Reexamining predictors of trial outcomes in New York State's sex offender civil management process.

OBJECTIVE: In 2007, New York enacted the Sex Offender Management and Treatment Act, empowering the state to civilly manage individuals who have committed sexual offenses (respondents) and are deemed to have a mental abnormality (MA) that predisposes them to sexually recidivate after serving their criminal sentences. We sought to replicate and extend a previous study (Lu et al., 2015) to identify factors predicting legal decisions. HYPOTHESES: We predicted, on the basis of previous research, that clinical information (e.g., diagnosis) as well as empirically supported risk factors (e.g., sexual deviance) would predict trial outcomes. METHOD: We analyzed multiple pieces of demographic, criminogenic, and clinical data on three nested subsamples of respondents on the basis of the legal process: MA consent (n = 713), MA trial (n = 316), and disposition hearing (n = 643). The binary outcomes of interest were as follows: For the MA consent subsample, it was whether the respondent waived their MA trial; for the MA trial subsample, it was whether the respondent was found at trial to have an MA; and for the disposition hearing, it was whether the respondent was ordered to inpatient or outpatient civil management. RESULTS: The strongest predictor of waiving the trial was geographic location; respondents outside New York City and Long Island were more likely to waive their trials (ORs = 2.38-3.37). The strongest predictors of MA trial and disposition hearing outcomes were Diagnostic and Statistical Manual of Mental Disorders diagnoses; pedophilia (ORs = 4.05-7.22) and sexual sadism (ORs = 2.68-7.03) diagnoses increased the likelihood of an MA finding and confinement order. CONCLUSIONS: Judges and juries give significant weight to clinical information, particularly pedophilia diagnoses, when making civil management legal decisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Identification of risk factors for incident cervical insufficiency in nulliparous and parous women: a population-based case-control study.

BACKGROUND: Cervical insufficiency is one of the underlying causes of late miscarriage and preterm birth. Although many risk factors have been identified, the relative magnitude of their association with risk in nulliparous versus parous women has not been well demonstrated, especially for incident cervical insufficiency (ICI). The aim of this study was to investigate and compare the magnitude of the association of ICI with predictive factors in nulliparous and parous women, and to further investigate various aspects of obstetric history for parous women. METHODS: Pregnant women with a first diagnosis of cervical insufficiency were compared to a random sample of control pregnancies from women with no diagnosis by using Swedish national health registers. Demographic, reproductive, and pregnancy-specific factors were compared in case and control pregnancies, and relative risks presented as odds ratios (OR), stratified by nulliparous/parous. Independent associations with ICI were estimated from multivariable logistic regression. Associations with obstetric history were further estimated for multiparous women. RESULTS: A total of 759 nulliparous ICI cases and 1498 parous cases were identified during the study period. Multifetal gestation had a strong positive association with ICI in both groups, but of much larger magnitude for nulliparous women. The number of previous miscarriages was also a much stronger predictor of risk in nulliparous women, especially for multifetal pregnancies. History of preterm delivery (<37 weeks' gestation) was an independent predictor for parous women, and for those whose most recent delivery was preterm, the association with ICI increased with each additional week of prematurity. A previous delivery with prolonged second stage of labor or delivery of a very large infant were both inversely associated with risk of ICI in the current pregnancy. CONCLUSIONS: The differences in importance of predictive risk factors for incident cervical insufficiency in nulliparous and parous women can help resolve some of the inconsistencies in the literature to date regarding factors that are useful for risk prediction. Stratifying on parity can inform more targeted surveillance of at-risk pregnancies, enable the two groups of women to be better informed of their risks, and eventually inform screening and intervention efforts.

Raising the Question: Medication Mix-Up or Diversion.

