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We assessed the incidence and prevalence of hepatitis B (Hep B) in patients with or without diabetes mellitus using the UK Clinical Practice Research Datalink. This was a retrospective, observational study of diabetic and non-diabetic cohorts aged 0–80 years using Clinical Practice Research Datalink (NCT02324218). Incidence rates (IR) for each coho...
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Context 1
... (established by post-hoc analysis, Table 1) from HES records was considered first and if it was missing or unknown, then the ethnicity recorded during the GP visit was considered. ...
Context 2
... the eligible 7,551,283 subjects, the non-DM cohort included a random sample of 4,679,010 subjects ( Figure 1). The mean ages were 54.4 years and 32.0 years, 57.20% and 50.14% were male and ethnicity data was available for 62% and 50% of subjects in the DM and non-DM cohorts, respectively (Table 1). In the DM cohort, 95.85% of subjects had type 2 diabetes, 4.14% had type 1 diabetes and 0.01% were non-specified. ...
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Citations
... Our results showed that patients with diabetes had a higher prevalence of HBV and HCV infections than those without. Similarly, a previous cohort study conducted in the United Kingdom observed a higher prevalence of HBV in patients with diabetes than in those without diabetes [27]. Another previous study from Pakistan Hospital also indicated that patients with diabetes were more associated with HCV infection than the non-diabetic group (OR = 3.03; 95% CI, 2.64-3.48, ...
This study aimed to identify the development of hepatitis B or C infection in diabetes patients compared to those without and to elucidate factors associated with the prevalence of hepatitis B or C infection in diabetes. We conducted a cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2013–2018. As evaluation factors, we included variables such as age, race, illicit drug use, and poverty. The diabetic group had a significantly higher prevalence of hepatitis B or C infection than the non-diabetic group (odds ratio (OR) = 1.73; 95% confidence interval (CI), 1.36–2.21, p < 0.01). In multivariate Cox regression, non-poverty and non-illicit drug use were lower risk factors contributing to hepatitis development in diabetes (hazard ratio (HR) = 0.50; 95% CI, 0.32–0.79, p < 0.01, and HR = 0.05; 95% CI, 0.03–0.08, p < 0.01, respectively). Logistic regression also showed that these factors were significant contributors to hepatitis development in the diabetic group (p < 0.01). In patients with diabetes, the development of hepatitis was higher than that in those without, and hepatitis development was influenced by poverty and illicit drug use. This may provide supporting evidence of response strategies for diabetes to care for hepatitis development in advance.
... These individuals may include a disproportionate number in vulnerable groups, including migrants 26 (and perhaps specifically non-English speakers), people who inject drugs 27 and those in prison or detention centres. 28 Although comparable HBV datasets are more available in other countries, such as China and the USA, [29][30][31] there are scarce comprehensive data of HBV in the UK except for data reported from certain populations [32][33][34] or the primary care population. 5,6 We believe this secondary care cohort can start to fill evidence gaps, especially by collating laboratory, imaging and treatment data which are not currently well captured in primary care. ...
Key Messages:
The National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) has established a cohort of individuals with chronic hepatitis B virus (HBV) infection in secondary care in the UK, providing a resource for translational research.
The dataset comprises >6000 individuals (99% adults aged 18–88, 1% children aged 1–17) with diverse ethnicities (32% Asian, 23% Black, 30% White and 15% mixed or other ethnic groups) from six NHS Trusts across England, representing data collected between August 1994 and October 2021.
The dataset is populated with routinely collected clinical data captured from electronic patient record (EPR) systems; follow-up frequency of each individual depends on clinical practice, with a median of 5.1 (IQR: 2.8–8.0) years.
Data on demographics, laboratory tests, antiviral treatment, elastography scores, imaging/biopsy reports, death information and potential risk factors for liver disease have been collected.
Over time, the cohort will continue to grow in size, average follow-up duration will increase and more NHS Trusts will participate.
This dataset offers important opportunities for epidemiological studies and biomedical informatics research, as well as characterizing an HBV population for clinical trials, including external collaborations with industry.
Collaborations are welcomed, further details are available at [https://hic.nihr.ac.uk]. Queries regarding data access should be directed to [orh-tr.nihrhic@nhs.net].
... Clinical practice indicates that long-term hyperglycemia will cause obvious injuries to eyes, kidneys, heart, blood vessels, and nerves, resulting in dysfunction of vital organs [7]. An epidemiological study suggests that global patients with diabetes occupy about 5.7% of the total population worldwide, and there are additional 7 million patients with diabetes across the globe every year [8]. Due to the aging population and the adjustment of dietary structure, the number of patients with diabetes has been on the rise in China. ...
Objective:
To investigate the impact of dietary management using teach-back method on glucose and lipid metabolism and quality of life in patients with hepatitis B complicated with diabetes.
