Determinants of leptospirosis in Sri Lanka: Study Protocol

<p>Abstract</p> <p>Background</p> <p>Leptospirosis is becoming a major public health threat in Sri Lanka as well as in other countries. We designed a case control study to determine the factors associated with local transmission of leptospirosis in Sri Lanka, in order to identify major modifiable determinants of leptospirosis. The purpose of this paper is to describe the study protocol in detail prior to the publishing of the study results, so that the readership will be able to understand and interpret the study results effectively.</p> <p>Methods</p> <p>A hospital based partially matched case control design is proposed. The study will be conducted in three selected leptospirosis endemic districts in central Sri Lanka. Case selection will include screening all acute fever patients admitted to selected wards to select probable cases of leptospirosis and case confirmation using an array of standard laboratory criteria. Age and sex matched group of acute fever patients with other confirmed diagnosis will be used as controls. Case to control ratio will be 1:2. A minimum sample of 144 cases is required to detect 20% exposure with 95% two sided confidence level and 80% power. A pre tested interviewer administered structured questionnaire will be used to collect data from participants. Variables included in the proposed study will be evaluated using conceptual hierarch of variables in three levels; Exposure variables as proximal; reservoir and environmental variables as intermediate; socio-demographic variables as distal. This conceptual hierarch hypothesised that the distal and intermediate variables are mediated through the proximal variables but not directly. A logistic regression model will be used to analyse the probable determinants of leptospirosis. This model will evaluate the effect of same level and upper level variables on the outcome leptospirosis, using three blocks.</p> <p>Discussion</p> <p>The present national control programme of leptospirosis is hampered by lack of baseline data on leptospirosis disease transmission. The present study will be able to provide these essential information for formulation of better control strategies.</p

Risk of renal cell carcinoma in relation to blood telomere length in a population-based case–control study

Background: There are few known risk factors for renal cell carcinoma (RCC). Two small hospital-based case–control studies suggested an association between short blood telomere length (TL) and increased RCC risk. Methods: We conducted a large population-based case–control study in two metropolitan regions of the United States comparing relative TL in DNA derived from peripheral blood samples from 891 RCC cases and 894 controls. Odds ratios and 95% confidence intervals were estimated using unconditional logistic regression in both unadjusted and adjusted models. Results: Median TL was 0.85 for both cases and controls (P=0.40), and no differences in RCC risk by quartiles of TL were observed. Results of analyses stratified by age, sex, race, tumour stage, and time from RCC diagnosis to blood collection were similarly null. In multivariate analyses among controls, increasing age and history of hypertension were associated with shorter TL (P<0.001 and P=0.07, respectively), and African Americans had longer TL than Caucasians (P<0.001). Conclusion: These data do not support the hypothesis that blood TL is associated with RCC. This population-based case–control study is, to our knowledge, the largest investigation to date of TL and RCC

A simple method for estimating relative risk using logistic regression

<p>Abstract</p> <p>Background</p> <p>Odds ratios (OR) significantly overestimate associations between risk factors and common outcomes. The estimation of relative risks (RR) or prevalence ratios (PR) has represented a statistical challenge in multivariate analysis and, furthermore, some researchers do not have access to the available methods. Objective: To propose and evaluate a new method for estimating RR and PR by logistic regression.</p> <p>Methods</p> <p>A provisional database was designed in which events were duplicated but identified as non-events. After, a logistic regression was performed and effect measures were calculated, which were considered RR estimations. This method was compared with binomial regression, Cox regression with robust variance and ordinary logistic regression in analyses with three outcomes of different frequencies.</p> <p>Results</p> <p>ORs estimated by ordinary logistic regression progressively overestimated RRs as the outcome frequency increased. RRs estimated by Cox regression and the method proposed in this article were similar to those estimated by binomial regression for every outcome. However, confidence intervals were wider with the proposed method.</p> <p>Conclusion</p> <p>This simple tool could be useful for calculating the effect of risk factors and the impact of health interventions in developing countries when other statistical strategies are not available.</p