CASE: Max is an 8-year-old boy with autism spectrum disorder and long-standing challenges with sleep maintenance, the latter of which persist despite behavioral intervention and environmental modification. When Max wakes in the early morning hours, he tends to wander the house, which causes his mother to be awake to monitor his safety. Given the impact of Max's fragmented sleep on his functioning and that of his family, you begin a trial of gabapentin liquid to promote sleep maintenance. Soon after, Max's mother reports that he is sleeping through the night, for the first time in his life.Two months later, you receive a message from Max's mother requesting an early refill of his 90-day supply because of having spilled the bottle. You provide a new prescription, and Max's insurance company allows the early refill. Six weeks after that, Max's mother calls to say that she needs another gabapentin prescription because Max has run out. You confirm that she is giving the prescribed dose but are unsure as to why Max is out of medication weeks early. Given these events, you begin to question whether Max's gabapentin prescription is being diverted. What would you do next?

Estimating risk ratio from any standard epidemiological design by doubling the cases.

BACKGROUND: Despite the ease of interpretation and communication of a risk ratio (RR), and several other advantages in specific settings, the odds ratio (OR) is more commonly reported in epidemiological and clinical research. This is due to the familiarity of the logistic regression model for estimating adjusted ORs from data gathered in a cross-sectional, cohort or case-control design. The preservation of the OR (but not RR) in case-control samples has contributed to the perception that it is the only valid measure of relative risk from case-control samples. For cohort or cross-sectional data, a method known as 'doubling-the-cases' provides valid estimates of RR and an expression for a robust standard error has been derived, but is not available in statistical software packages. METHODS: In this paper, we first describe the doubling-of-cases approach in the cohort setting and then extend its application to case-control studies by incorporating sampling weights and deriving an expression for a robust standard error. The performance of the estimator is evaluated using simulated data, and its application illustrated in a study of neonatal jaundice. We provide an R package that implements the method for any standard design. RESULTS: Our work illustrates that the doubling-of-cases approach for estimating an adjusted RR from cross-sectional or cohort data can also yield valid RR estimates from case-control data. The approach is straightforward to apply, involving simple modification of the data followed by logistic regression analysis. The method performed well for case-control data from simulated cohorts with a range of prevalence rates. In the application to neonatal jaundice, the RR estimates were similar to those from relative risk regression, whereas the OR from naive logistic regression overestimated the RR despite the low prevalence of the outcome. CONCLUSIONS: By providing an R package that estimates an adjusted RR from cohort, cross-sectional or case-control studies, we have enabled the method to be easily implemented with familiar software, so that investigators are not limited to reporting an OR and can examine the RR when it is of interest.

Population-based incidence and risk factors for cholestasis in hemolytic disease of the fetus and newborn.

OBJECTIVE: To estimate the incidence of cholestasis in neonates with hemolytic disease of the fetus and newborn (HDFN) and investigate risk factors and long-term liver disease. STUDY DESIGN: A population-based cohort study of all infants born with HDFN within the Stockholm region between 2006 and 2015. The study period was the first 90 days of life, and presence of any chronic liver disease was evaluated at two years of age. RESULTS: Cholestasis occurred in 7% (11/149). Median age at detection was 1.1 days. Intrauterine blood transfusions and maternal alloimmunization with multiple red blood cell antibodies including D-, c- or K-antibodies were independent risk factors for cholestasis. No infant had chronic liver disease at two years of age. CONCLUSIONS: Infants with severe HDFN have increased risk for cholestasis, particularly those requiring multiple intrauterine transfusions. Early and repeated screening for conjugated hyperbilirubinemia in the first week of life is needed to ensure adequate management.

Pooled testing for SARS-CoV-2, options for efficiency at scale.

Widescale testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is recognized as a key element of surveillance and outbreak control in the coronavirus disease 2019 (COVID-19) pandemic. The practical challenges, however, have often led to testing only symptomatic individuals and their close contacts. As many countries plan for a cautious relaxation of social restrictions, more effective approaches for widescale testing are increasingly important. Early in the COVID-19 pandemic, laboratories in several countries demonstrated the feasibility of detecting SARS-CoV-2 infection by pooled testing, which combines the specimens from several individuals. Since no further testing is needed for individuals in a negative pool, there is potential for greater efficiency of testing. Despite validations of the accuracy of the results and the efficiency in testing specific groups, the benefits of pooling are less acknowledged as a population surveillance strategy that can detect new disease outbreaks without posing restrictions on entire societies. Pooling specimens from natural clusters, such as school classes, sports teams, workplace colleagues and other social networks, would enable timely and cost-effective widescale testing for SARS-CoV-2. The initial result would be readily translatable into action in terms of quarantine and isolation policies. Clusters of uninfected individuals would be quickly identified and immediate local lockdown of positive clusters would be the appropriate and sufficient action while retesting those individuals. By adapting to the social networks of a population, pooled testing offers a cost-efficient surveillance system that is synchronized with quarantine policies that are rational, risk-based and equitable.

