Prevalence and Risk Factors of Gastroesophageal Reflux Disease in Bulgaria: An Internet-Based Survey

Abstract

Background: The global pooled prevalence of gastroesophageal reflux disease (GORD) is approximately 14% and varies significantly according to the country. More population-based studies are needed in regions lacking epidemiological data on the prevalence of GORD, such as Eastern Europe, particularly Bulgaria. This current study aimed to evaluate the prevalence of GORD in Bulgaria and assess the risk factors associated with this disorder. Methods: An internet-based health survey was sent to Bulgarian adults. Individuals were invited to complete an online questionnaire on general and gastrointestinal (GI) health. The aim of the study was not explicitly stated. The survey collected data on socio-demographic and behavioral characteristics, validated questions to assess GORD, diagnostic questions based on Rome IV criteria to assess irritable bowel syndrome (IBS) and functional dyspepsia (FD), and questions about antisecretory drugs usage. Results: Data was collected from 1896 individuals (mean age = 35.5 years, SD = 11.7, 73.1% females). The prevalence of GORD in the study population was 27.5%, while 57.29% of all GORD patients taking PPIs had PPI-refractory GORD. Age (p=0.02), BMI (p<0.001), marital status (p=0.03), occupation (p<0.001), sexual problems (p<0.001), FD (p<0.001), and IBS (p<0.001) were significantly associated with GORD prevalence. Patients with FD (p<0.001; OR 5.38), IBS (p=0.03; OR 1.07), and with higher BMI (p<0.001, OR 1.05) were at an increased risk of having GORD. Conclusions: The first data on GORD prevalence in the adult population in Bulgaria has been reported. Disorders of gut-brain interaction have a significant impact on the prevalence of GORD.

Late age at first birth is a protective factor for oesophageal cancer and gastro-oesophageal reflux: the evidence from the genetic study

“…To further control for confounding variables, the PhenoScanner database is employed, facilitating the mitigation of the impact of extraneous covariates. In the present study, the identified confounding factors for the respective outcomes are as follows: for “OO,” the confounding factor is esophageal atresia ( 20 ); for “OV,” diabetes ( 21 ) is a confounding factor; “GOR” is affected by old age and body mass index ( 22 ); and “OC” is influenced by smoking, alcohol consumption, and obesity ( 23 , 24 ). Fifthly, SNPs characterized by palindromic sequences and intermediate allele frequencies are systematically excluded from the analytical process ( 25 ).…”

Gastroesophageal reflux disease, obesity, bronchial asthma: simple combination or complex interaction

“…Глобальная объединенная распространенность ГЭРБ по данным, опубликованным в 2020 году, составила 13,98% и варьирует в зависимости от региона: от 12,9% в Латинской Америке и Карибском бассейне до 19,6% в Северной Америке, от 4,2% в Китае до 22,4% в Турции [9]. Наибольшая распространенность ГЭРБ в Польше, где ГЭРБ страдают около 35,5% взрослых [10], в Болгарии -27,5% [11]. По данным, опубликованным в 2005 году, в Европе распространенность ГЭРБ составляла от 10 до 20%, в то время как в Азии этот показатель был ниже и составлял менее 5% [12], при этом за 20 лет (с 1990 по 2019 гг) в мире количество пациентов с ГЭРБ увеличилось незначительно -на 0,1% [13].…”

“…Ассоциация избыточной массы тела/ожирения и ГЭРБ установлена во многих отечественных и иностранных исследованиях [9,11,[19][20][21]25]. В метаанализе 20 обсервационных исследований с участием в общей сложности 18 346 пациентов с ГЭРБ, избыточный вес и ожирение были связаны с повышением риска развития ГЭРБ в 1,57 и 2,15 раза (избыточный вес OR = 1,57, 95% ДИ=1,36-1,80; ожирение OR = 2,15, 95% ДИ=1,89-2,45), соответственно [22].…”

Phylogenomics reveals the deep ocean as an accelerator for evolutionary diversification in anglerfishes

“…Following recommended practices 84 , we model-averaged rates among the set of models with >5% of the relative Akaike weight, where the contribution of each model towards the mean was proportional to its Akaike weight. We plotted model-averaged rates onto the branches of the tree using the gghisse package v.0.1.1 162 . Note that SSE models avoid issues of identifiability raised by Louca and Pennell 163 because they incorporate multiple information sources to infer rates 164…”