Methods:
A total of 110 patients with hepatitis B complicated with diabetes admitted to our hospital were selected as the study subjects, and divided into Group A (n=60) and Group B (n=50) in accordance with different intervention measures. Group A was treated with dietary management using teach-back method, while Group B was treated with conventional dietary management. The two groups received follow-up observations. The changes in indices of glucose and lipid metabolism within 12 months after intervention were recorded and compared between the two groups, and the changes in treatment adherence, dietary adherence and quality of life scores were compared between the two groups before and after intervention.
Results:
The results showed that at month 1-12 after intervention, Group A had lower levels of fasting plasma glucose (FPG), 2-h post-load plasma glucose (2hPG), hemoglobin A1c (HbA1c), triglycerides (TG), and total cholesterol (TC) compared with Group B (P < 0.05). The comparison of treatment adherence revealed that the good and excellent adherence rate in Group A was noticeably higher than that in Group B after intervention (93.33% VS. 70.00%) (P < 0.05). The scores of dietary adherence attitude and behavior and quality of life in Group A were also remarkably higher than those in Group B after intervention (P < 0.05).
Conclusion:
The dietary management using teach-back method can effectively improve the indices of glucose and lipid metabolism, treatment adherence, dietary adherence, and quality of life in patients with hepatitis B complicated with diabetes.
... Unlike the Centers for Disease Control and Prevention and American Diabetes Association, the World Health Organization Regional Office for Europe, European Association for the Study of Diabetes, and the European Center for Disease Control do not provide guidance on vaccination of patients with DM. In several countries in the European Union, there are currently no recommendations for HBV vaccination of people with DM [39,40]. With HBV outbreaks in people with DM similar to those in the United States, European healthcare providers and policymakers may need to consider strategies to protect people with diabetes against hepatitis B and, if desired, create a framework to provide HBV vaccination to these patients. ...
Background
Hepatitis B virus (HBV) remains a major public health issue, although it is a vaccine-preventable disease. Adults with diabetes are at greater risk of contracting HBV than the general population. Commonly used 3-dose HBV vaccines have reduced immunogenicity in older individuals and in those with diabetes mellitus.
Methods
In this post hoc analysis of a phase 3 clinical trial, participants with type 2 diabetes mellitus aged 60–70 years received either 2-dose HBsAg/CpG 1018 (HEPLISAV-B®, n = 327) at 0 and 4 weeks and placebo at 24 weeks or 3-dose HBsAg/alum (Engerix-B®, n = 153) at 0, 4, and 24 weeks. Immunogenicity, including seroprotection rate (SPR) at week 28, and safety were assessed by subgroup (sex, body mass index, and smoking status). SPR was defined as antibody against hepatitis B surface antigen serum concentration ≥10 mIU/mL.
Results
The SPR at week 28 was significantly higher with HBsAg/CpG 1018 (85.8% [235/274]) than with HBsAg/alum (58.5% [76/130]) in the per-protocol analysis, for an overall difference of 27.3% (95% CI, 18.0–36.8). SPRs with HBsAg/CpG 1018 were consistently markedly higher compared with HBsAg/alum, regardless of sex, body mass index, or smoking status. Adverse events and deaths were comparable between groups.
Conclusions
Two-dose HBsAg/CpG 1018 provides a higher level of seroprotection against HBV than does a 3-dose vaccine (HBsAg/alum) with a similar safety profile in patients aged 60–70 years with type 2 diabetes mellitus.
Study identifier: NCT02117934.
... A study by Uddin et al. [22] found that HBV infection and transmission are geographically more prevalent in Asian regions. Ferreira et al. [23] reported that the risk of incident HBV diagnosis in the diabetes cohort was not different from that in the non diabetes cohort in UK population. However, bayramer et al claim that HBV and HCV associated hospitalization rate was higher in diabetes than in non diabetes cohort [24], which correlate with the present study. ...
This is a brief summary of the prevalence on Hepatitis C (HCV) and Hepatitis B (HBV) viral infections and associated risk factors in Type 2 diabetes subjects. Prevalence of HBV (9%) was higher compared to HCV (2%) infection in the screened 388 subjects. Results showed that these infections are independent of the liver damage. Risk factors prominently observed among positive HCV and HBV cases were longer duration of diabetes, hospital admission, history of jaundice and history of surgeries which enlightened the importance of hepatitis vaccination once the subject is diagnosed with diabetes.