Le dépistage à grande échelle de l'infection au coronavirus 2 du syndrome respiratoire aigu sévère (SARS-CoV-2) est considéré comme l'un des piliers de la surveillance et de la lutte contre la pandémie de maladie à coronavirus 2019 (COVID-19). Néanmoins, les défis pratiques inhérents à ce dépistage ont souvent poussé à ne tester que les individus symptomatiques et leurs contacts rapprochés. Alors que de nombreux pays prévoient de lever certaines restrictions sociales, il devient de plus en plus important d'adopter des approches plus efficaces pour dépister à grande échelle. Dès le début de la pandémie de COVID-19, des laboratoires répartis dans divers pays ont démontré qu'il était possible de détecter une infection au SARS-CoV-2 par le biais de tests groupés, qui rassemblent des échantillons provenant de plusieurs individus. Si le résultat de l'un des groupes est négatif, nul besoin de tester chaque individu, ce qui pourrait accroître considérablement l'efficacité du processus. Même si les résultats se sont révélés fiables et que le dépistage de groupes spécifiques a prouvé l'efficacité du système, le regroupement d'échantillons est rarement envisagé comme stratégie de surveillance de la population permettant d'identifier toute nouvelle flambée des contaminations sans imposer de restrictions à l'ensemble de la société. Pourtant, le regroupement d'échantillons provenant de foyers épidémiques naturels tels que les salles de classe, les équipes sportives, les collègues de travail et autres interactions sociales ferait gagner du temps et de l'argent lors du dépistage à grande échelle du SARS-CoV-2. Le résultat initial pourrait aussitôt se traduire par des mesures d'isolation et de quarantaine. Les groupes d'individus non infectés seraient rapidement repérés, et le confinement immédiat des groupes positifs constituerait une intervention appropriée et suffisante pendant que ces individus subiraient un nouveau test. En s'adaptant aux interactions sociales d'une population donnée, les tests groupés représentent une solution de surveillance rentable en phase avec une politique de quarantaine rationnelle, équitable et fondée sur une analyse des risques.

Las pruebas a gran escala para detectar la infección por el coronavirus del síndrome respiratorio agudo grave-2 (SARS-CoV-2) se reconocen como un elemento clave de la vigilancia y el control de los brotes de la pandemia por enfermedad del coronavirus (COVID-19). Sin embargo, los desafíos prácticos han llevado a menudo a realizar pruebas solo a las personas sintomáticas y a sus contactos cercanos. A medida que muchos países planifican una cautelosa relajación de las restricciones sociales, es cada vez más importante contar con enfoques más eficaces para la realización de pruebas a gran escala. Al principio de la pandemia de COVID-19, los laboratorios de varios países demostraron la viabilidad de la detección de la infección por el SARS-CoV-2 mediante pruebas conjuntas, que combinan las muestras de varias personas. Dado que no se necesitan más pruebas para las personas en un grupo negativo, existe la posibilidad de que las pruebas sean más eficaces. A pesar de las validaciones de la exactitud de los resultados y la eficiencia en las pruebas de grupos específicos, los beneficios de la agrupación son menos reconocidos como una estrategia de vigilancia de la población que puede detectar nuevos brotes de la enfermedad sin plantear restricciones en sociedades enteras. La puesta en común de muestras procedentes de grupos naturales, como clases escolares, equipos deportivos, compañeros de trabajo y otras redes sociales, permitiría realizar pruebas a gran escala, oportunas y rentables, para detectar el SARS-CoV-2. El resultado inicial permitiría ejecutar fácilmente políticas de cuarentena y aislamiento. Se identificarían rápidamente los grupos de personas no infectadas y bastaría con una cuarentena inmediata de los grupos positivos mientras se vuelven a realizar las pruebas a dichas personas. Al adaptarse a las redes sociales de una población, las pruebas conjuntas ofrecen un sistema de vigilancia rentable que se sincroniza con políticas de cuarentena que son racionales, basadas en el riesgo y equitativas.

