Spatial smoothing models to deal with the complex sampling design and nonresponse in the Florida BRFSS survey.

Public health and governmental organizations have acknowledged the importance of obtaining information of various characteristics for small areas, such as counties. Spatial smoothing models have been developed to gain reliable information on the geographical distribution of the outcome of interest. When the geographical analysis is based on survey data, two issues pose challenges: (1) the complex design of the survey and (2) the presence of missing data due to non-response. We investigate the influence of missing data and the adjustment thereof in the context of the 2013 Florida Behavioral Risk Factor Surveillance System (BRFSS) health survey. We focus on the application and comparison of the Hajek ratio estimator and two model-based approaches for estimation of the spatial trend of the prevalence of having no health insurance coverage. The model-based methods are compared using the Deviance Information Criterion which show the benefits of modeling the weights as flexibly as possible. Methods are extended towards subgroup analyses and the estimation of area-specific standardized rates, where household incomes was identified as an important factor to include in the analysis.

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

Spatial small area smoothing models for handling survey data with nonresponse.

Spatial smoothing models play an important role in the field of small area estimation. In the context of complex survey designs, the use of design weights is indispensable in the estimation process. Recently, efforts have been made in these spatial smoothing models, in order to obtain reliable estimates of the spatial trend. However, the concept of missing data remains a prevalent problem in the context of spatial trend estimation as estimates are potentially subject to bias. In this paper, we focus on spatial health surveys where the available information consists of a binary response and its associated design weight. Furthermore, we investigate the impact of nonresponse as missing data on a range of spatial models for different missingness mechanisms and different degrees of missingness by means of an extensive simulation study. The computations were performed in R, using INLA and other existing packages. The results show that weight adjustment to correct for missingness has a beneficial effect on the bias in the missing at random setting for all models. Furthermore, we estimate the geographical distribution of perceived health at the district level based on the Belgian Health Interview Survey (2001). Copyright © 2017 John Wiley & Sons, Ltd.

Estimating bias in derived body mass index in the Maternity Experiences Survey. / Estimation du biais lié à l'indice de masse corporelle utilisé dans l'Enquête canadienne sur l'expérience de la maternité.

INTRODUCTION: The objective of this study was to assess bias in the body mass index (BMI) measure in the Canadian Maternity Experiences Survey (MES) and possible implications of bias on the relationship between BMI and selected pregnancy outcomes. METHODS: We assessed BMI classification based on self-reported versus measured values. We used a random sample of 6175 women from the MES, which derived BMI from self-reported height and weight, and a random sample of 259 women who had previously given birth from the Canadian Health Measures Survey (CHMS), which derived BMI from self-reported and measured height and weight. Two correction equations were applied to self-reported based BMI, and the impact of these corrections on associations between BMI and caesarean section, small-for-gestational age (SGA) and large-for-gestational age (LGA) births was studied. RESULTS: Overall, 86.9% of the CHMS subsample was classified into the same BMI category based on self-reported versus measured data. However, misclassification had a substantial effect on the proportion of women in underweight and obese BMI categories. For example, 14.5% versus 20.8% of women were classified as obese based on self-reported data versus measured data. Corrections improved estimates of obesity prevalence, but over- and underestimated other BMI categories. Corrections had nonsignificant effects on the associations between BMI and SGA, LGA, and caesarean section. CONCLUSION: While there was high concordance in BMI classification based on selfreported versus measured height and weight, bias in self-reported based measures may slightly over- or underestimate the risks associated with a particular BMI class. However, the general trend in associations is unaffected.