A significant number of studies invoked diabetes as a risk factor for virus infections, but the issue remains controversial. We aimed to examine whether non-autoimmune diabetes mellitus enhances the risk of virus infections compared with the risk in healthy individuals without non-autoimmune diabetes mellitus. In this systematic review and meta-analysis, we assessed case-control and cohort studies on the association between non-autoimmune diabetes and viruses. We searched PubMed, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Web of Science with no language restriction, to identify articles published until February 15, 2021. The main outcome assessment was the risk of virus infection in individuals with non-autoimmune diabetes. We used a random-effects model to pool individual studies and assessed heterogeneity (I²) using the χ2 test on Cochrane’s Q statistic. This study is registered with PROSPERO, number CRD42019134142. Out of 3136 articles identified, we included 68 articles (90 studies, as the number of virus and or diabetes phenotype varied between included articles). The summary OR between non-autoimmune diabetes and virus infections risk were, 10.8(95% CI: 10.3–11.4; 1-study) for SARS-CoV-2; 3.6(95%CI: 2.7–4.9, I² = 91.7%; 43-studies) for HCV; 2.7(95% CI: 1.3–5.4, I² = 89.9%, 8-studies;) for HHV8; 2.1(95% CI: 1.7–2.5; 1-study) for H1N1 virus; 1.6(95% CI: 1.2–2.13, I² = 98.3%, 27-studies) for HBV; 1.5(95% CI: 1.1–2.0; 1-study) for HSV1; 3.5(95% CI: 0.6–18.3 , I² = 83.9%, 5-studies) for CMV; 2.9(95% CI: 1–8.7, 1-study) for TTV; 2.6(95% CI: 0.7–9.1, 1-study) for Parvovirus B19; 0.7(95% CI: 0.3–1.5 , 1-study) for coxsackie B virus; and 0.2(95% CI: 0–6.2; 1-study) for HGV. Our findings suggest that, non-autoimmune diabetes is associated with increased susceptibility to viruses especially SARS-CoV-2, HCV, HHV8, H1N1 virus, HBV and HSV1. Thus, these viruses deserve more attention from diabetes health-care providers, researchers, policy makers, and stakeholders for improved detection, overall proper management, and efficient control of viruses in people with non-autoimmune diabetes.
Objective
Clinical code sets are vital to research using routinely-collected electronic healthcare data. Existing code set engineering methods pose significant limitations when considering reproducible research. To improve the transparency and reusability of research, these code sets must abide by FAIR principles; this is not currently happening. We propose ‘term sets’, an equivalent alternative to code sets that are findable, accessible, interoperable and reusable.
Materials and methods
We describe a new code set representation, consisting of natural language inclusion and exclusion terms (term sets), and explain its relationship to code sets. We formally prove that any code set has a corresponding term set. We demonstrate utility by searching for recently published code sets, representing them as term sets, and reporting on the number of inclusion and exclusion terms compared with the size of the code set.
Results
Thirty-one code sets from 20 papers covering diverse disease domains were converted into term sets. The term sets were on average 74% the size of their equivalent original code set. Four term sets were larger due to deficiencies in the original code sets.
Discussion
Term sets can concisely represent any code set. This may reduce barriers for examining and reusing code sets, which may accelerate research using healthcare databases. We have developed open-source software that supports researchers using term sets.
Conclusion
Term sets are independent of clinical code terminologies and therefore: enable reproducible research; are resistant to terminology changes; and are less error-prone as they are shorter than the equivalent code set.
Introduction
The link between diabetes mellitus and hepatitis B and C Virus infections has not yet been studied in the Democratic Republic of Congo, a country where diabetes mellitus is a growing disease and the prevalence of hepatitis B and C viruses infections is high. The aim of this study was to determine the seroprevalence of these viruses in diabetic patients.
Methods
We conducted a descriptive cross-sectional study in diabetic subjects attending Kisangani University Clinics and General Hospitals of Kisangani City as well as the Diabetics Association of Oriental Province. The control group consisted of volunteer blood donors recruited from the Kisangani Provincial Blood Transfusion Center. Blood glucose was measured with the spectrophotometer; for hepatitis B and hepatitis C viruses serology, we used rapid test kits (Determine TM® HBsAg and Hexagon® HCV test) and ELISA if seropositivity by rapid tests. The analysis was done by SPSS software.
Results
Seroprevalence of hepatitis C virus in diabetics was 24.8% compared to 1.9% in volunteer blood donors (p = 0.0000); that of hepatitis B virus was 3.4% versus 3.5% in volunteer blood donors (p = 0.906). Hepatitis C virus infection was more common in type 2 diabetics (p = 0.006) and significantly associated with age of diabetic patients (p = 0.002).
Conclusion
The seroprevalence of hepatitis C virus and not hepatitis B virus infection is significantly high in diabetic subjects, particularly type 2 diabetics, in the Democratic Republic of Congo and suggests systematic screening for this infection in any diabetic patient.
I read with interest the recently published article by Ferreira et al., where the authors have reported that despite a similar risk of incident hepatitis B virus (HBV) infection between a diabetic and a non‐diabetic cohort, the prevalence of HBV infection and HBV related hospitalization rate was higher in the diabetic cohort. This is a well‐conducted study; however, interpretation of such results must be mindful of two facts: first, the composition of the two cohorts and the temporal relationship between onset of HBV infection and diabetes mellitus (DM) were not uniform between incidence and prevalence analyses; and second, the clinical profile of the patients, which determines hospitalization rate, was not provided.
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