Management and clinical consequences of red blood cell antibodies in pregnancy: A population-based cohort study.

INTRODUCTION: Anti-D alloimmunization is the most common cause of severe hemolytic disease of the fetus and newborn (HDFN). The management of pregnancies affected by less frequent red blood cell (RBC) antibodies poses a challenge to clinicians, and perinatal outcomes are less well described. This study aimed to describe the frequency of clinically significant RBC antibodies in our pregnant population and analyze the risk of prenatal and postnatal treatment for HDFN in relation to our national risk classification system and management guidelines. MATERIAL AND METHODS: A retrospective cohort study in the population of all alloimmunized singleton pregnancies in the Stockholm region 1990-2016. Descriptive summaries of different RBC antibodies and pregnancy outcomes were presented, the risks of intrauterine blood transfusion (IUT) and neonatal treatment for HDFN were estimated by type of antibodies. RESULTS: Of the 1724 alloimmunized pregnancies, 1079 (63%) were at risk of HDFN and constituted our study cohort. Anti-D was detected in 492 (46%) pregnancies, followed by anti-E in 161 (15%), and anti-c in 128 (12%). Eighty-seven (8%) pregnancies had IUT, with the highest risk in pregnancies affected by anti-D combined with other antibodies. The maximum titer recorded before IUT was 64 or above, except for two pregnancies affected by anti-c, for which the maximum titers were 8 and 16. For the 942 (95%) live-born neonates from 992 alloimmunized pregnancies without IUT, the median gestational age at birth was 38+5  weeks compared with 35+5  weeks for those who had IUT. Neonatal treatment was most common in the anti-D alone and anti-D combined groups, with 136 (57%) and 21 (44%), respectively, treated with phototherapy and 35 (15%) and 9 (20%) receiving exchange transfusions, respectively. For pregnancies complicated by moderate- and low-risk antibodies, phototherapy was less frequent (32 [36%] and 21 [19%]) and exchange transfusion was rare (5 [6%] and 3 [3%]). CONCLUSIONS: Anti-D, especially in combination with other antibodies, presents the highest risk of severe HDFN. The classification of less frequent and less well-known RBC antibodies into risk groups can help clinicians in assessing the risk of HDFN and counseling alloimmunized pregnant women regarding the risk of prenatal and postnatal treatments.

Preeclampsia Prevalence, Risk Factors, and Pregnancy Outcomes in Sweden and China.

