| 1. |
- Davídsdóttir, Lóa, et al.
(författare)
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Hepatocellular carcinoma in individuals with HBV infection or HBV-HCV co-infection in a low endemic country
- 2010
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Ingår i: Scandinavian journal of gastroenterology. - : Informa UK Limited. - 1502-7708 .- 0036-5521. ; 45:7-8, s. 944-952
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: The aim of this nationwide cohort study was to assess the risk for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B virus (HBV) infection or HBV and hepatitis C virus (HCV) co-infection in Sweden, a low endemic country.MATERIAL AND METHODS: A total of 12,080 patients with HBV and 3238 patients with HBV-HCV co-infection were notified to the Swedish institute for Infectious Disease Control between 1990 and 2004. After excluding 1850 patients with acute HBV and 584 patients infected in adult life, we analyzed the cohort of 9646 subjects with chronic HBV infection. In the co-infection cohort, 1697 patients were analyzed after excluding 1541 cases with acute HBV. The Swedish national cancer registry was used for follow-up. The HCC incidence rate in the cohorts was compared with the HCC incidence rate in the general population and the standardized incidence ratio (SIR) was calculated for different strata according to estimated infection period.RESULTS: HCC was found in 45 patients in the HBV cohort. In the stratum of 40-49 years of infection we found a SIR of 47 and in stratum 50-59 years the SIR was 54. In the co-infected cohort 10 HCCs were found. The SIR in the stratum 20-29 years of infection was 34 and the SIR in the stratum 30 years and over was 91.CONCLUSIONS: This national cohort study of HBV infected and HBV-HCV co-infected subjects in a low endemic country confirms a highly increased risk of liver cancer compared to the general population.
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| 2. |
- Hofmann, Jonathan N., et al.
(författare)
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Risk of kidney cancer and chronic kidney disease in relation to hepatitis C virus infection : a nationwide register-based cohort study in Sweden
- 2011
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Ingår i: European Journal of Cancer Prevention. - 0959-8278 .- 1473-5709. ; 20:4, s. 326-30
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Tidskriftsartikel (refereegranskat)abstract
- Chronic hepatitis C virus (HCV) infection is an established cause of liver cancer, and recent studies have suggested a link with kidney cancer. The aim of this study was to evaluate risk of kidney cancer in relation to HCV infection in a nationwide registry-based study of Swedish residents diagnosed with HCV between 1990 and 2006. A total of 43 000 individuals with chronic HCV infection were included, and the mean follow-up time was 9.3 years. Observed kidney cancer incidence and mortality in the cohort were compared with expected values based on the age-adjusted and sex-adjusted rates in the general population. Risk of hospitalization for other chronic kidney disease was also evaluated using Cox proportional hazards regression. No association between HCV infection and risk of kidney cancer was observed [standardized incidence ratio with 1-year lag=1.2; 95% confidence interval (CI): 0.8-1.7]. Risk of hospitalization for noncancer kidney disease was significantly elevated in the HCV cohort, with significantly stronger associations observed among women than among men [hazard ratio=5.8 (95% CI: 4.2-7.9) and 3.9 (95% CI: 3.2-4.8) for women and men, respectively]. Results of this study do not support the hypothesis that chronic HCV infection confers an increased risk of kidney cancer. However, we did find an association between HCV infection and chronic kidney disease, particularly among women. Given inconsistent findings in the literature, it is premature to consider HCV infection to be a risk factor for kidney cancer.
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| 3. |
- Huang, Jiaqi, et al.
(författare)
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Risk of pancreatic cancer among individuals with hepatitis C or hepatitis B virus infection : a nationwide study in Sweden
- 2013
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Ingår i: British Journal of Cancer. - London, United Kingdom : Nature Publishing Group. - 0007-0920 .- 1532-1827. ; 109:11, s. 2917-2923
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Tidskriftsartikel (refereegranskat)abstract
- Background: A few studies indicated that hepatitis C and hepatitis B virus (HCV/HBV) might be associated with pancreatic cancer risk. The aim of this nationwide cohort study was to examine this possible association.Methods: Hepatitis C virus- and hepatitis B virus-infected individuals were identified from the national surveillance database from 1990 to 2006, and followed to the end of 2008. The pancreatic cancer risk in the study population was compared with the general population by calculation of Standardized Incidence Ratios (SIRs), and with a matched reference population using a Cox proportional hazards regression model to calculate hazard ratios (HRs).Results: In total 340 819 person-years in the HCV cohort and 102 295 in the HBV cohort were accumulated, with 34 and 5 pancreatic cancers identified, respectively. The SIRHCV was 2.1 (95% confidence interval, CI: 1.4, 2.9) and the SIRHBV was 1.4 (0.5, 3.3). In the Cox model analysis, the HR for HCV infection was 1.9 (95% CI: 1.3, 2.7), diminishing to 1.6 (1.04, 2.4) after adjustment for potential confounders.Conclusion: Our results indicated that HCV infection might be associated with an increased risk of pancreatic cancer but further studies are needed to verify such association. The results in the HBV cohort indicated an excess risk, however, without statistical significance due to lack of power.
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| 4. |
- Törner, Anna, et al.
