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Objective To compare prevalence estimates obtained by the ADDM cerebral palsy surveillance method to other administrative or diagnostic indications of cerebral palsy. Introduction Cerebral Palsy (CP) is the most common cause of motor disability in children. CP registries often rely on administrative data such as CP diagnoses or International Classification of Diseases (ICD) codes indicative of CP. However, little is known about the validity of these indicators. We calculated sensitivity, specificity, positive and negative predictive values of CP ICD-9 codes and CP diagnoses compared to a “gold standard” CP classification based on detailed medical and education record review. Methods This sample includes 50,332 8-year-olds living in four US sites (32 counties in Alabama, 5 counties in Georgia, 10 counties in Wisconsin, and 5 counties in Missouri) in 2006, 2008, and 2010. The Autism and Developmental Disabilities Monitoring (ADDM) Network reviewed medical and education records for these children as part of the US Centers for Disease Control and Prevention population-based surveillance of developmental disabilities. All of these children received special education services or were assigned one or more ICD-9 codes associated with a variety of developmental disabilities by community medical providers. Medical and education records were reviewed by trained staff; if the records contained CP diagnoses or motor findings indicative of CP, detailed clinical information was abstracted for additional review by trained clinicians who determined whether the child met the CP case definition based on all information available. Abstracted records were also reviewed for evidence of known motor disorders or genetic conditions that disqualified a child from being a CP case, such as inborn error of metabolism or muscular dystrophy. Trained clinicians reviewed and excluded children with confirmed disqualifying conditions. We calculated CP prevalence, sensitivity, specificity, and positive and negative predictive values for three different methods used to identify cases, using the ADDM surveillance case identification as the gold standard. These methods include: 1) ICD-9 codes for CP (342–344); 2) a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions, and 3) both ICD-9 codes (342–344) and a CP diagnosis written in the medical or education records, excluding children with disqualifying conditions. In an attempt to avoid requiring record review for method 1, we considered using ICD-9 codes for disqualifying conditions. However, we found that ICD codes for these conditions did not correlate well with disqualifying conditions identified in medical record reviews; therefore disqualifying conditions were not considered for method 1. Methods 2 and 3 did require review of medical records for disqualifying conditions and for a written CP diagnosis, but overall were less extensive than traditional ADDM surveillance methods. In order to determine the impact of different classification criteria on how and which children are captured by surveillance methods, we compared demographic and other characteristics of all children who met the ADDM surveillance case definition. We compared children who would and would not be classified as CP cases using method 3. Results Out of the total 50,332 children, 1294 met the ADDM surveillance case definition, 2201 had CP ICD codes (method 1), 1502 had a written CP diagnosis and no disqualifying conditions (method 2), and 1345 had both CP ICD codes and a written diagnosis and no disqualifying conditions (method 3). Each study year, between 32—48% of abstracted children were excluded due to disqualifying conditions found in medical records. The ADDM network gold standard CP prevalence was 3.3 per 1000 in 2006, 3.1 per 1000 in 2008, and 2.9 per 1000 in 2010. For method 1, sensitivity was 90.0%, specificity was 97.4%, positive predictive value was 51.6% and negative predictive value was 99.7%. Method 1 prevalence estimates were 5.3 per 1000 in 2006, 4.6 per 1000 in 2008, and 4.6 per 1000 in 2010. For method 2, sensitivity was 98.1%, specificity was 88.4%, PPV was 84.5% and NPV was 98.4% compared to the ADDM Network definition. Method 2 estimated prevalence was 3.9 per 1000 for 2006, 3.6 per 1000 for 2008, and 3.2 per 1000 for 2010. For method 3, sensitivity was 89.6%, specificity was 99.5%, PPV was 84.3% and NPV was 99.7%. Method 3 estimated prevalence was 3.5 per 1000 for 2006, 3.2 per 1000 for 2008, and 2.8 per 1000 for 2010. Using Pearson’s Chi-Square tests, we compared demographic and other characteristics of ADDM Network CP case children who also met method 3 case definition (n = 1134) and children who met the ADDM Network CP definition but not method 3 case definition (n = 160). Demographic information was not different between these children. ADDM Network CP case children who did not meet method 3 criteria were significantly less likely to require a wheelchair for mobility than children who met method 3 criteria (4.4% versus 27.4%, p < .05). Conclusions Relying on ICD-9 codes without excluding disqualifying conditions to identify CP cases (method 1) resulted in high sensitivity (90%), but low positive predictive value as well as an overestimated CP prevalence when compared with the ADDM Network method. Use of a written diagnosis and excluding disqualifying conditions (method 2) resulted in very high sensitivity (98%), with fewer false positives but overestimated CP prevalence compared to the ADDM estimate. In contrast, using both CP ICD codes and a written CP diagnosis and excluding disqualifying conditions (method 3) yielded prevalence estimates similar to ADDM Network CP estimates; this approach also had high sensitivity, specificity, and PPV. Methods 2 and 3 still require manual record review, unlike method 1. For method 2, reviewers would need to review all records for CP and disqualifying conditions. Method 3 only requires review of records with CP ICD codes, comprising 4% of all records currently reviewed. Method 3 would fail to capture children without both a written diagnosis and ICD codes; and this approach may be less sensitive for detecting CP among children with less severe motor impairment than using the gold standard. Using ICD codes and written CP diagnoses contained in medical and education records combined with a limited medical record review to identify disqualifying conditions could lower operational costs of CP surveillance while preserving accurate prevalence estimates compared with the more labor-intensive processes currently used. Further evaluation is needed to determine if improvements in efficiency are worth potential trade-offs in the data collected by the system. Of particular importance is whether the approach could capture all the necessary indicators that are important to stakeholders. Additional analyses would also need to evaluate whether the surveillance methods affect other findings, such as previously observed disparities, co-occurring conditions, or CP severity.

