Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.

Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method's robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

Joint regression modelling of intensity and timing of accelerometer counts.

Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical activity and sleep behaviours studies, to identification of movement patterns in people affected by diseases of the locomotor system and prediction of risk of injury in high performance sports. The accelerometer output provides the intensity (activity count) and timing (timestamp) of the movement, which can be used to define bouts of activity (periods of sustained movement of a given intensity). In some contexts, it may be important to include both dimensions to obtain a broader and deeper understanding of the phenomenon under study. Such is the case of a large-scale epidemiological investigation on the daily and weekly physical activity behaviours of school-aged children enrolled in the UK Millennium Cohort Study, which has motivated the present article. I present a statistical approach to joint modelling of intensity and timing of activity bouts that takes advantage of the circular nature of the timing. The model, which accounts for the longitudinal structure of the observations, is remarkably simple to implement using standard statistical software.

Household food insecurity is associated with diabetic ketoacidosis but not severe hypoglycemia or glycemic control in youth and young adults with youth-onset type 2 diabetes.

OBJECTIVE: To examine the association between household food insecurity (HFI), glycemic control, severe hypoglycemia and diabetic ketoacidosis (DKA) among youth and young adults (YYA) with youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS: This cross-sectional study included 395 YYA with type 2 diabetes from the SEARCH for Diabetes in Youth Study (2015-2019). HFI was reported by young adult participants or parents of minor participants via the US Household Food Security Survey Module. Glycemic control was assessed by HbA1c and analyzed as a continuous and categorical variable (optimal: <7.0%, suboptimal: ≥7.0%-9.0%, poor: >9.0%). Acute complications included self-reported severe hypoglycemia or DKA in the last 12 months. Adjusted logistic and linear regression were used for binary and continuous outcomes, respectively. RESULTS: Approximately 31% reported HFI in the past 12 months. Mean HbA1c among those with HFI was 9.2% compared to 9.5% without HFI. Of those with HFI, 56% had an HbA1c >9.0% compared to 55% without HFI. Adjusted models showed no associations between HFI and glycemic control. Of those with HFI, 14.4% reported experiencing DKA and 4.7% reported severe hypoglycemia. YYA with HFI had 3.08 times (95% CI: 1.18-8.06) the odds of experiencing DKA as those without HFI. There was no association between HFI and severe hypoglycemia. CONCLUSIONS: HFI was associated with markedly increased odds of DKA but not with glycemic control or severe hypoglycemia. Future research among YYA with type 2 diabetes should evaluate longitudinally whether alleviating HFI reduces DKA.

Residential Segregation and Emergency Department Utilization Among an Underserved Urban Emergency Department Sample in North Carolina.

BACKGROUND Residential segregation is a spatial manifestation of structural racism. Racial disparities in emergency department (ED) utilization mirror social inequity in the larger community. We evaluated associations between residential segregation and ED utilization in a community with known disparities and geographically concentrated social and health risk.METHODS Cross-sectional data were collected from electronic medical records of 101 060 adult ED patients living in Mecklenburg County, North Carolina in 2017. Community context was measured as residential segregation using the dissimilarity index, categorized into quintiles (Q1-Q5) using 2013-2017 American Community Survey estimates, and residency in a public health priority area (PHPA). The outcome was measured as total ED visits during the study period. Associations between community context and ED utilization were modeled using Anderson's behavioral model of health service utilization, and estimated using negative binomial regression, including interaction terms by race.RESULTS Compared to areas with the lowest proportions of Black residents (Q1), living in Q4 was associated with higher rates of ED utilization among Black/Other (AME = 0.11) and White (AME = 0.23) patients, while associations with living in Q5 were approximately equivalent (AME = 0.12). PHPA residency was associated with higher rates of ED utilization among Black/Other (AME = 0.10) and White patients (AME = 0.22).LIMITATIONS Associations should not be interpreted as causal, or be generalized to the larger community without ED utilization. Health system leakage is possible but limited.CONCLUSIONS Residential segregation is associated with higher rates of ED utilization, as are PHPA residency and other individual-level determinants.

