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In a stratified analysis, the results from different strata if homogeneity assumption is met are pooled together to obtain a single summary estimate for the common effect parameter. However, the effect can appear homogeneous across strata using one measure but heterogeneous using another. Consequently, two researchers analyzing the same data can arrive at conflicting conclusions if they use different effect measures. In this paper, the author draws on the sufficient component cause model to develop a stratified-analysis method regarding a particular effect measure, the ‘peril ratio’. When there is no mechanistic interaction between the exposure under study and the stratifying variable (i.e., when they do not work together to complete any sufficient cause), the peril ratio is constant across strata. The author presents formulas for the estimation of such a common peril ratio. Three real data are re-analyzed for illustration. When the data is consistent with peril-ratio homogeneity in a stratified analysis, researchers can use the formulas in this paper to pool the strata. [ABSTRACT FROM AUTHOR]

As a performance measure for a prediction model, the area under the receiver operating characteristic curve (AUC) is insensitive to the addition of strong markers. A number of measures sensitive to performance change have recently been proposed; however, these relative-performance measures may lead to self-contradictory conclusions. This paper examines alternative performance measures for prediction models: the Lorenz curve-based Gini and Pietra indices, and a standardized version of the Brier score, the scaled Brier. Computer simulations are performed in order to study the sensitivity of these measures to performance change when a new marker is added to a baseline model. When the discrimination power of the added marker is concentrated in the gray zone of the baseline model, the AUC and the Gini show minimal performance improvements. The Pietra and the scaled Brier show more significant improvements in the same situation, comparatively. The Pietra and the scaled Brier indices are therefore recommended for prediction model performance measurement, in light of their ease of interpretation, clinical relevance and sensitivity to gray-zone resolving markers. [ABSTRACT FROM AUTHOR]

Background: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes–syndromic surveillance–using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results: High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro = .955), however the classification process is not transparent to the domain experts. Conclusion: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input. [ABSTRACT FROM AUTHOR]

Background: Graphical representation of data is one of the most easily comprehended forms of explanation. The current study describes a simple visualization tool which may allow greater understanding of medical and epidemiological data. Method: We propose a simple tool for visualization of data, known as a “quilt plot”, that provides an alternative to presenting large volumes of data as frequency tables. Data from the Australian Needle and Syringe Program survey are used to illustrate “quilt plots”. Conclusion: Visualization of large volumes of data using “quilt plots” enhances interpretation of medical and epidemiological data. Such intuitive presentations are particularly useful for the rapid assessment of problems in the data which cannot be readily identified by manual review. We recommend that, where possible, “quilt plots” be used along with traditional quantitative assessments of the data as an explanatory data analysis tool. [ABSTRACT FROM AUTHOR]

Background: Cadmium (Cd) is a heavy metal that can cause renal tubular dysfunction in humans. Women are among the high-risk group for Cd health effects. Determining the thresholds of Cd-induced renal effects is important. Thus, in this article, we aimed to identify the benchmark dose (BMD) and its low limit (BMDL) levels as the Cd thresholds for Chinese women. Methods: Epidemiologic investigation was performed in county A and county B to obtain data on Cd exposure and its renal effect on respondents. Levels of Cd (UCd), β2-microglobulin (UB2M), and N-acetyl-β-D-glucosaminidase (UNAG) were measured in morning urine samples. The BMD approach was mainly performed. Results: Results of the BMD approach were similar whether the method was conducted for the two sets of data (collected in CA and CB, respectively) separately or cooperatively. The BMD/BMDL values of UCd for all subjects were 1.07/0.44 and 2.12/0.53 µg/g cr based on UB2M and UNAG, respectively, given a predetermined BMR of 0.05. Conclusions: The presented thresholds of Cd-induced renal effects (i.e., the BMDLs of UCd) are close to the counterpart values reported in Japan, Sweden and Belguim. [ABSTRACT FROM AUTHOR]