ObjectiveTo compare associations between interpregnancy intervals (IPIs) and adverse perinatal outcomes in deliveries following IVF with deliveries following spontaneous conception or other (non-IVF) fertility treatments.DesignCohort using linked birth certificate and assisted reproductive technology surveillance data from Massachusetts and Michigan.SettingNot applicable.Patient(s)1,225,718 deliveries.Intervention(s)None.Main outcomes measure(s)We assessed associations between IPI and preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) according to live birth or nonlive pregnancy outcome in the previous pregnancy.Result(s)In IVF deliveries following previous live birth, risk of PTB was 22.2% for IPI 12 to

NBDPN Standards Assessment Tool on Data Quality. (DOCX 215 kb)

Purpose: To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods: The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results: The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero -inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions: Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. (C) 2016 Elsevier Inc. All rights reserved. Support from the National Institutes of Health is acknowledged (award number R01CA172805). Support from the IAP Research Network, P7/06, of the Belgian State (Belgian Science Policy) is gratefully acknowledged.

It is our primary focus to study the spatial distribution of disease incidence at different geographical levels. Often, spatial data are available in the form of aggregation at multiple scale levels such as census tract, county, and state. When data are aggregated from a fine (e.g., county) to a coarse (e.g., state) geographical level, there will be loss of information. The problem is more challenging when excessive zeros are available at the fine level. After data aggregation, the excessive zeros at the fine level will be reduced at the coarse level. If we ignore the zero inflation and the aggregation effect, we could get inconsistent risk estimates at the fine and coarse levels. Hence, in this paper, we address those problems using zero-inflated multiscale models that jointly describe the risk variations at different geographical levels. For the excessive zeros at the fine level, we use a zero-inflated convolution model, whereas we consider a regular convolution model for the smoothed data at the coarse level. These methods provide a consistent risk estimate at the fine and coarse levels when high percentages of structural zeros are present in the data. National Institute for Health Research [R01CA172805]; National Institutes of Health [R01CA172805]

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.