Ambulatory care, insurance, and avoidable emergency department utilization in North Carolina.

OBJECTIVE: To examine whether and how avoidable emergency department (ED) utilization is associated with ambulatory or primary care (APC) utilization, insurance, and interaction effects. DESIGN AND SAMPLE: A cross-sectional analysis of electronic health records from 70,870 adults residing in Mecklenburg County, North Carolina, who visited an ED within a large integrated healthcare system in 2017. METHODS: APC utilization was measured as total visits, categorized as: 0, 1, and > 1. Insurance was defined as the method of payment for the ED visit as: Medicaid, Medicare, private, or uninsured. Avoidable ED utilization was quantified as a score (aED), calculated as the sum of New York University Algorithm probabilities multiplied by 100. Quantile regression models were used to predict the 25th, 50th, 75th, 95th, and 99th percentiles of avoidable ED scores with APC visits and insurance as predictors (Model 1) and with an interaction term (Model 2). RESULTS: Having >1 APC visit was negatively associated with aED at the lower percentiles and positively associated at higher percentiles. A higher aED was associated with having Medicaid insurance and a lower aED was associated with having private insurance, compared to being uninsured. In stratified models, having >1 APC visit was negatively associated with aED at the 25th percentile for the uninsured and privately insured, but positively associated with aED at higher percentiles among the uninsured, Medicaid-insured, and privately insured. CONCLUSIONS: The association between APC utilization and avoidable ED utilization varied based on segments of the distribution of ED score and differed significantly by insurance type.

Multistate quantile regression models.

We develop regression methods for inference on conditional quantiles of time-to-transition in multistate processes. Special cases include survival, recurrent event, semicompeting, and competing risk data. We use an ad hoc representation of the underlying stochastic process, in conjunction with methods for censored quantile regression. In a simulation study, we demonstrate that the proposed approach has a superior finite sample performance over simple methods for censored quantile regression, which naively assume independence between states, and over methods for competing risks, even when the latter are applied to competing risk data settings. We apply our approach to data on hospital-acquired infections in cirrhotic patients, showing a quantile-dependent effect of catheterization on time to infection.

Identification and evaluation of risk of generalizability biases in pilot versus efficacy/effectiveness trials: a systematic review and meta-analysis.

BACKGROUND: Preliminary evaluations of behavioral interventions, referred to as pilot studies, predate the conduct of many large-scale efficacy/effectiveness trial. The ability of a pilot study to inform an efficacy/effectiveness trial relies on careful considerations in the design, delivery, and interpretation of the pilot results to avoid exaggerated early discoveries that may lead to subsequent failed efficacy/effectiveness trials. "Risk of generalizability biases (RGB)" in pilot studies may reduce the probability of replicating results in a larger efficacy/effectiveness trial. We aimed to generate an operational list of potential RGBs and to evaluate their impact in pairs of published pilot studies and larger, more well-powered trial on the topic of childhood obesity. METHODS: We conducted a systematic literature review to identify published pilot studies that had a published larger-scale trial of the same or similar intervention. Searches were updated and completed through December 31st, 2018. Eligible studies were behavioral interventions involving youth (≤18 yrs) on a topic related to childhood obesity (e.g., prevention/treatment, weight reduction, physical activity, diet, sleep, screen time/sedentary behavior). Extracted information included study characteristics and all outcomes. A list of 9 RGBs were defined and coded: intervention intensity bias, implementation support bias, delivery agent bias, target audience bias, duration bias, setting bias, measurement bias, directional conclusion bias, and outcome bias. Three reviewers independently coded for the presence of RGBs. Multi-level random effects meta-analyses were performed to investigate the association of the biases to study outcomes. RESULTS: A total of 39 pilot and larger trial pairs were identified. The frequency of the biases varied: delivery agent bias (19/39 pairs), duration bias (15/39), implementation support bias (13/39), outcome bias (6/39), measurement bias (4/39), directional conclusion bias (3/39), target audience bias (3/39), intervention intensity bias (1/39), and setting bias (0/39). In meta-analyses, delivery agent, implementation support, duration, and measurement bias were associated with an attenuation of the effect size of - 0.325 (95CI - 0.556 to - 0.094), - 0.346 (- 0.640 to - 0.052), - 0.342 (- 0.498 to - 0.187), and - 0.360 (- 0.631 to - 0.089), respectively. CONCLUSIONS: Pre-emptive avoidance of RGBs during the initial testing of an intervention may diminish the voltage drop between pilot and larger efficacy/effectiveness trials and enhance the odds of successful translation.

