Dataset Information

A Systematic Review and Meta-analysis of Associations between Clinical Prostatitis and Prostate Cancer: New Estimates Accounting for Detection Bias.

ABSTRACT: BACKGROUND:Previous meta-analyses have estimated summary positive associations between clinical prostatitis and prostate cancer. However, none have accounted for detection bias, the possibility for increased prostate cancer screening and detection in men with clinical prostatitis, in their pooled estimates. METHODS:We searched for studies that investigated the relation between clinical prostatitis and prostate cancer through November 2018. Random effects meta-analysis was used to calculate summary odds ratios (OR) among all studies and in strata defined by methods used to reduce detection bias.Results: Although an increased odds of prostate cancer was seen among men with a history of clinical prostatitis in all 38 eligible studies combined [OR, 2.05; 95% confidence interval (CI), 1.64-2.57], this estimate attenuated to null among studies that performed the most rigorous analyses to limit detection bias (OR, 1.16; 95% CI, 0.77-1.74). CONCLUSIONS:Our findings indicate that previously reported positive associations between clinical prostatitis and prostate cancer are likely due to detection bias. IMPACT:Studies using rigorous detection bias methods are warranted to replicate these findings, as well as to examine the possible relation between prostate inflammation and prostate cancer directly, rather than indirectly through the diagnosis of "prostatitis," which includes a large proportion of men without evidence of prostate inflammation.

SUBMITTER: Langston ME

PROVIDER: S-EPMC6774844 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

A Systematic Review and Meta-analysis of Associations between Clinical Prostatitis and Prostate Cancer: New Estimates Accounting for Detection Bias.

Langston Marvin E ME Horn Mara M Khan Saira S Pakpahan Ratna R Doering Michelle M Dennis Leslie K LK Sutcliffe Siobhan S

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20190723 10

<h4>Background</h4>Previous meta-analyses have estimated summary positive associations between clinical prostatitis and prostate cancer. However, none have accounted for detection bias, the possibility for increased prostate cancer screening and detection in men with clinical prostatitis, in their pooled estimates.<h4>Methods</h4>We searched for studies that investigated the relation between clinical prostatitis and prostate cancer through November 2018. Random effects meta-analysis was used to ...[more]

PMID: 31337640

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Project description:OBJECTIVE: Use systematic review methods to quantify the association between prostatitis and prostate cancer, under both fixed and random effects model. EVIDENCE ACQUISITION: Case control studies of prostate cancer with information on prostatitis history. All studies published between 1990-2012, were collected to calculate a pooled odds ratio. SELECTION CRITERIA: the selection criteria are as follows: human case control studies; published from May 1990 to July 2012; containing number of prostatitis, and prostate cancer cases. EVIDENCE SYNTHESIS: In total, 20 case control studies were included. A significant association between prostatitis and prostate cancer was found, under both fixed effect model (pooled OR=1.50, 95%CI: 1.39-1.62), and random effects model (OR=1.64, 95%CI: 1.36-1.98). Personal interview based case control studies showed a high level of association (fixed effect model: pooled OR=1.59, 95%CI: 1.47-1.73, random effects model: pooled OR= 1.87, 95%CI: 1.52-2.29), compared with clinical based studies (fixed effect model: pooled OR=1.05, 95%CI: 0.86-1.28, random effects model: pooled OR= 0.98, 95%CI: 0.67-1.45). Additionally, pooled ORs, were calculated for each decade. In a fixed effect model: 1990's: OR=1.58, 95% CI: 1.35-1.84; 2000's: OR=1.59, 95% CI: 1.40-1.79; 2010's: OR=1.37, 95% CI: 1.22-1.56. In a random effects model: 1990's: OR=1.98, 95% CI: 1.08-3.62; 2000's: OR=1.64, 95% CI: 1.23-2.19; 2010's: OR=1.34, 95% CI: 1.03-1.73. Finally a meta-analysis stratified by each country was conducted. In fixed effect models, U.S: pooled OR =1.45, 95%CI: 1.34-1.57; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90. In random effects model, U.S: pooled OR=1.50, 95%CI: 1.25-1.80; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90. CONCLUSIONS: the present meta-analysis provides the statistical evidence that the association between prostatitis and prostate cancer is significant.

Project description:Background Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01817-0.

Project description:BackgroundExisting WHO estimates of the prevalence of mental disorders in emergency settings are more than a decade old and do not reflect modern methods to gather existing data and derive estimates. We sought to update WHO estimates for the prevalence of mental disorders in conflict-affected settings and calculate the burden per 1000 population.MethodsIn this systematic review and meta-analysis, we updated a previous systematic review by searching MEDLINE (PubMed), PsycINFO, and Embase for studies published between Jan 1, 2000, and Aug 9, 2017, on the prevalence of depression, anxiety disorder, post-traumatic stress disorder, bipolar disorder, and schizophrenia. We also searched the grey literature, such as government reports, conference proceedings, and dissertations, to source additional data, and we searched datasets from existing literature reviews of the global prevalence of depression and anxiety and reference lists from the studies that were identified. We applied the Guidelines for Accurate and Transparent Health Estimates Reporting and used Bayesian meta-regression techniques that adjust for predictors of mental disorders to calculate new point prevalence estimates with 95% uncertainty intervals (UIs) in settings that had experienced conflict less than 10 years previously.FindingsWe estimated that the prevalence of mental disorders (depression, anxiety, post-traumatic stress disorder, bipolar disorder, and schizophrenia) was 22·1% (95% UI 18·8-25·7) at any point in time in the conflict-affected populations assessed. The mean comorbidity-adjusted, age-standardised point prevalence was 13·0% (95% UI 10·3-16·2) for mild forms of depression, anxiety, and post-traumatic stress disorder and 4·0% (95% UI 2·9-5·5) for moderate forms. The mean comorbidity-adjusted, age-standardised point prevalence for severe disorders (schizophrenia, bipolar disorder, severe depression, severe anxiety, and severe post-traumatic stress disorder) was 5·1% (95% UI 4·0-6·5). As only two studies provided epidemiological data for psychosis in conflict-affected populations, existing Global Burden of Disease Study estimates for schizophrenia and bipolar disorder were applied in these estimates for conflict-affected populations.InterpretationThe burden of mental disorders is high in conflict-affected populations. Given the large numbers of people in need and the humanitarian imperative to reduce suffering, there is an urgent need to implement scalable mental health interventions to address this burden.FundingWHO; Queensland Department of Health, Australia; and Bill & Melinda Gates Foundation.

Dataset Information

A Systematic Review and Meta-analysis of Associations between Clinical Prostatitis and Prostate Cancer: New Estimates Accounting for Detection Bias.

Publications

A Systematic Review and Meta-analysis of Associations between Clinical Prostatitis and Prostate Cancer: New Estimates Accounting for Detection Bias.

Similar Datasets

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