Background: In this study, we aim to identify key climatic factors that are associated with the transmission of Japanese encephalitis virus in areas located near the Three Gorges Dam, between 1997 and 2008. Methods: We identified three geographical regions of Chongqing, based on their distance from the Three Gorges Dam. Collectively, the three regions consisted of 12 districts from which study information was collected. Zero-Inflated Poisson Regression models were run to identify key climatic factors of the transmission of Japanese encephalitis virus for both the whole study area and for each individual region; linear regression models were conducted to examine the fluctuation of climatic variables over time during the construction of the Three Gorges Dam. Results: Between 1997 and 2008, the incidence of Japanese encephalitis decreased throughout the entire city of Chongqing, with noticeable variations taking place in 2000, 2001 and 2006. The eastern region, which is closest to the Three Gorges Dam, suffered the highest incidence of Japanese encephalitis, while the western region experienced the lowest incidence. Linear regression models revealed that there were seasonal fluctuations of climatic variables during this period. Zero-Inflated Poisson Regression models indicated a significant positive association between temperature (with a lag of 1 and 3 months) and Japanese encephalitis incidence, and a significant negative association between rainfall (with a lag of 0 and 4 months) and Japanese encephalitis incidence. Conclusion: The spatial and temporal trends of Japanese encephalitis incidence that occurred in the City of Chongqing were associated with temperature and rainfall. Seasonal fluctuations of climatic variables during this period were also observed. Additional studies that focus on long-term data collection are needed to validate the findings of this study and to further explore the effects of the Three Gorges Dam on Japanese encephalitis and other related diseases. [ABSTRACT FROM AUTHOR]

Background: Health inequities in developing countries are difficult to eradicate because of limited resources. The neglect of adult mortality in Sub-Saharan Africa (SSA) is a particular concern. Advances in data availability, software and analytic methods have created opportunities to address this challenge and tailor interventions to small areas. This study demonstrates how a generic framework can be applied to guide policy interventions to reduce adult mortality in high risk areas. The framework, therefore, incorporates the spatial clustering of adult mortality, estimates the impact of a range of determinants and quantifies the impact of their removal to ensure optimal returns on scarce resources. Methods: Data from a national cross-sectional survey in 2007 were used to illustrate the use of the generic framework for SSA and elsewhere. Adult mortality proportions were analyzed at four administrative levels and spatial analyses were used to identify areas with significant excess mortality. An ecological approach was then used to assess the relationship between mortality “hotspots” and various determinants. Population attributable fractions were calculated to quantify the reduction in mortality as a result of targeted removal of high-impact determinants. Results: Overall adult mortality rate was 145 per 10,000. Spatial disaggregation identified a highly non-random pattern and 67 significant high risk local municipalities were identified. The most prominent determinants of adult mortality included HIV antenatal sero-prevalence, low SES and lack of formal marital union status. The removal of the most attributable factors, based on local area prevalence, suggest that overall adult mortality could be potentially reduced by ∼90 deaths per 10,000. Conclusions: The innovative use of secondary data and advanced epidemiological techniques can be combined in a generic framework to identify and map mortality to the lowest administration level. The identification of high risk mortality determinants allows health authorities to tailor interventions at local level. This approach can be replicated elsewhere. [ABSTRACT FROM AUTHOR]

Background: Acute alcohol-related dysfunctional behaviours, such as hangover, are predictive of poor health and mortality. Although much is known about the association of education with alcohol consumption, little is known about its association with these dysfunctional behaviours. Methods: The study population was 1,705 male drinkers aged 25–54 years resident in the city of Izhevsk, Russia who participated in a cross-sectional survey (2003–6). Structural equation modelling was used to examine the relationships between education, beverage and non-beverage alcohol intake, drinking patterns, and acute alcohol-related dysfunction score among these drinkers. Results: Dysfunction was related to all other drinking variables, with the strongest predictors being spirit intake, non-beverage alcohol consumption and drinking patterns. There was a strong relationship between education and acute dysfunction which was not explained by adjusting for alcohol intake and drinking patterns (mean adjusted dysfunction score 0.35 SD (95% CI 0.10, 0.61) lower in men with higher versus secondary education). Conclusions: Although by definition one or more aspects of alcohol consumption should explain the educational differences in alcohol-related dysfunction, detailed information on drinking only partly accounted for the observed patterns. Thus beyond their intrinsic interest, these results illustrate the challenges in constructing statistical models that convincingly identify the pathways that link educational differences to health-related outcomes. [ABSTRACT FROM AUTHOR]