Reporting and evaluating genetic association studies

Genetic association studies have become an important part of our scientific landscape. This commentary discusses some basic scientific issues which should be considered when reporting and evaluating such studies including SNP Discovery, Genotyping and Haplotype Analysis; Population Size, Matching of Cases and Controls, and Population Stratification; Phenotype Definition and Multiple Related Phenotypes; Multiple Testing; Replication; Genome-wide Association Studies (GWAS); and the Role of Functional Studies. All of these elements are important in evaluating such studies and should be carefully considered when these studies are conceived and carried out

Experience with multiple control groups in a large population-based case–control study on genetic and environmental risk factors

We discuss the analytic and practical considerations in a large case–control study that had two control groups; the first control group consisting of partners of patients and the second obtained by random digit dialling (RDD). As an example of the evaluation of a general lifestyle factor, we present body mass index (BMI). Both control groups had lower BMIs than the patients. The distribution in the partner controls was closer to that of the patients, likely due to similar lifestyles. A statistical approach was used to pool the results of both analyses, wherein partners were analyzed with a matched analysis, while RDDs were analyzed without matching. Even with a matched analysis, the odds ratio with partner controls remained closer to unity than with RDD controls, which is probably due to unmeasured confounders in the comparison with the random controls as well as intermediary factors. However, when studying injuries as a risk factor, the odds ratio remained higher with partner control subjects than with RRD control subjects, even after taking the matching into account. Finally we used factor V Leiden as an example of a genetic risk factor. The frequencies of factor V Leiden were identical in both control groups, indicating that for the analyses of this genetic risk factor the two control groups could be combined in a single unmatched analysis. In conclusion, the effect measures with the two control groups were in the same direction, and of the same order of magnitude. Moreover, it was not always the same control group that produced the higher or lower estimates, and a matched analysis did not remedy the differences. Our experience with the intricacies of dealing with two control groups may be useful to others when thinking about an optimal research design or the best statistical approach

Single Nucleotide Polymorphisms in the PRDX3 and RPS19 and Risk of HPV Persistence and Cervical Precancer/Cancer

Host genetic factors might affect the risk of progression from infection with carcinogenic human papillomavirus (HPV), the etiologic agent for cervical cancer, to persistent HPV infection, and hence to cervical precancer and cancer.We assessed 18,310 tag single nucleotide polymorphisms (SNPs) from 1113 genes in 416 cervical intraepithelial neoplasia 3 (CIN3)/cancer cases, 356 women with persistent carcinogenic HPV infection (median persistence of 25 months) and 425 randomly selected women (non-cases and non-HPV persistent) from the 10,049 women from the Guanacaste, Costa Rica HPV natural history cohort. For gene and SNP associations, we computed age-adjusted odds ratio and p-trend. Three comparisons were made: 1) association with CIN3/cancer (compared CIN3/cancer cases to random controls), 2) association with persistence (compared HPV persistence to random controls), and 3) progression (compared CIN3/cancers with HPV-persistent group). Regions statistically significantly associated with CIN3/cancer included genes for peroxiredoxin 3 PRDX3, and ribosomal protein S19 RPS19. The single most significant SNPs from each gene associated with CIN3/cancer were PRDX3 rs7082598 (P(trend)<0.0001), and RPS19 rs2305809 (P(trend)=0.0007), respectively. Both SNPs were also associated with progression.These data suggest involvement of two genes, RSP19 and PRDX3, or other SNPs in linkage disequilibrium, with cervical cancer risk. Further investigation showed that they may be involved in both the persistence and progression transition stages. Our results require replication but, if true, suggest a role for ribosomal dysfunction, mitochondrial processes, and/or oxidative stress, or other unknown function of these genes in cervical carcinogenesis

Reporting of Human Genome Epidemiology (HuGE) association studies: An empirical assessment

<p>Abstract</p> <p>Background</p> <p>Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.</p> <p>Methods</p> <p>Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003.</p> <p>Results</p> <p>During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.</p> <p>Conclusion</p> <p>We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.</p