Opioid Prescription Patterns for Adults With Longstanding Disability and Inflammatory Conditions Compared to Other Users, Using a Nationally Representative Sample.

OBJECTIVES: To investigate the opioid prescription patterns for adults with longstanding physical disability and inflammatory conditions, compared to a mixed group of other opioid users, after excluding cancer patients. DESIGN: Nationally representative cross-sectional study, 2010-2014. SETTING: Medical Expenditure Panel Survey (MEPS). PARTICIPANTS: The participants (N=7134) were adults who participated in MEPS and had at least 1 opioid prescription, did not have cancer, and were between 18 years and 64 years of age. The participants were grouped as longstanding physical disability (group 1), inflammatory conditions (group 2), and a mixed group with at least 1 opioid prescription during the 2-year study period (comparison group). Participants with both groups of conditions were excluded. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Morphine milligram equivalent (MME) doses for each participant were cumulated over a 2-year panel period. RESULTS: By using quantile regression, cumulative MME in groups 1 and 2 was higher than the comparison group across all the percentiles, and differences between condition groups and comparison group became larger in higher percentiles. Participants in group 1 had the highest cumulative MME in 75th and 90th percentiles after controlling for other covariates. CONCLUSIONS: This study documented the opioid prescription patterns for patients with longstanding physical disability or inflammatory conditions. All indexed groups (groups 1 and 2) had higher MME use compared to the comparison group.

Modelling and estimation of nonlinear quantile regression with clustered data.

In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective function is nonsmooth as in the case of quantile regression. Methods are developed for the modelling and estimation of nonlinear conditional quantile functions when data are clustered within two-level nested designs. The proposed estimation algorithm is a blend of a smoothing algorithm for quantile regression and a second order Laplacian approximation for nonlinear mixed models. This optimization approach has the appealing advantage of reducing the original nonsmooth problem to an approximated L 2 problem. While the estimation algorithm is iterative, the objective function to be optimized has a simple analytic form. The proposed methods are assessed through a simulation study and two applications, one in pharmacokinetics and one related to growth curve modelling in agriculture.

In-Hospital Outcomes in Large for Gestational Age Infants at 22-29 Weeks of Gestation.

OBJECTIVE: To estimate the risks of mortality and morbidities in large for gestational age (LGA) infants relative to appropriate for gestational age infants born at 22-29 weeks of gestation. STUDY DESIGN: Data on 156 587 infants were collected between 2006 and 2014 in 852 US centers participating in the Vermont Oxford Network. We defined LGA as sex-specific birth weight above the 90th centile for gestational age measured in days. Generalized additive models with smoothing splines on gestational age by LGA status were fitted on mortality and morbidity outcomes to estimate adjusted relative risks and their 95% CIs. RESULTS: Compared with appropriate for gestational age infants, being born LGA was associated with decreased risks of mortality, respiratory distress syndrome, patent ductus arteriosus, necrotizing enterocolitis, late-onset sepsis, severe retinopathy of prematurity, and chronic lung disease. Early onset sepsis and severe intraventricular hemorrhage were increased among LGA infants, but these risks were not homogeneous across the gestational age range. CONCLUSIONS: Being born LGA was associated with lower risks for all the examined outcomes except for early onset sepsis and severe intraventricular hemorrhage.

