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Objectives: To examine the incidence of Campylobacter and Salmonella infection in patients prescribed proton pump inhibitors (PPIs) compared with controls., Methods: Retrospective cohort study using anonymous general practitioner (GP) data. Anonymised individual-level records from the Secure Anonymised Information Linkage (SAIL) system between 1990 and 2010 in Wales were selected. Data were available from 1,913,925 individuals including 358,938 prescribed a PPI. The main outcome measures examined included incidence of Campylobacter or Salmonella infection following a prescription for PPI., Results: The rate of Campylobacter and Salmonella infections was already at 3.1-6.9 times that of non-PPI patients even before PPI prescription. The PPI group had an increased hazard rate of infection (after prescription for PPI) of 1.46 for Campylobacter and 1.2 for Salmonella, compared with baseline. However, the non-PPI patients also had an increased hazard ratio with time. In fact, the ratio of events in the PPI group compared with the non-PPI group using the prior event rate ratio was 1.17 (95% CI 0.74-1.61) for Campylobacter and 1.00 (0.5-1.5) for Salmonella., Conclusions: People who go on to be prescribed PPIs have a greater underlying risk of gastrointestinal (GI) infection beforehand and they have a higher prevalence of risk factors before PPI prescription. The rate of diagnosis of infection is increasing with time regardless of PPI use, and there is no evidence that PPI is associated with an increase in diagnosed GI infection. It is likely that factors associated with the demographic profile of the patient are the main contributors to increased rate of GI infection for patients prescribed PPIs.

(1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990–2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21–25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis. [ABSTRACT FROM AUTHOR]