INTRODUCTION: Cette étude visait à évaluer le biais dans la mesure de l'indice de masse corporelle (IMC) dans l'Enquête canadienne sur l'expérience de la maternité (ECEM) ainsi que les répercussions possibles de ce biais sur l'association entre l'IMC et certaines issues de grossesse. MÉTHODOLOGIE: Nous avons évalué la répartition des valeurs de l'IMC en comparant des données autodéclarées et des données mesurées. Nous avons utilisé un échantillon aléatoire de 6 175 participantes à l'ECEM dont l'IMC avait été calculé d'après la taille et le poids autodéclarés et un échantillon aléatoire de 259 participantes à l'Enquête canadienne sur les mesures de santé (ECMS) ayant déjà accouché et dont l'IMC avait été calculé d'après la taille et le poids autodéclarés et mesurés. Nous avons appliqué deux équations de correction à l'IMC fondé sur des valeurs autodéclarées, et nous avons examiné l'impact de ces corrections sur les associations entre l'IMC et un accouchement par césarienne, un faible poids pour l'âge gestationnel (FPAG) et un poids élevé pour l'âge gestationnel (PEAG). RÉSULTATS: Dans l'ensemble, 86,9 % des femmes du sous-échantillon de l'ECMS appartenaient à la même catégorie d'IMC lorsque ses valeurs étaient autodéclarées et lorsqu'elles étaient mesurées. Cependant, les différences de répartition ont eu un effet considérable sur la proportion de femmes des catégories de poids insuffisant et d'obésité. Par exemple, le pourcentage de femmes classées comme obèses était de 14,5 % avec les données autodéclarées contre 20,8 % avec les données mesurées. Les corrections ont permis d'améliorer les estimations de la prévalence de l'obésité, mais ont surestimé ou sous-estimé les autres catégories d'IMC. Les corrections ont eu un effet non significatif sur les associations entre l'IMC et l'accouchement par césarienne, le FPAG et le PEAG. CONCLUSION: Bien que la concordance pour la répartition des valeurs de l'IMC entre la taille et le poids autodéclarés et la taille et le poids mesurés soit élevée, il existe un biais dans les mesures fondées sur les valeurs autodéclarées susceptible d'entraîner de légères surestimations ou sous-estimations des risques associés à une catégorie d'IMC donnée. La tendance générale des associations reste néanmoins inchangée.

Spatio-temporal Bayesian model selection for disease mapping.

Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor.

Model-based inference for small area estimation with sampling weights.

Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts.

Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping.

The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions.

Services and supports for young children with Down syndrome: parent and provider perspectives.

BACKGROUND: As individuals with Down syndrome are living longer and more socially connected lives, early access to supports and services for their parents will ensure an optimal start and improved outcomes. The family's journey begins at the child's diagnosis, and cumulative experiences throughout infancy and childhood set the tone for a lifetime of decisions made by the family regarding services, supports and activities. METHODS: This study utilized focus groups and interviews with seven nurses, five therapists, 25 service co-ordinators, and 10 English- and three Spanish-speaking parents to better understand family experiences and perceptions on accessing Down syndrome-related perinatal, infant and childhood services and supports. RESULTS: Parents and providers reflected on key early life issues for children with Down syndrome and their families in five areas: prenatal diagnosis; perinatal care; medical and developmental services; care co-ordination and services; and social and community support. CONCLUSIONS: Systems of care are not consistently prepared to provide appropriate family-centred services to individuals with Down syndrome and their families. Individuals with disabilities require formal and informal supports from birth to achieve and maintain a high quality of life.

Space-Time Areal Mixture Model: Relabeling Algorithm and Model Selection Issues.

With the growing popularity of spatial mixture models in cluster analysis, model selection criteria have become an established tool in the search for parsimony. However, the label-switching problem is often inherent in Bayesian implementation of mixture models and a variety of relabeling algorithms have been proposed. We use a space-time mixture of Poisson regression models with homogeneous covariate effects to illustrate that the best model selected by using model selection criteria does not always support the model that is chosen by the optimal relabeling algorithm. The results are illustrated for real and simulated datasets. The objective is to make the reader aware that if the purpose of statistical modeling is to identify clusters, applying a relabeling algorithm to the model with the best fit may not generate the optimal relabeling.

Overcoming the challenges of robot-assisted radical prostatectomy.