Importance: Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality worldwide. Within-country studies have reported racial differences in the presentation and outcome, but little is known about differences between countries. Objective: To compare preeclampsia prevalence, risk factors, and pregnancy outcomes between the Swedish and Chinese populations. Design, Setting, and Participants: This cross-sectional study compared deliveries from the Swedish national Medical Birth Register (2007-2012) and the China Labor and Delivery Survey (2015-2016). The Swedish Medical Birth Register records maternal, pregnancy, and neonatal information for nearly all deliveries in Sweden. The China Labor and Delivery Survey was conducted throughout China, and these data were reweighted to enable national comparisons. Participants included 555 446 deliveries from Sweden and 79 243 deliveries from China. Data management and analysis was conducted from November 2018 to August 2020 and revised in February to March 2021. Exposures: Maternal characteristics, parity, multiple gestation, chronic and gestational diabetes, cesarean delivery. Main Outcomes and Measures: Preeclampsia prevalence and risk factors, overall and for mild and severe forms and rates of adverse neonatal outcomes compared with pregnancies with no gestational hypertension. Results: The 555 446 Swedish pregnancies and 79 243 Chinese pregnancies had mean (SD) maternal age of 30.9 (5.3) years and 28.6 (4.6) years, respectively. The overall prevalence of preeclampsia was similar in Sweden and China, 16 068 (2.9%) and 1803 (2.3%), respectively, but with 5222 cases (32.5%) considered severe in Sweden and 1228 cases (68.1%) considered severe in China. Obesity (defined as BMI ≥28 in China and BMI ≥30 in Sweden) was a stronger risk factor in China compared with Sweden (China: odds ratio [OR], 5.12; 95% CI, 3.82-6.86; Sweden: OR, 3.49; 95% CI, 3.31-3.67). Nulliparity had a much stronger association with severe preeclampsia in Sweden compared with China (Sweden: OR, 3.91; 95% CI, 3.65-4.18; China: OR, 1.65; 95% CI, 1.20-2.25). The overall stillbirth rate for singleton in China was more than 3-fold higher than in Sweden (846/77 512[1.1%] vs 1753/547 219 [0.3%], P < .001), and 10-fold higher among women with preeclampsia (66/1652 [4.6%] vs 60/14 499[0.4%], P < .001). Conclusions and Relevance: In this study, the prevalence rates of preeclampsia in Sweden and China were similar, but women in China had more severe disease and worse pregnancy outcomes than women in Sweden. The associations of obesity and nulliparity with preeclampsia suggest a role for lifestyle and health care factors but may reflect some differences in pathophysiology. These findings have relevance for current efforts to identify high-risk pregnancies and early serum markers because the value of risk prediction models and biomarkers may be population specific.

A New Procedure to Assess When Estimates from the Cumulative Link Model Can Be Interpreted as Differences for Ordinal Scales in Quality of Life Studies.

PURPOSE: Assessing the clinical importance of an exposure effect on a quality of life (QoL) score often requires quantifying the effect in terms of a difference in scores. Using the linear regression model (LRM) for this purpose assumes the ordinal score is a proxy for an underlying continuous variable, but the analysis offers no assessment for the validity of the assumption. We propose an approach that assesses the proxy assumption and estimates the exposure effect by using the cumulative link model (CLM). PATIENTS AND METHODS: CLM is a well-established regression model that assumes an ordinal score is an ordered category generated from applying thresholds to a latent continuous variable. Our approach assesses the proxy assumption by testing whether these thresholds are equidistant. We compared the performance of CLM and LRM using simulated ordinal data and illustrated their application to the effect of time since diagnosis on five subscales of fatigue among breast cancer survivors measured using the Multidimensional Fatigue Inventory. RESULTS: CLM had good performance in estimating the difference in means with simulated ordinal data satisfying the proxy assumption, even when the outcome had only a few categories. When the proxy assumption was inadequate, both the CLM and LRM had biased estimates with poor coverage. The proxy assumption was appropriate for four of the five subscales in our real data application to fatigue scores, which highlighted the importance of assessing the proxy assumption to avoid reporting invalid estimates in terms of the difference in scores. CONCLUSION: The proxy assumption is critical to the interpretation of the exposure effect on the difference in mean QoL scores. CLM offers a valid test for the presence of an association, a method for assessing the proxy assumption, and when the assumption is adequate, an assessment for clinical significance using the difference in means.

Growth patterns and their contributing factors among HIV-exposed uninfected infants.

With expanded HIV treatment and prevention programmes, most infants born to HIV-positive women are uninfected, but the patterns and determinants of their growth are not well described. This study aimed to assess growth patterns in a cohort of HIV-exposed uninfected (HEU) infants who participated in an experimental HIV vaccine trial and to test for associations with maternal and infant factors, including in-utero exposure to antiretroviral therapy (ART), mode of delivery, exclusive breastfeeding, mother's education and receipt of the vaccine. Infants in the trial were seen at regular clinic visits from birth to 48 weeks of age. From the anthropometric measurements at these visits, weight-for-age z-scores (WAZ), weight-for-length z-scores (WLZ) and length-for-age z-scores (LAZ) were computed using World Health Organization (WHO) software and reference tables. Growth patterns were investigated with respect to maternal and infant factors, using linear mixed regression models. From 94 infants included at birth, growth data were available for 75.5% at 48 weeks. The determinants of infant growth in this population are multifactorial: infant LAZ during the first year was significantly lower among infants delivered by caesarean section (p = 0.043); both WAZ and LAZ were depressed among infants with longer exposure to maternal ART (WAZ: p = 0.015; LAZ: p < 0.0001) and among infants of mothers with lower educational level (WAZ: p = 0.038; LAZ: p < 0.0001); the effect of maternal education was modified by breastfeeding practice, with no differences seen in exclusively breastfed infants. These findings inform intervention strategies to preserve growth in this vulnerable infant population.