Neonatal and fetal growth charts to identify preterm infants <30 weeks gestation at risk of adverse outcomes.

BACKGROUND: It is unclear whether a neonatal or a fetal growth standard is a better predictor of adverse in-hospital newborn infant outcomes. OBJECTIVE: We aimed to evaluate and compare the power of birthweight for gestational age to predict adverse neonatal outcomes using neonatal and fetal growth charts. Gestational age-specific birthweight was examined either as a percentile score or as a binary indicator for birthweight <10th percentile (small for gestational age) with the use of 3 fetal growth charts (National Institute of Child Health and Human Development, World Health Organization, and Intergrowth-21st) and 1 neonatal sex-specific birthweight chart. STUDY DESIGN: Inborn singleton infants from 2006-2014 with gestational age between 22 and 29 weeks and who were enrolled at 1 of the 852 US centers that were participating in the Vermont Oxford Network were studied. Outcomes included death, necrotizing enterocolitis, severe intraventricular hemorrhage, severe retinopathy of prematurity, and chronic lung disease. Receiver operating characteristic curve analysis was used to assess the predictive power of birthweight for gestational age, either as a score or as a small-for-gestational-age indicator, with the use of the 4 charts. We also examined the relative risks of the outcomes by comparing small-for-gestational-age and non-small-for-gestational-age infants with the use of the 4 charts. RESULTS: The percentage of small-for-gestational-age newborn infants ranged from 25.9-29.7% when with used the fetal growth charts. In contrast, the percentage was 10% when we used the neonatal charts. The areas under the receiver operating characteristic curves were similar across the 4 classification methods and were all <0.60, which suggests a poor predictive power. Small-for-gestational-age status, as classified by the neonatal chart, showed stronger associations with death, necrotizing enterocolitis, severe retinopathy of prematurity, and chronic lung disease, compared with those associations that were based on the other classification methods. CONCLUSION: Neither the neonatal nor the fetal growth charts are predictive of adverse infant in-hospital outcomes. In contrast to fetal charts, the use of the neonatal charts results in stronger associations between small-for-gestational-age and adverse outcomes.

Modeling normative kinetic perimetry isopters using mixed-effects quantile regression.

Kinetic perimetry is used to quantify visual field size/sensitivity. Clinically, perimetry can be used to diagnose and monitor ophthalmic and neuro-ophthalmic disease. Normative data are integral to the interpretation of these findings. However, there are few computational developments that allow clinicians to collect and analyze normative data from kinetic perimeters. In this article we describe an approach to fitting kinetic responses using linear quantile mixed models. Analogously to traditional linear mixed-effects models for the mean, linear quantile mixed models account for repeated measurements taken from the same individual, but differently from linear mixed-effects models, they are more flexible as they require weaker distributional assumptions and allow for quantile-specific inference. Our approach improves on parametric alternatives based on normal assumptions. We introduce the R package kineticF, a freely available and open-access resource for the analysis of perimetry data. Our proposed approach can be used to analyze normative data from further studies.

Does heat tolerance actually predict animals' geographic thermal limits?