Background: Most meta-analyses include data from one or more small studies that, individually, do not have power to detect an intervention effect. The relative influence of adequately powered and underpowered studies in published meta-analyses has not previously been explored. We examine the distribution of power available in studies within meta-analyses published in Cochrane reviews, and investigate the impact of underpowered studies on meta-analysis results. Methods and Findings: For 14,886 meta-analyses of binary outcomes from 1,991 Cochrane reviews, we calculated power per study within each meta-analysis. We defined adequate power as ≥50% power to detect a 30% relative risk reduction. In a subset of 1,107 meta-analyses including 5 or more studies with at least two adequately powered and at least one underpowered, results were compared with and without underpowered studies. In 10,492 (70%) of 14,886 meta-analyses, all included studies were underpowered; only 2,588 (17%) included at least two adequately powered studies. 34% of the meta-analyses themselves were adequately powered. The median of summary relative risks was 0.75 across all meta-analyses (inter-quartile range 0.55 to 0.89). In the subset examined, odds ratios in underpowered studies were 15% lower (95% CI 11% to 18%, P<0.0001) than in adequately powered studies, in meta-analyses of controlled pharmacological trials; and 12% lower (95% CI 7% to 17%, P<0.0001) in meta-analyses of controlled non-pharmacological trials. The standard error of the intervention effect increased by a median of 11% (inter-quartile range −1% to 35%) when underpowered studies were omitted; and between-study heterogeneity tended to decrease. Conclusions: When at least two adequately powered studies are available in meta-analyses reported by Cochrane reviews, underpowered studies often contribute little information, and could be left out if a rapid review of the evidence is required. However, underpowered studies made up the entirety of the evidence in most Cochrane reviews. [ABSTRACT FROM AUTHOR]

Background: Hepatitis C virus (HCV) emerged as sexually transmitted infection among HIV-infected men who have sex with men (MSM). We studied whether HCV circulated in identifiable high-risk MSM subcultures and performed phylogenetic analysis. Methods: HIV-infected MSM were recruited at the sexually transmitted infections (STI) outpatient clinic and a university HIV clinic in Amsterdam, the Netherlands, 2008–2009. Participants completed a detailed questionnaire and were tested for HCV antibodies and RNA, with NS5B regions sequenced for analysis of clusters. Results: Among 786 participants, the median age was 43 (IQR 37–48) years, and 93 (11.8%) were HCV-positive. Seropositivity was associated with belonging to subcultures identified as leather (aOR 2.60; 95% CI 1.56–4.33), rubber/lycra (aOR 2.15; 95% CI 1.10–4.21), or jeans (aOR 2.23; 95% CI 1.41–3.54). The two largest HCV-RNA monophyletic clusters were compared; MSM in cluster I (genotype 1a, n = 13) reported more partners (P = 0.037) than MSM in cluster II (genotype 4d, n = 14), but demographics, subculture characteristics and other risk behaviors did not differ significantly between the two clusters. Discussion: HCV infection is associated with identifiable groups of leather/rubber/lycra/jeans subcultures among HIV-infected MSM. Separate epidemiological HCV transmission networks were not revealed. Active HCV screening and treatment within specific subcultures may reduce HCV spread among all MSM. [ABSTRACT FROM AUTHOR]