Robust estimation of the effect of an exposure on the change in a continuous outcome.

BACKGROUND: The change in two measurements of a continuous outcome can be modelled directly with a linear regression model, or indirectly with a random effects model (REM) of the individual measurements. These methods are susceptible to model misspecifications, which are commonly addressed by applying monotonic transformations (e.g., Box-Cox transformation) to the outcomes. However, transforming the outcomes complicates the data analysis, especially when variable selection is involved. We propose a robust alternative through a novel application of the conditional probit (cprobit) model. METHODS: The cprobit model analyzes the ordered outcomes within each subject, making the estimate invariant to monotonic transformation on the outcome. By scaling the estimate from the cprobit model, we obtain the exposure effect on the change in the observed or Box-Cox transformed outcome, pending the adequacy of the normality assumption on the raw or transformed scale. RESULTS: Using simulated data, we demonstrated a similar good performance of the cprobit model and REM with and without transformation, except for some bias from both methods when the Box-Cox transformation was applied to scenarios with small sample size and strong effects. Only the cprobit model was robust to skewed subject-specific intercept terms when a Box-Cox transformation was used. Using two real datasets from the breast cancer and inpatient glycemic variability studies which utilize electronic medical records, we illustrated the application of our proposed robust approach as a seamless three-step workflow that facilitates the use of Box-Cox transformation to address non-normality with a common underlying model. CONCLUSIONS: The cprobit model provides a seamless and robust inference on the change in continuous outcomes, and its three-step workflow is implemented in an R package for easy accessibility.

Amniotic immune biomarkers as risk factors in women with different symptoms of threatened late miscarriage.

PROBLEM: To investigate risk factors that can help identify the possibility of pregnancy loss in threatened late miscarriage (TLM) patients with and without spontaneous uterine contractions. METHOD OF STUDY: Amniotic immune biomarkers (IL2ß receptor, IL6, IL8, IL10, IL1ß, and TNFα) were assayed, and "sludge" was assessed. Patients without intrauterine infections were treated and followed up until delivery, and pregnancy outcomes were recorded. The two groups were compared for the differences in biomarker levels and "sludge," and the independent associations of biomarkers, "sludge," and other maternal factors with late miscarriage were investigated. RESULTS: The amniotic levels of IL2ßR, IL8, and TNFα were higher in the group with contractions (P < .05). When considered alone, each of the six biomarkers was significantly associated with late miscarriage in the no-contractions group and four of these (IL8, IL10, IL1ß, and TNFα) in the contractions group (P < .05). Biomarker levels were correlated, and in multivariate Cox regression analysis, there was an independent effect only for IL8 in the no-contractions group (HR = 18.16, 95% CI: 5.75-57.43) and TNFα in the contractions group (HR = 4.11, 95% CI: 1.68-10.08). For patients with contractions, IL10, IL8, and IL1ß were different in those with and without "sludge," but no such difference was seen in the no-contractions group. CONCLUSION: For TLM patients without intrauterine infections, amniotic immune biomarkers differ between patients with different symptoms, not only for their levels but also for the impact of these biomarkers on the risk of late miscarriage. These findings suggest that the symptoms of TLM should be considered in the study of miscarriage risk.

Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes.

