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  1. Imawana RA, Smith DR, Goodson ML
    Ann Gastroenterol, 2020 06 06;33(5):485-494.
    PMID: 32879595 DOI: 10.20524/aog.2020.0507
    Background: The current literature suggests a protective benefit of Helicobacter pylori (H. pylori) infection against inflammatory bowel disease (IBD). Here we assessed whether this effect varied by IBD subtype-Crohn's disease (CD) or ulcerative colitis (UC)-and geographic region: East Asia, Europe (non-Mediterranean) or Mediterranean region.

    Methods: A database search was performed up to July 2019 inclusive for all studies that compared H. pylori infection in IBD patients vs. non-IBD controls. The relative risk (RR) was used to quantify the association between IBD and H. pylori, and the effects were combined across studies using a mixed-effects meta-regression model, which included IBD subtype and geographic region as categorical moderator variables.

    Results: Our meta-regression model exhibited moderate heterogeneity (I2=48.74%). Pooled RR depended on both region (P=0.02) and subtype (P<0.001). Pooled RRs were <1 for all subtype and region combinations, indicative of a protective effect of H. pylori against IBD. The pooled RR was 28% (9%, 50%; P=0.001) greater for UC vs. CD and 43% (4%, 96%; P=0.02) greater for Mediterranean countries vs. East Asia. The pooled RR was 18% (-13%, 60%; P=0.48) greater for Europe vs. East Asia and 21% (-13%, 68%; P=0.42) greater for Mediterranean vs. Europe, though these differences were not statistically significant.

    Conclusions: The protective effect of H. pylori on IBD varied by both subtype (more protection against CD vs. UC) and region (East Asia more protected than Mediterranean regions). Variation due to these effects could provide insight into IBD etiology.

  2. Smith DR, Behzadnia A, Imawana RA, Solim MN, Goodson ML
    Sci Rep, 2021 07 14;11(1):14478.
    PMID: 34262067 DOI: 10.1038/s41598-021-91644-y
    The prevalence of smokers is a major driver of lung cancer incidence in a population, though the "exposure-lag" effects are ill-defined. Here we present a multi-country ecological modelling study using a 30-year smoking prevalence history to quantify the exposure-lag response. To model the temporal dependency between smoking prevalence and lung cancer incidence, we used a distributed lag non-linear model (DLNM), controlling for gender, age group, country, outcome year, and population at risk, and presented the effects as the incidence rate ratio (IRR) and cumulative incidence rate ratio (IRRcum). The exposure-response varied by lag period, whilst the lag-response varied according to the magnitude and direction of changes in smoking prevalence in the population. For the cumulative lag-response, increments above and below the reference level was associated with an increased and decreased IRRcum respectively, with the magnitude of the effect varying across the lag period. Though caution should be exercised in interpretation of the IRR and IRRcum estimates reported herein, we hope our work constitutes a preliminary step towards providing policy makers with meaningful indicators to inform national screening programme developments. To that end, we have implemented our statistical model a shiny app and provide an example of its use.
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