for DDD/DOT (p = 0.0107). The reduction in consumption detected in the last phase of 2019 in terms of DDD/DOT was 0.09 with a consequent containment of pharmaceutical spending. The development and implementation of multidisciplinary prescribing/deprescribing protocols in both hospital and community settings could lead to a reduction in the misuse of PPIs, with significant savings in healthcare resources.

Regional and seasonal variations in functional abdominal pain and functional constipation prevalence among Saudi children

Objectives:

This study aimed to evaluate functional abdominal pain disorders and functional constipation prevalence in the central region of Saudi Arabia, and compare it to that of the western region.

Methods:

This was a cross-sectional study using online questionnaires targeting the general population of Riyadh region of Saudi Arabia. Subjects were randomly selected by sharing links on social media groups. Any parent with a 3–18-year-old child was included, and children with chronic medical illnesses or symptoms of organic GI disorders were excluded.

Results:

Three hundred nineteen subjects were included in the final analysis; the prevalence of functional abdominal pain disorders overall was 6.2% and the prevalence of functional constipation was 8.1%.

Conclusions:

Functional constipation diagnosis seems to be affected by life stressors or a previous viral illness. Seasonal variations had minimal effect on functional abdominal pain disorder and functional constipation symptom frequency and severity.

Empirical assessment of alternative methods for identifying seasonality in observational healthcare data

Background

Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help guide appropriate responses. Observational data, however, are not comprised of biological events, but timestamped diagnosis codes the combination of which (along with additional requirements) are used as proxies for biological events. As there exist different methods for determining the seasonality of a time series, it is necessary to know if these methods exhibit concordance. In this study we seek to determine the concordance of these methods by applying them to time series derived from diagnosis codes in observational data residing in databases that vary in size, type, and provenance.

Methods

We compared 8 methods for determining the seasonality of a time series at three levels of significance (0.01, 0.05, and 0.1), against 10 observational health databases. We evaluated 61,467 time series at each level of significance, totaling 184,401 evaluations.

Results

Across all databases and levels of significance, concordance ranged from 20.2 to 40.2%. Across all databases and levels of significance, the proportion of time series classified seasonal ranged from 4.9 to 88.3%. For each database and level of significance, we computed the difference between the maximum and minimum proportion of time series classified seasonal by all methods. The median within-database difference was 54.8, 34.7, and 39.8%, for p < 0.01, 0.05, and 0.1, respectively.

Conclusion

Methods of binary seasonality classification when applied to time series derived from diagnosis codes in observational health data produce inconsistent results. The methods exhibit considerable discord within all databases, implying that the discord is a result of the difference between the methods themselves and not due to the choice of database. The results indicate that researchers relying on automated methods to assess the seasonality of time series derived from diagnosis codes in observational data should be aware that the methods are not interchangeable and thus the choice of method can affect the generalizability of their work. Seasonality determination is highly dependent on the method chosen.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01652-3.