Investigating the Effect of Antibiotic Exposure on the Prevalence of Antibiotic-resistant H. pylori Infection and the Incidence of Anti-H. pylori Treatment Failure in Northern Canadian Communities
Bibliographic record
Abstract
The frequent and improper use of antibiotics for the treatment of unrelated bacterial infections is believed to influence the development of antibiotic-resistant H. pylori infection. Antibiotic-resistant H. pylori infection is a major risk factor for the failure of anti-H. pylori treatment regimens to eliminate the infection. Considering the limited therapeutic options available to eliminate H. pylori infection, it is of public health interest to better understand factors that impact the occurrence of antibiotic resistance in H. pylori to inform strategies to prevent its occurrence and improve the effectiveness of anti-H. pylori treatment regimens. Northern Canadian Aboriginal communities are disproportionately affected by H. pylori infection relative to southern Canadian communities. In response to community concerns regarding H. pylori-associated health outcomes, the Canadian North Helicobacter pylori (CANHelp) Working Group conducts community-driven research to inform public health policy pertaining to control of H. pylori infection. One aim of the CANHelp Working Group is to improve the clinical management of H. pylori infection in northern Canadian communities by identifying factors that limit the effectiveness of anti-H. pylori treatment regimens. To better understand factors associated with antibiotic-resistant H. pylori infection and anti-H. pylori treatment effectiveness, this thesis aims to estimate among CANHelp project participants the effect of exposure to antibiotics on the prevalence of antibiotic-resistant H. pylori infection and incidence of anti-H. pylori treatment failure. I completed a systematic review of the literature published during 2010 to 2014 to synthesize current evidence of the prevalence of antibiotic-resistant H. pylori infection worldwide and antibiotic resistance mechanisms in H. pylori infection. To estimate the effect of antibiotic exposure on the prevalence of antibiotic-resistant H. pylori infection and the incidence of anti-H. pylori treatment failure, I developed a chart review tool to collect antibiotic exposure histories from participant medical charts for the 5-year period before enrollment in a CANHelp community project for H. pylori-positive participants who either had samples of H. pylori cultured from gastric biopsies or who had completed a post-treatment breath test to classify their post-treatment infection status. I used logistic regression to estimate odds ratios and 95% confidence intervals (CIs) for the effect of antibiotic exposure on the prevalence of antibiotic-resistant H. pylori infection among individuals who had H. pylori organisms cultured from gastric biopsy samples. I used robust Poisson regression and binomial regression to estimate risk ratios with 95% CIs and risk differences with 95% CIs, respectively, for the effect of antibiotic exposure on the average risk of treatment failure among individuals who completed a post-treatment breath test. I also completed a qualitative analysis of semi-structured interviews to describe participants’ perspectives on the value and potential harms of using antibiotics to treat infections and factors that influence their adherence to prescribed regimens. Results of my quantitative analysis provide evidence that suggest a higher frequency of antibiotic exposure was associated with the prevalence odds of antibiotic-resistant H. pylori infection as well as the risk of anti-H. pylori treatment failure in CANHelp communities. Additionally, the results suggest that previous exposure to anti-H. pylori treatment regimens is associated with both the prevalence odds of antibiotic-resistant H. pylori infection and the average risk of anti-H. pylori treatment failure in CANHelp communities. Results of my qualitative analysis provide evidence of widespread knowledge among participants of the importance of adherence to prescribed antibiotics regimens. Factors that influence adherence to prescribed antibiotics most frequently identified by participants included the occurrence of adverse events and forgetfulness. Given that poor adherence to prescribed antibiotic regimens is known to influence the occurrence of antibiotic-resistant bacterial infections, strategies that support individuals to manage adverse events and remember to take their medications may be useful to improve the effectiveness of anti-H. pylori treatment regimens, which would decrease the frequency of repeated exposure to such regimens and, in turn, decrease the occurrence of resistant H. pylori infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".