Robot-assisted radical prostatectomy (RARP) is the most commonly performed robotic procedure worldwide and is firmly established as a standard treatment option for localised prostate cancer. Part of the explanation for the rapid uptake of RARP is the reported gentler learning curve compared with the challenges of laparoscopic radical prostatectomy (LRP). However, robotic surgery is still fraught with potential difficulties and avoiding complications while on the steepest part of the learning curve is critical. Furthermore, as surgeons progress there is a tendency to take on increasingly complex cases, including patients with difficult anatomy and prior surgery, and these cases present a unique challenge. Significant intra-abdominal adhesions may be identified following open surgery, or dense periprostatic inflammation may be encountered following TURP; large prostate gland size and median lobes may alter bladder neck anatomy, making difficult subsequent urethro-vesical anastomosis. Even experienced robotic surgeons will be challenged by salvage RARP. Approaching these problems in a structured manner allows many of the problems to be overcome. We discuss some of the specific techniques to deal with these potential difficulties and highlight ways to avoid making serious mistakes.

Awareness of prostate cancer among patients and the general public: results of an international survey.

The objective of this study was to assess the level of awareness of prostate cancer (PCa) among the general public and PCa patients in Europe and North America. A survey was undertaken across four European countries (UK, Germany, Italy and Spain), and across the United States and Canada in late 2007. In total, 1008 men with PCa and their partners (the 'prostate sample'), and 911 men without PCa and their partners (the 'well sample') participated in the survey, all aged > or =50 years. Interviews were conducted through telephone, pen and paper, and online. Many people surveyed (53%) thought that breast cancer is more common than PCa. Moreover, 1 in 10 people from the well sample (10%) thought that PCa affects both men and women. When the prostate sample was asked about their perceived level of risk of PCa before diagnosis, 50% believed that they/their husband or partner were previously at low or very low risk, before they were diagnosed. Awareness of the major risk factors for PCa (age and family history) was generally good, but respondents were less clear about the role of other potential factors, such as smoking and drinking alcohol. This international survey, thought to be largest of its type, shows that although patient and public awareness of PCa is generally satisfactory, there is still a considerable lack of clarity about PCa risk factors, and a danger for people to underestimate their own/their partner's perceived risk for PCa. Programmes to responsibly educate and inform men and their partners about risk factors, prevalence and screening tools for PCa are required.

A selected review and personal experience with robotic prostatectomy: implications for adoption of this new technology in the United Kingdom.

Robot-assisted laparoscopic prostatectomy (RALP) is a rapidly evolving technique for the treatment of localized prostate cancer. However, cynics point to the increasing role of market forces in the robotic revolution. As yet, Europe has not taken up RALP in large numbers and this may in part relate to the high level of expertise in laparoscopy previously gained. Furthermore, setting up a robotic programme is a major undertaking for many surgical units. This review discusses some of the challenges in the development of a robotic service drawn from personal experience within the United Kingdom. Furthermore, available data on RALP versus open and laparoscopic approaches are reviewed for surgical and cancer-related outcomes. Preliminary data appear to show an advantage over open prostatectomy with reduced blood loss, decreased pain and early mobilisation and shorter hospital stay. Most intra-institutional studies demonstrate better postoperative continence and potency with RALP; however, this needs to be viewed in the context of a paucity of randomized data available in the literature. There is no definitive data to show an advantage over standard laparoscopic surgery, but the fact that this technique has reached parity with laparoscopy within 5 years is encouraging: with continued experience, the hope is that results will continue to improve.

Comparison of glyburide and insulin for the management of gestational diabetics with markedly elevated oral glucose challenge test and fasting hyperglycemia.