Objective: We examined whether a panel of SNPs, systematically selected from genome-wide association studies (GWAS), could improve risk prediction of coronary heart disease (CHD), over-and-above conventional risk factors. These SNPs have already demonstrated reproducible associations with CHD; here we examined their use in long-term risk prediction. Study Design and Setting: SNPs identified from meta-analyses of GWAS of CHD were tested in 840 men and women aged 55–75 from the Edinburgh Artery Study, a prospective, population-based study with 15 years of follow-up. Cox proportional hazards models were used to evaluate the addition of SNPs to conventional risk factors in prediction of CHD risk. CHD was classified as myocardial infarction (MI), coronary intervention (angioplasty, or coronary artery bypass surgery), angina and/or unspecified ischaemic heart disease as a cause of death; additional analyses were limited to MI or coronary intervention. Model performance was assessed by changes in discrimination and net reclassification improvement (NRI). Results: There were significant improvements with addition of 27 SNPs to conventional risk factors for prediction of CHD (NRI of 54%, P<0.001; C-index 0.671 to 0.740, P = 0.001), as well as MI or coronary intervention, (NRI of 44%, P<0.001; C-index 0.717 to 0.750, P = 0.256). ROC curves showed that addition of SNPs better improved discrimination when the sensitivity of conventional risk factors was low for prediction of MI or coronary intervention. Conclusion: There was significant improvement in risk prediction of CHD over 15 years when SNPs identified from GWAS were added to conventional risk factors. This effect may be particularly useful for identifying individuals with a low prognostic index who are in fact at increased risk of disease than indicated by conventional risk factors alone. [ABSTRACT FROM AUTHOR]

We compared several techniques for assigning Hispanic ethnicity to records in data systems where this information may be missing, variously making use of country of origin, surname, race, and county of residence. We considered an algorithm in use by the North American Association of Central Cancer Registries (NAACCR), a variation of this developed by the authors, a “fast and frugal” algorithm developed with the aid of recursive partitioning methods, and conventional logistic regression. With the exception of logistic regression, each approach was rule-based: if specific criteria were met, an ethnicity assignment was made; otherwise, the next criterion was considered, until all records were assigned. We evaluated the algorithms on a sample of over 500,000 female clients from the New York State Cancer Services Program for whom self-reported Hispanic ethnicity was known. We found that all approaches yielded similarly high accuracy, sensitivity, and positive predictive value in all parts of the state, from areas with very low to very high Hispanic populations. An advantage of the fast and frugal method is that it consists of a small number of easily remembered steps. [ABSTRACT FROM AUTHOR]

Background: Two previous reviews found that access-enhancing interventions were effective in increasing mammography uptake amongst low-income women. The purpose of this study was to estimate the magnitude of the effect of interventions used to increase uptake of mammography amongst low-income women. Methods: Searches were conducted in MEDLINE and EMBASE (2002–April 2012) using relevant MeSH terms and keywords. Randomised controlled trials which aimed to increase mammography use in an asymptomatic low-income population and which had as an outcome receipt of a mammogram, were eligible for inclusion. The primary outcome was the post-intervention difference in the proportion of women who had a mammogram in the intervention and control groups. The quality of the studies was assessed using the Cochrane risk of bias tool. We calculated summary estimates using random effects meta-analyses. Possible reasons for heterogeneity were investigated using sub-group analyses and meta-regression. Publication bias was assessed using Egger's test. Results: Twenty-one studies met the inclusion criteria, including 33 comparisons. Interventions increased the uptake of mammography in low income women by an additional 8.9% (95% CI 7.3 to 10.4%) compared to the control group. There was some evidence that interventions with multiple strategies were more effective than those with single strategies (p  = 0.03). There was some suggestion of publication bias. The quality of the included studies was often unclear. Omitting those with high risk of bias has little effect on the results. Conclusions: Interventions can increase mammography uptake among low-income women, multiple interventions being the most effective strategy. Given the robustness of the results to sensitivity analyses, the results are likely to be reliable. The generalisability of the results beyond the US is unclear. [ABSTRACT FROM AUTHOR]