(författare)
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A method to visualize and adjust for selection bias in prevalent cohort studies
- 2011
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 174:8, s. 969-76
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Tidskriftsartikel (refereegranskat)abstract
- Selection bias and confounding are concerns in cohort studies where the reason for inclusion of subjects in the cohort may be related to the outcome of interest. Selection bias in prevalent cohorts is often corrected by excluding observation time and events during the first time period after inclusion in the cohort. This time period must be chosen carefully-long enough to minimize selection bias but not too long so as to unnecessarily discard observation time and events. A novel method visualizing and estimating selection bias is described and exemplified by using 2 real cohort study examples: a study of hepatitis C virus infection and a study of monoclonal gammopathy of undetermined significance. The method is based on modeling the hazard for the outcome of interest as a function of time since inclusion in the cohort. The events studied were "hospitalizations for kidney-related disease" in the hepatitis C virus cohort and "death" in the monoclonal gammopathy of undetermined significance cohort. Both cohorts show signs of considerable selection bias as evidenced by increased hazard in the time period after inclusion in the cohort. The method was very useful in visualizing selection bias and in determining the initial time period to be excluded from the analyses.
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| 5. |
- Törner, Anna, et al.
(författare)
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A proposed method to adjust for selection bias in cohort studies
- 2010
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 171:5, s. 602-608
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Tidskriftsartikel (refereegranskat)abstract
- Selection bias is a concern in cohort studies in which selection into the cohort is related to the studied outcome. An example is chronic infection with hepatitis C virus, where the initial infection may be asymptomatic for decades. This problem leads to selection of more severely ill individuals into registers of such infections. Cohort studies often adjust for this bias by introducing a time window between entry into the cohort and entry into the study. This paper describes and assesses a novel method to improve adjustment for this type of selection bias. The size of the time window is decided by calculating a standardized incidence ratio as a continuous function of the size of the time window. The resulting graph is used to decide on an appropriate window size. The method is evaluated by using the Swedish register of hepatitis C virus infections for 1990-2006. The complications studied were non-Hodgkin lymphoma and liver cancer. Selection bias differed for the studied outcomes, and a time window of a minimum of 2 months and 12 months, respectively, was judged to be appropriate. The novel method may have advantages compared with an interval-based method, especially in cohort studies with small numbers of events.
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| 6. |
- Törner, Anna
(författare)
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Statistical methods for long-term follow-up of infectious diseases
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The overall aim of this work has been to investigate methodological issues connected to long-term follow-up of infections diseases. The work extended to prevalent cohorts in general. The common denominator for the main methodological efforts in these four papers is issues connected to selection bias. In the first three papers methods for visualizing selection bias in prevalent cohorts were explored and different approaches to adjust for this bias discussed. In the fourth paper, capture-recapture modeling was used to examine ascertainment level for liver cancer in the Swedish Cancer Register. Study 1: In this study we investigated a novel approach to visualize and adjust for selection bias in prevalent cohorts. The method is an extension of the standard interval-based approach, where a risk estimate is calculated for disjointed time periods after inclusion in the cohort of interest. In the proposed method, observation time and events are cumulated, giving more power and more precise estimates which may be useful for studies with few events where it may be difficult to judge what is a true effect and what is random noise. The proposed method, cumulative SIR, is exemplified using data on hepatitis-C virus infection and the outcome liver cancer and non-Hodgkin lymphoma. The results using this novel approach were comparable to a standard approach with disjoint intervals. The results indicate that the method may be useful in situations with few events in the cohort. The method is only useful for cohorts where the risk of the studied outcome is fairly stable over time. Study 2: Spurious observations have indicated that there may be a relationship between hepatitis C virus (HCV) infection and kidney cancer. In this study the relationship between HCV-infection and kidney cancer was investigated by use of disease registers. In addition the known association of HCV-infection and other forms of kidney disease was explored further. Methods for investigating selection bias explored in Paper I were used, in addition new ideas were investigated which were further developed in paper III. The relationship between HCV-infection and kidney cancer was not confirmed in this study, but the association of HCV-infection with other kidney-related diseases was investigated further. Study 3: For cohorts that may have high hazard immediately after inclusion in the cohort, which then first decreases to later increase with follow-up time, the method of cumulative SIR must not be used. The cumulative properties will obscure the initial decrease and the method cannot give clear answers. In paper III we used restricted cubic splines to model to instantaneous failure rate (hazard). The shape of the hazard function may give an indication of the possible presence of selection bias in the cohort. The proposed method was exemplified using 1) data on HCV-infection where the outcome of interest was ‘kidney disease’ and 2) a cohort a patients with Monoclonal Gammapathy of Uncertain Significance (MGUS) and the outcome of interest ‘death’. The model was useful to study the shape of the hazard in the cohorts and the number of knots was adjusted to give a suitably flexible model, clearly showing the shape of the hazard without being too flexible. Study 4: In this study we explored capture-recapture modeling, using a log-linear model to estimate ascertainment level of the Swedish Cancer Register (CR). We used a three-source model: CR, the National Patient Register (PR) and the Cause of Death Register (DR). Due to the limited degrees of freedom in available data, a full model can not be used. We chose to estimate a single two-way interaction between the most dependent registers (DR and PR) and a three-way interaction. This model will estimate the number of unreported cases of liver cancer to about 25% of the total number of cases in all three registers together, accounting for overlap. The analysis is likely to be biased by false positive cases identified in the PR and/or DR.
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