The "climate extremes hypothesis" is a major assumption of geographic studies of heat tolerance and climatic vulnerability. However, this assumption remains vastly untested across taxa, and multiple factors may contribute to uncoupling heat tolerance estimates and geographic limits. Our dataset includes 1000 entries of heat tolerance data and maximum temperatures for each species' known geographic limits (hereafter, Tmax). We gathered this information across major animal taxa, including marine fish, terrestrial arthropods, amphibians, non-avian reptiles, birds, and mammals. We first tested if heat tolerance constrains the Tmax of sites where species could be observed. Secondly, we tested if the strength of such restrictions depends on how high Tmax is relative to heat tolerance. Thirdly, we correlated the different estimates of Tmax among them and across species. Restrictions are strong for amphibians, arthropods, and birds but often weak or inconsistent for reptiles and mammals. Marine fish describe a non-linear relationship that contrasts with terrestrial groups. Traditional heat tolerance measures in thermal vulnerability studies, like panting temperatures and the upper set point of preferred temperatures, do not predict Tmax or are inversely correlated to it, respectively. Heat tolerance restricts the geographic warm edges more strongly for species that reach sites with higher Tmax for their heat tolerance. These emerging patterns underline the importance of reliable species' heat tolerance indexes to identify their thermal vulnerability at their warm range edges. Besides, the tight correlations of Tmax estimates across on-land microhabitats support a view of multiple types of thermal challenges simultaneously shaping ranges' warm edges for on-land species. The heterogeneous correlation of Tmax estimates in the ocean supports the view that fish thermoregulation is generally limited, too. We propose new hypotheses to understand thermal restrictions on animal distribution.

Household Food Insecurity Is Associated With Physical Activity in Youth and Young Adults With Diabetes: A Cross-Sectional Study.

BACKGROUND: Physical activity (PA) is essential for optimal diabetes management. Household food insecurity (HFI) may negatively affect diabetes management behaviors. The purpose of this study was to cross-sectionally examine the association between HFI and PA in youth and young adults (YYA) with type 1 (N = 1998) and type 2 (N = 391) diabetes from the SEARCH for Diabetes in Youth Study. METHODS: HFI was measured with the US Household Food Security Survey Module. PA was measured with the International Physical Activity Questionnaire Short Form. Walking, moderate-intensity PA (excluding walking), vigorous-intensity PA, moderate- to vigorous-intensity PA, and total PA were estimated as minutes per week, while time spent sitting was assessed in minutes per day. All were modeled with median regression. Meeting PA guidelines or not was modeled using logistic regression. RESULTS: YYA with type 1 diabetes who experienced HFI spent more time walking than those who were food secure. YYA with type 2 diabetes who experienced HFI spent more time sitting than those who were food secure. CONCLUSIONS: Future research should examine walking for leisure versus other domains of walking in relation to HFI and use objective PA measures to corroborate associations between HFI and PA in YYA with diabetes.

A retrospective analysis of the factors associated with surgical checklist compliance using data from a local health unit in Italy, 2018-2021.

RATIONALE: Since its publication, the World Health Organization Surgical Safety Checklist (SSC) has been progressively adopted by healthcare providers around the world to monitor and safeguard the delivery of surgeries. In one Italian region's health system, the SSC and other two surgery-specific checklists were supplemented by a document that records any non-conformity (NC) arising from the safety checks. AIMS AND OBJECTIVES: In this study, we investigated the factors associated with NCs using data from a local health unit (LHU). The secondary aim of this study was to explore the potential impact of the coronavirus crisis on surgical checklist compliance. METHODS: We used data on surgical activity from the Modena LHU between 2018 and 2021 and the accompanying NC documents. The primary goal was to estimate the relative risk (RR) of NCs according to several factors, including checklist incompleteness and surgery class (elective, urgent or emergency), using Poisson regression. A similar analysis was performed separately for 2018-2019 and 2020-2021 to assess the COVID-19 potential impact. RESULTS AND CONCLUSIONS: Checklist compliance in the LHU was 95%, with the presence of NCs in about 7% of surgeries. The factors that increased the RR were incompleteness of the checklist (adjusted RR = 3.12; 95% confidence interval [CI] = 2.86-3.40), urgent surgeries (adjusted RR [aRR] = 1.59; 95% CI = 1.47-1.72), emergencies (aRR = 2.09; 95% CI = 1.15-3.79), and surgeries with more than four procedures (aRR = 1.64; 95% CI = 1.41-1.92). Most notably, the RR for incomplete checklists showed a negative association with NCs before the COVID-19 outbreak but positive afterwards. Checklist compliance was overall satisfactory, though the observation of noncompliant checklists of about 1000 per year suggests there is still room for improvement. Moreover, attention to the checklist best practices and organization of outpatient workload may have been affected by the exceptional circumstances of the pandemic.