The rank-ordered logit (rologit) model was recently introduced as a robust approach for analysing continuous outcomes, with the linear exposure effect estimated by scaling the rank-based log-odds estimate. Here we extend the application of the rologit model to continuous outcomes with ties and ordinal outcomes treated as imperfectly-observed continuous outcomes. By identifying the functional relationship between survival times and continuous outcomes, we explicitly establish the equivalence between the rologit and Cox models to justify the use of the Breslow, Efron and perturbation methods in the analysis of continuous outcomes with ties. Using simulation, we found all three methods perform well with few ties. Although an increasing extent of ties increased the bias of the log-odds and linear effect estimates and resulted in reduced power, which was somewhat worse when the model was mis-specified, the perturbation method maintained a type I error around 5%, while the Efron method became conservative with heavy ties but outperformed Breslow. In general, the perturbation method had the highest power, followed by the Efron and then the Breslow method. We applied our approach to three real-life datasets, demonstrating a seamless analytical workflow that uses stratification for confounder adjustment in studies of continuous and ordinal outcomes.

Association of blood group and red blood cell transfusion with the incidence of antepartum, peripartum and postpartum venous thromboembolism.

The increased risk of venous thromboembolism (VTE) associated with pregnancy is well-known and prophylaxis guidelines consider a number of risk factors. Although non-O blood group and red blood cell (RBC) transfusion are known to be associated with VTE risk, their contribution to pregnancy-associated VTE has received little attention. This study was conducted in a population-based cohort of 1,000,997 deliveries to women with no prior history of VTE or thrombophilia. The independent contributions of ABO blood type and RBC transfusion to the risks of antepartum, peripartum and postpartum VTE are reported as odds ratios adjusted for risk factors that are considered in current prophylaxis guidelines and other potential confounders. Compared with type O, A and B blood types have higher risk of antepartum and postpartum VTE, with odds ratios between 1.4 and 1.8. Transfusion around delivery has the largest increased risks and a dose-response effect, with adjusted odds ratios from 2.60 (1.71-3.97) for 1-2 units to 3.55 (1.32-9.55) for more than 5 units. ABO blood type and RBC transfusion were found to be independent risk factors for pregnancy-associated VTE. Further research is required to understand the underlying mechanisms and to conduct a risk-benefit assessment of the small volumes of RBCs transfused around delivery.

A stratification approach using logit-based models for confounder adjustment in the study of continuous outcomes.

The control of confounding is an area of extensive epidemiological research, especially in the field of causal inference for observational studies. Matched cohort and case-control study designs are commonly implemented to control for confounding effects without specifying the functional form of the relationship between the outcome and confounders. This paper extends the commonly used regression models in matched designs for binary and survival outcomes (i.e. conditional logistic and stratified Cox proportional hazards) to studies of continuous outcomes through a novel interpretation and application of logit-based regression models from the econometrics and marketing research literature. We compare the performance of the maximum likelihood estimators using simulated data and propose a heuristic argument for obtaining the residuals for model diagnostics. We illustrate our proposed approach with two real data applications. Our simulation studies demonstrate that our stratification approach is robust to model misspecification and that the distribution of the estimated residuals provides a useful diagnostic when the strata are of moderate size. In our applications to real data, we demonstrate that parity and menopausal status are associated with percent mammographic density, and that the mean level and variability of inpatient blood glucose readings vary between medical and surgical wards within a national tertiary hospital. Our work highlights how the same class of regression models, available in most statistical software, can be used to adjust for confounding in the study of binary, time-to-event and continuous outcomes.

Young Children With Gender Nonconforming Behaviors and Preferences.

BACKGROUND: There is growing awareness and exposure in both the medical community and the lay media about the characteristics and complex needs of individuals who believe that their gender identity does not match their birth sex. Despite research and lay publications about teens with gender dysphoria and those who identify as transgender, little guidance is available regarding young (prepubertal) children with questions about their gender identity. Although many terms are used to describe these children, we have chosen to describe them as "gender nonconforming" (GNC). OBJECTIVE: Primary care and developmental-behavioral pediatric providers are often the first professionals with whom young gender nonconforming children and their families discuss their concerns about their emerging gender identity. It is important, therefore, that pediatric providers be knowledgeable about the dilemmas, conflicts, and choices that are typical of these children and their families to guide them appropriately. OVERVIEW: In this special article, we present observations, informed by clinical experience, an emerging body of research, and a developmental-behavioral pediatric framework, of the complex needs of prepubertal gender nonconforming children and their families and an approach to their care. The article begins by outlining the cognitive and biological bases for gender identity development, as well as the natural history of gender nonconforming preferences and behaviors. It then sets the context for understanding the care of GNC children as an area in which developmentally sophisticated providers can play a crucial role in support of the complex developmental patterns and need for advocacy in multiple settings among these children.

