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Spatial analysis of hypospadias cases in northern France: taking clinical data into account.

ABSTRACT: BACKGROUND:Strong evidence for a causal role of environmental factors in a congenital anomaly is still difficult to produce. The collection of statistical data is crucial for gaining a better understanding of the epidemiology and pathophysiology of these anomalies. We aimed to evaluate spatial variations in hypospadias within our region and it's association to socioeconomic and ecological factors, taking clinical data into account. METHODS:All boys with hypospadias born in northern France and seen in Lille University Medical Center (Lille, France) between 1999 and 2012 were included in the analysis. We retrospectively collected geographic data, clinical data (especially known confounding factors associated with an elevated risk of hypospadias), and demographic, socio-economic and ecological data. We analyzed the entire study population and subsequently the subset of boys lacking confounding factors. RESULTS:The study sample of 975 cases of hypospadias over the 13-year period resulted in an incidence of 25.4/10,000 male births, and was characterized by significant spatial heterogeneity (p?

SUBMITTER: Lauriot Dit Prevost A

PROVIDER: S-EPMC7504625 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Spatial analysis of hypospadias cases in northern France: taking clinical data into account.

Lauriot Dit Prevost Arthur A Genin Michael M Occelli Florent F Priso René-Hilaire RH Besson Remi R Lanier Caroline C Sharma Dyuti D

BMC pediatrics 20200921 1

<h4>Background</h4>Strong evidence for a causal role of environmental factors in a congenital anomaly is still difficult to produce. The collection of statistical data is crucial for gaining a better understanding of the epidemiology and pathophysiology of these anomalies. We aimed to evaluate spatial variations in hypospadias within our region and it's association to socioeconomic and ecological factors, taking clinical data into account.<h4>Methods</h4>All boys with hypospadias born in norther ...[more]

PMID: 32957953

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Project description:BackgroundOmics data can be very informative in survival analysis and may improve the prognostic ability of classical models based on clinical risk factors for various diseases, for example breast cancer. Recent research has focused on integrating omics and clinical data, yet has often ignored the need for appropriate model building for clinical variables. Medical literature on classical prognostic scores, as well as biostatistical literature on appropriate model selection strategies for low dimensional (clinical) data, are often ignored in the context of omics research. The goal of this paper is to fill this methodological gap by investigating the added predictive value of gene expression data for models using varying amounts of clinical information.MethodsWe analyze two data sets from the field of survival prognosis of breast cancer patients. First, we construct several proportional hazards prediction models using varying amounts of clinical information based on established medical knowledge. These models are then used as a starting point (i.e. included as a clinical offset) for identifying informative gene expression variables using resampling procedures and penalized regression approaches (model based boosting and the LASSO). In order to assess the added predictive value of the gene signatures, measures of prediction accuracy and separation are examined on a validation data set for the clinical models and the models that combine the two sources of information.ResultsFor one data set, we do not find any substantial added predictive value of the omics data when compared to clinical models. On the second data set, we identify a noticeable added predictive value, however only for scenarios where little or no clinical information is included in the modeling process. We find that including more clinical information can lead to a smaller number of selected omics predictors.ConclusionsNew research using omics data should include all available established medical knowledge in order to allow an adequate evaluation of the added predictive value of omics data. Including all relevant clinical information in the analysis might also lead to more parsimonious models. The developed procedure to assess the predictive value of the omics data can be readily applied to other scenarios.

Project description:BackgroundDuring the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that many patients were not treated promptly or effectively. However, many unexplained deaths were subsequently identified as cases, indicating a few undetected cases, resulting in a higher estimate of FR. Whether the true FR is exceedingly high and what factors determine the detection of cases remain unknown. Estimating the true number of total infected cases (i.e. including undetected cases) can allow an accurate estimation of FR and effective reproduction number ([Formula: see text]).MethodsWe aimed at quantifying the time-varying FR and [Formula: see text] using the estimated true numbers of cases; and, exploring the relationship between the true case number and test and trace data. After adjusting for reporting delays, we developed a model to estimate the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and [Formula: see text] were calculated using the true number of cases. Afterwards, a logistic regression model was used to assess the impact of daily testing and tracing data on the detection ratio of deaths.ResultsThe estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterwards. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. [Formula: see text] reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was found to be associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact traced before symptom onset.ConclusionsIncreasing testing capacity and contact tracing coverage without delays not only improve parameter estimation by reducing hidden cases but may also reduce fatality rates.

Dataset Information

Spatial analysis of hypospadias cases in northern France: taking clinical data into account.

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Spatial analysis of hypospadias cases in northern France: taking clinical data into account.

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