Trends in Resources for Neonatal Intensive Care at Delivery Hospitals for Infants Born Younger Than 30 Weeks' Gestation, 2009-2020.

Importance: In an ideal regionalized system, all infants born very preterm would be delivered at a large tertiary hospital capable of providing all necessary care. Objective: To examine whether the distribution of extremely preterm births changed between 2009 and 2020 based on neonatal intensive care resources at the delivery hospital. Design, Setting, and Participants: This retrospective cohort study was conducted at 822 Vermont Oxford Network (VON) centers in the US between 2009 and 2020. Participants included infants born at 22 to 29 weeks' gestation, delivered at or transferred to centers participating in the VON. Data were analyzed from February to December 2022. Exposures: Hospital of birth at 22 to 29 weeks' gestation. Main Outcomes and Measures: Birthplace neonatal intensive care unit (NICU) level was classified as A, restriction on assisted ventilation or no surgery; B, major surgery; or C, cardiac surgery requiring bypass. Level B centers were further divided into low-volume (<50 inborn infants at 22 to 29 weeks' gestation per year) and high-volume (≥50 inborn infants at 22 to 29 weeks' gestation per year) centers. High-volume level B and level C centers were combined, resulting in 3 distinct NICU categories: level A, low-volume B, and high-volume B and C NICUs. The main outcome was the change in the percentage of births at hospitals with level A, low-volume B, and high-volume B or C NICUs overall and by US Census region. Results: A total of 357 181 infants (mean [SD] gestational age, 26.4 [2.1] weeks; 188 761 [52.9%] male) were included in the analysis. Across regions, the Pacific (20 239 births [38.3%]) had the lowest while the South Atlantic (48 348 births [62.7%]) had the highest percentage of births at a hospital with a high-volume B- or C-level NICU. Births at hospitals with A-level NICUs increased by 5.6% (95% CI, 4.3% to 7.0%), and births at low-volume B-level NICUs increased by 3.6% (95% CI, 2.1% to 5.0%), while births at hospitals with high-volume B- or C-level NICUs decreased by 9.2% (95% CI, -10.3% to -8.1%). By 2020, less than half of the births for infants at 22 to 29 weeks' gestation occurred at hospitals with high-volume B- or C-level NICUs. Most US Census regions followed the nationwide trends; for example, births at hospitals with high-volume B- or C-level NICUs decreased by 10.9% [95% CI, -14.0% to -7.8%) in the East North Central region and by 21.1% (95% CI, -24.0% to -18.2%) in the West South Central region. Conclusions and Relevance: This retrospective cohort study identified concerning deregionalization trends in birthplace hospital level of care for infants born at 22 to 29 weeks' gestation. These findings should serve to encourage policy makers to identify and enforce strategies to ensure that infants at the highest risk of adverse outcomes are born at the hospitals where they have the best chances to attain optimal outcomes.

The contrasting age-incidence patterns of bone tumours in teenagers and young adults: Implications for aetiology.