Quantifying temporal trends of age-standardized rates with odds.

BACKGROUND: To quantify temporal trends in age-standardized rates of disease, the convention is to fit a linear regression model to log-transformed rates because the slope term provides the estimated annual percentage change. However, such log-transformation is not always appropriate. METHODS: We propose an alternative method using the rank-ordered logit (ROL) model that is indifferent to log-transformation. This method quantifies the temporal trend using odds, a quantity commonly used in epidemiology, and the log-odds corresponds to the scaled slope parameter estimate from linear regression. The ROL method can be implemented by using the commands for proportional hazards regression in any standard statistical package. We apply the ROL method to estimate temporal trends in age-standardized cancer rates worldwide using the cancer incidence data from the Cancer Incidence in Five Continents plus (CI5plus) database for the period 1953 to 2007 and compare the estimates to their scaled counterparts obtained from linear regression with and without log-transformation. RESULTS: We found a strong concordance in the direction and significance of the temporal trends in cancer incidence estimated by all three approaches, and illustrated how the estimate from the ROL model provides a measure that is comparable to a scaled slope parameter estimated from linear regression. CONCLUSIONS: Our method offers an alternative approach for quantifying temporal trends in incidence or mortality rates in a population that is invariant to transformation, and whose estimate of trend agrees with the scaled slope from a linear regression model.

Helping a Child with Autism Spectrum Disorder Cope with Divorce.

CASE: Aaron is an 11-year-old boy with autism spectrum disorder (ASD), with cognitive and language skills in the above-average range, whose parents have recently separated. Aaron's mother initiated the separation when she learned that Aaron's father had maintained a relationship with a woman with whom he has a 10-year-old daughter. When Aaron's mother discovered this relationship, she demanded that Aaron's father leave their home.Aaron's father has moved in with his long-term girlfriend and keeps in contact with Aaron by calling once a day. Neither Aaron's father nor mother has discussed the reason for their separation with Aaron. So far, they have explained their separation by telling Aaron that they are "taking a break."Aaron's mother has been deeply hurt by Aaron's father's infidelity and does not want to reconcile with him. Aaron's father recognizes this but would like to continue to have a close relationship with his son. He would also like Aaron to get to know his half-sister.Aaron's mother seeks guidance regarding how to talk to Aaron about the separation and his father's second family. Given Aaron's diagnosis of ASD, she is particularly concerned about his ability to cope with this unexpected change in circumstances. What is your advice?

Valid and efficient subgroup analyses using nested case-control data.

BACKGROUND: It is not uncommon for investigators to conduct further analyses of subgroups, using data collected in a nested case-control design. Since the sampling of the participants is related to the outcome of interest, the data at hand are not a representative sample of the population, and subgroup analyses need to be carefully considered for their validity and interpretation. METHODS: We performed simulation studies, generating cohorts within the proportional hazards model framework and with covariate coefficients chosen to mimic realistic data and more extreme situations. From the cohorts we sampled nested case-control data and analysed the effect of a binary exposure on a time-to-event outcome in subgroups defined by a covariate (an independent risk factor, a confounder or an effect modifier) and compared the estimates with the corresponding subcohort estimates. Cohort analyses were performed with Cox regression, and nested case-control samples or restricted subsamples were analysed with both conditional logistic regression and weighted Cox regression. RESULTS: For all studied scenarios, the subgroup analyses provided unbiased estimates of the exposure coefficients, with conditional logistic regression being less efficient than the weighted Cox regression. CONCLUSIONS: For the study of a subpopulation, analysis of the corresponding subgroup of individuals sampled in a nested case-control design provides an unbiased estimate of the effect of exposure, regardless of whether the variable used to define the subgroup is a confounder, effect modifier or independent risk factor. Weighted Cox regression provides more efficient estimates than conditional logistic regression.