Is the Utilization of Helicobacter pylori Stool Antigen Tests Appropriate in an Urban Canadian Population?

Lifestyle-based nomogram for identifying the Chaoshan inhabitants of China at high risk of Helicobacter pylori infection

BACKGROUND

Helicobacter pylori (HP) infection is associated with various diseases. Early detection can prevent the onset of illness. We constructed a nomogram to predict groups at high risk of HP infection.

METHODS

Patients who underwent regular medical check-ups at hospital in Chaoshan, China from March to September 2022 were randomly allocated to the training and validation cohorts. Risk factors including basic characteristics and lifestyle habits associated with HP infection were analyzed by logistic regression analyses. The independent varieties were calculated and plotted into a nomogram. The nomogram was internally validated by receiver operating characteristic curve, calibration, and decision curve analyses (DCAs).

RESULTS

Of the 945 patients, 680 were included in the training cohort and 265 in the validation cohort. 356 patients in training cohort with positive 13 C-UBT results served as the infected group, and 324 without infection were the control group. The multivariate regression analyses showed that the risk factors for HP infection included alcohol consumption (OR = 1.29, 95%CI = 0.78-2.13, P = 0.03), family history of gastric disease (OR = 4.35, 95%CI = 1.47-12.84, P = 0.01), living with an HP-positive individual (OR = 18.09, 95%CI = 10.29-31.82, P < 0.0001), drinking hot tea (OR = 1.58, 95%CI = 1.05-2.48, P = 0.04), and infection status of co-drinkers unknown (OR = 2.29, 95%CI = 1.04-5.06, P = 0.04). However, drinking tea > 3 times per day (OR = 0.56, 95%CI = 0.33-0.95, P = 0.03), using serving chopsticks (OR = 0.30, 95%CI = 0.12-0.49, P < 0.0001) were protective factors for HP infection. The nomogram had an area under the curve (AUC) of 0.85 in the training cohort. The DCA was above the reference line within a large threshold range, indicating that the model was better. The calibration analyses showed the actual occurrence rate was basically consistent with the predicted occurrence rate. The model was validated in the validation cohort, and had a good AUC (0.80), DCA and calibration curve results.

CONCLUSIONS

This nomogram, which incorporates basic characteristics and lifestyle habits, is an efficient model for predicting those at high risk of HP infection in the Chaoshan region.

The Impact of COVID-19 on Laboratory Test Utilization at a Pediatric Medical Center

Background

The epidemiology and clinical manifestation of coronavirus disease 2019 (COVID-19) in the pediatric population is different from the adult population. The purpose of this study is to identify effects of the COVID-19 pandemic on laboratory test utilization in a pediatric hospital.

Methods

We performed retrospective analysis on test utilization data from Ann & Robert H. Lurie Children’s Hospital of Chicago, an academic pediatric medical center. Data between two 100-day periods prior to (prepandemic) and during the pandemic (mid-pandemic) were analyzed to evaluate changes in test volume, lab utilization, and test positivity rate. We also evaluated these metrics based on in- vs outpatient testing and performed modeling to determine what variables significantly impact the test positivity rate.

Results

During the pandemic period, there was an expected surge in COVID-19 testing, while over 84% of lab tests studied decreased in ordering volume. The average number of tests ordered per patient was not significantly different during the pandemic for any of the laboratories (adjusted P value &gt; 0.05). Thirty-three studied tests showed significant change in positivity rate during the pandemic. Linear modeling revealed test volume and inpatient status as the key variables associated with change in test positivity rate.

Conclusions

Excluding severe acute respiratory syndrome coronavirus 2 tests, the COVID-19 pandemic has generally led to decreased test ordering volume and laboratory utilization. However, at this pediatric hospital, the average number of tests performed per patient and test positivity rates were comparable between pre- and mid-pandemic periods. These results suggest that, overall, clinical test utilization at this site remained consistent during the pandemic.

Geospatial analysis of Helicobacter pylori infection in South Australia: Should location influence eradication therapy?

BACKGROUND AND AIM

Rates of antimicrobial-resistant Helicobacter pylori infection are rising globally; however, geospatial location and its interaction with risk factors for infection have not been closely examined.

METHODS

Gastric biopsy specimens were collected to detect H. pylori infection at multiple centers in Adelaide, South Australia, between 1998 and 2017. The geospatial distribution of antibiotic-resistant H. pylori in the Greater Adelaide region was plotted using choropleth maps. Moran's I was used to assess geospatial correlation, and multivariate linear regression (MLR) was used to examine associations between migration status, socioeconomic status, age, gender, and rates of H. pylori positivity and antibiotic resistance. Geographically weighted regression (GWR) was used to determine the extent to which the associations varied according to geospatial location.

RESULTS

Of 20 108 biopsies across 136 postcodes within the Greater Adelaide region, 1901 (9.45%) were H. pylori positive. Of these, 797 (41.9%) displayed clarithromycin, tetracycline, metronidazole, or amoxicillin resistance. In MLR, migration status was associated with the rate of H. pylori positivity (β = 3.85% per 10% increase in a postcode's migrant population; P < 0.001). H. pylori positivity and resistance to any antibiotic were geospatially clustered (Moran's I = 0.571 and 0.280, respectively; P < 0.001 for both). In GWR, there was significant geospatial variation in the strength of the migrant association for both H. pylori positivity and antibiotic resistance.

CONCLUSION

Our study demonstrates the heterogeneous geospatial distribution of H. pylori positivity and antibiotic resistance, as well as its interaction with migrant status. Geographic location and migrant status are important factors to consider for H. pylori eradication therapy.