OBJECTIVE: To compare the effectiveness of glyburide and insulin for the treatment of Gestational diabetes mellitus (GDM) in women who had OGCT >or=200 mg/dl and fasting hyperglycemia. STUDY DESIGN: A retrospective study was performed among a subset of women treated with glyburide or insulin for GDM from 1999 to 2002 with an OGCT >or=200 mg/dl and pretreatment fasting plasma glucose >or=105 mg/dl. Exclusion criteria included pretreatment fasting >or=140 mg/dl, gestational age >or=34 weeks and multiple gestation. Maternal and neonatal outcomes were assessed. Statistical methods included bivariate and multivariable logistic regression analyses. RESULTS: In 1999 to 2000, 78 women were treated with insulin; in 2001 to 2002, 44 of 69 (64%) received glyburide. There were no statistically significant differences between the two groups with regards to mean OGCT (230+/-25 vs 223+/-23 mg/dl, P=0.07) and mean pretreatment fasting (120+/-10 vs 119+/-11 mg/dl, P=0.45). Seven women (16%) failed glyburide. Women in the insulin group were younger (31.5+/-5.8 vs 35.2+/-4.7 years, P<0.001) and had a higher mean BMI (32.4+/-6.4 vs 29.1+/-5.8 kg/m(2), P=0.003) compared to glyburide group. There were no significant differences in birth weight (3524+/-548 vs 3420+/-786 g, P=0.65), macrosomia (19 vs 23%, P=0.65), pre-eclampsia (12 vs 11%, P=0.98) or cesarean delivery (39 vs 46%, P=0.45). Neonates in the glyburide group were diagnosed more frequently with hypoglycemia (34 vs 14%, P=0.01). When controlled for confounders, macrosomia was found to be associated with glyburide treatment (OR 3.5, 95% CI 1.1 to 11.4). CONCLUSION: In women with GDM who had a markedly elevated OGCT and fasting hyperglycemia, glyburide achieved similar birth weights and delivery outcomes but was associated with an increased risk of macrosomia. The possible increased risk of neonatal hypoglycemia in the glyburide group warrants further investigation.

Hypoandrogen-metabolic syndrome: a potentially common and underdiagnosed condition in men.

Recently, it has been proposed that hypoandrogenaemia (hypogonadism, hypotestosteronaemia) may be a common accompanying factor in men with the metabolic syndrome (insulin resistance, Reaven's syndrome or syndrome X). When they are present together they may be considered as a specific entity, the hypoandrogen-metabolic (HAM) syndrome. The metabolic syndrome is common and its prevalence is predicted to increase in coming years. Hypoandrogenaemia, often unrecognised, is also common and may be an aetiological factor in the development of the metabolic syndrome in men. The prevalence of both hypoandrogenaemia and the metabolic syndrome increases with age and the clinician will frequently attend to men in their middle to advanced years with obesity, low androgen levels and metabolic syndrome. These conditions place men at an increased risk of cardiovascular and coronary heart disease and type 2 diabetes and can be simply investigated with weight, waist and blood pressure measurement and blood sample analyses. Men with HAM and symptoms of androgen deficiency may be managed by, in the absence of contraindications, testosterone replacement therapy along with weight reduction and other measures to normalise glucose, lipid and blood pressure control.

Allelic imbalance and biochemical outcome after radical prostatectomy.

OBJECTIVE: To compare the incidence of allelic imbalance (AI) in men with rapid disease progression with those who remained disease free after radical prostatectomy, with the aim of identifying genetic markers to predict prognosis and guide further treatment. PATIENTS AND METHODS: Tumour and normal DNA were extracted from two matched groups of 31 men with extracapsular node-negative (pT3N0) prostate cancer who had undergone radical prostatectomy. One group comprised men who developed biochemical recurrence within 2 years of surgery and one group were prostate-specific antigen (PSA) free for at least 3 years. Men were matched for Gleason grade, preoperative PSA and pathological stage. Analysis was performed by genotyping. RESULTS: Allelic imbalance was analysed using 30 markers, and was seen in at least one marker in 57 (92%) of the cases. Deletion at marker D10S211 (10p12.1) was significantly more common in the relapse group than the non-relapse group (35 vs 5%, P=0.03). CONCLUSIONS: This study demonstrates significant association between AI on chromosome 10 and biochemical progression after radical prostatectomy.

Testosterone replacement therapy for late onset hypogonadism: what is the risk of inducing prostate cancer?

Prescription sales of testosterone have risen considerably over the last decade and are likely to continue to grow as further preparations become available. Testosterone promotes existing prostate cancer; however, concern does exist as to whether or not testosterone therapy induces prostate cancer. The aim of this article is to review the evidence for such a link.