Bone tumours comprise 0.2% of cancers overall but 5.7% in 15-24 year olds. To explore the relationship with adolescence we have analysed age-incidence patterns of bone tumours in a large national dataset. Data on incident cases of bone tumours in 0-84 year olds in England, 1979-2003, were extracted from national cancer registration data. Incidence rates per million person-years by 5-year age-group, sex, morphology and primary site were calculated and adjusted to the world standard population. Nine thousand one hundred forty-six cases were identified giving an overall age-standardized rate of 7.19 per million person-years. The distribution by morphology was: osteosarcoma, 34.2%; chondrosarcoma, 27.2%; Ewing sarcoma, 19.3%; other, 19.4%. The distribution varied by age. Ewing sarcoma was most common in 0-9 year olds, osteosarcoma in 10-29 year olds and chondrosarcoma in 30-84 year olds. 29.2% of all tumours occurred in 0-24 year olds. Highest incidence of osteosarcoma and Ewing sarcoma in females was in 10-14 year olds. In males, peak incidence occurred at 15-19 years and exceeded that in females. Chondrosarcoma incidence steadily increased with age. The proportions of Ewing sarcomas occurring in respective bones were consistent with those of the adult skeleton by weight. In osteosarcoma tumours of long bones of lower limb were markedly over-represented in the adolescent peak, being six times more than at any other site. Variation in incidence patterns with age and site suggests pubertal bone growth to be a key factor in osteosarcoma while different biological pathways could be relevant for Ewing sarcoma.

Comparative incidence patterns and trends of gonadal and extragonadal germ cell tumors in England, 1979 to 2003.

BACKGROUND: It is believed that gonadal and extragonadal germ cell tumors (GCTs) arise from primordial germ cells and may have similar etiopathogenesis. Unlike testicular GCTs, there has been limited comprehensive population-based analysis of ovarian and extragonadal GCTs. METHODS: All malignant GCTs and all central nervous system (CNS) GCTs with benign and uncertain behavior that were registered in England in the age group 0 to 84 years from 1979 to 2003 were included in the current study. Incidence rates were calculated and adjusted to the world standard population. RESULTS: There were 33,364 GCTs (92.5% testes, 3.9% ovary, 3.2% extragonadal) in individuals aged 0 to 84 years. The CNS was the most common extragonadal site. An initial peak in incidence at ages 0 to 4 years of nongerminomas was observed at all sites except ovary. Second incidence peaks between ages 10 to 39 years that were more marked among males also were observed at all sites. The ages at these incidence peaks varied by site and were 10 to 14 years (CNS), 15 to 19 years (ovary), 25 to 29 years (other extragonadal sites), and 30 to 34 years (testes). A statistically significant increase in incidence over time was observed in germinomas (testes, CNS) and nongerminomas (testes, ovary). CONCLUSIONS: The age-incidence patterns observed suggested a common initiation of GCTs in embryonic/fetal life with variable rates of tumor progression as a result of subsequent events that may be site specific. The authors concluded that future genetic studies should consider GCTs from all sites to enable a better understanding of their etiology.

Cancer at ages 15-29 years: the contrasting incidence in India and England.

BACKGROUND: There has been a steady increase in published research from Europe and North America on the epidemiology of cancers in young people. There are limited data from the developing world. We contrast the incidence of cancer at ages 15-29 years in India and England. PROCEDURE: Malignant neoplasms in those aged 15-29 years registered during 2001-2003 in five urban population-based cancer registries (PBCRs) of India and in eight PBCRs in England were included. Site-based classification was used. Age-standardized incidence rates were expressed per 100,000 person years. RESULTS: In India, 4,864 (5.8%) of 84,450 cases and in England, 8,137 (1.2%) of 65,6752 cancer cases occurred in those aged 15-29 years. For this age group, the incidence rate for males and females in India were 12.91 and 14.19, and in England were 27.75 and 28.88, respectively. In males aged 15-29 years, the three most common cancers in India were leukemia, lymphoma, and central nervous system tumors and in England were cancers of male genital organs, lymphoma, and leukemia. Cancers of female genital organs, breast, and leukemia were most common in females in India and cancers of female genital organs, lymphoma, and melanoma in England. For cancers of mouth, stomach, and gall bladder, the incidence was higher in India. CONCLUSION: Incidence of cancer at ages 15-29 years in England is higher at most sites than in India. Variation in environmental exposures between the two countries might be an explanation. Under-ascertainment of cases and gender bias in seeking healthcare may also influence reported incidence rates in India.