Obtaining numerically consistent estimates from a mix of administrative data and surveys

National statistical institutes (NSIs) fulfil an important role as providers of objective and undisputed statistical information on many different aspects of society. To this end NSIs try to construct data sets that are rich in information content and that can be used to estimate a large variety of population figures. At the same time NSIs aim to construct these rich data sets as efficiently and cost effectively as possible. This can be achieved by utilizing already available administrative data as much as possible, and supplementing these administrative data with survey data collected by the NSI. In this paper we focus on one of the challenges when using a mix of administrative data sets and surveys, namely obtaining numerically consistent population estimates. We will sketch general approaches based on weighting, imputation and macro-integration, and discuss their advantages and drawbacks

Processing of Erroneous and Unsafe Data

Statistical offices have to overcome many problems before they can publish reliable data. Two of these problems are examined in this thesis. The first problem is the occurrence of errors in the collected data. Due to these errors publication figures cannot be directly based on the collected data. Before publication the errors in the data have to be localised and corrected. In this thesis we focus on the localisation of errors in a mix of categorical and numerical data. The problem is formulated as a mathematical optimisation problem. Several new algorithms for solving this problem are proposed, and computational results of the most promising algorithms are compared to each other. The second problem that is examined in this thesis is the occurrence of unsafe data, i.e. data that would reveal too much sensitive information about individual respondents. Before publication of data, such unsafe data need to be protected. In the thesis we examine various aspects of the protection of unsafe data.Statistische bureaus dienen tal van problemen te overwinnen voordat zij de resultaten van hun onderzoeken kunnen publiceren. In het proefschrift wordt ingegaan op twee van deze problemen. Het eerste probleem is dat verzamelde gegevens foutief kunnen zijn. Door de mogelijke aanwezigheid van fouten in de gegevens moeten deze gegevens eerst worden gecontroleerd en indien nodig worden gecorrigeerd voordat tot publicatie van resultaten wordt overgegaan. In het proefschrift wordt vooral aandacht besteed aan het opsporen van de foutieve gegevens. Door te veronderstellen dat er zo min mogelijk fouten zijn gemaakt kan het opsporen van de foutieve waarden als een wiskundig optimaliseringsprobleem worden geformuleerd. In het proefschrift wordt een aantal methoden ontwikkeld om dit complexe probleem efficient op te lossen. Het tweede probleem dat in het proefschrift onderzocht wordt is dat geen gegevens gepubliceerd mogen worden die de privacy van individuele respondenten of kleine groepen respondenten schaden. Om gegevens van individuele of kleine groepen respondenten te beschermen moeten beveiligingsmaatregelen, zoals het niet publiceren van bepaalde informatie, worden getroffen. In het proefschrift wordt ingegaan op de wiskundige problemen die het beveiligen van gevoelige gegevens met zich mee brengt. Voor een aantal problemen, zoals het berekenen van het informatieverlies ten gevolge van het beveiligen van gevoelige gegevens en het minimaliseren van de informatie die niet gepubliceerd wordt, worden oplossingen beschreven

Estimating classification error under edit restrictions in combined survey-register data using Multiple Imputation Latent Class modelling (MILC)

Both registers and surveys can contain classication errors. These errors can be estimated by making use of information that is obtained when making use of a combined dataset. We propose a new method based on latent class modelling that estimates the number of classification errors in the multiple sources, and simultaneously takes impossible combinations with other variables into account. Furthermore, we use the latent class model to multiply impute a new variable, which enhances the quality of statistics based on the combined dataset. The performance of this method is investigated by a simulation study, which shows that whether the method can be applied depends on the entropy of the LC model and the type of analysis a researcher is planning to do. Furthermore, the method is applied to a combined dataset from Statistics Netherlands

Novel Leptin Receptor Mutations Identified in Two Girls with Severe Obesity Are Associated with Increased Bone Mineral Density

Background: Recessive mutations in the leptin receptor (LEPR) are a rare cause of hyperphagia and severe early-onset obesity. To date, the phenotype has only been described in 25 obese children, some of whom also had altered immune function, hypogonadotropic hypogonadism, reduced growth hormone secretion, hypothalamic hypothyroidism or reduced adult height. We provide a detailed description of the phenotype of 2 affected girls to add to this knowledge. Methods: Whole-exome sequencing and targeted sequencing were used to detect the LEPR mutations. RNA analysis was performed to assess the effect of splice-site mutations. Results: In 2 unrelated girls with severe obesity, three novel LEPR mutations were detected. Longitudinal growth data show normal childhood growth, and in the older girl, a normal adult height despite hypogonadotropic hypogonadism and the lack of an obvious pubertal growth spurt. Bone age is remarkably advanced in the younger (prepubertal) girl, and bone mineral density (BMD) is high in both girls, which might be directly or indirectly related to leptin resistance. Conclusion: The spectrum of clinical features of LEPR deficiency may be expanded with increased BMD. Future observations in LEPR-deficient subjects should help further unravel the role of leptin in human bone biology

Prolonged Influenza Virus Shedding and Emergence of Antiviral Resistance in Immunocompromised Patients and Ferrets

Immunocompromised individuals tend to suffer from influenza longer with more serious complications than otherwise healthy patients. Little is known about the impact of prolonged infection and the efficacy of antiviral therapy in these patients. Among all 189 influenza A virus infected immunocompromised patients admitted to ErasmusMC, 71 were hospitalized, since the start of the 2009 H1N1 pandemic. We identified 11 (15%) cases with prolonged 2009 pandemic virus replication (longer than 14 days), despite antiviral therapy. In 5 out of these 11 (45%) cases oseltamivir resistant H275Y viruses emerged. Given the inherent difficulties in studying antiviral efficacy in immunocompromised patients, we have infected immunocompromised ferrets with either wild-type, or oseltamivir-resistant (H275Y) 2009 pandemic virus. All ferrets showed prolonged virus shedding. In wild-type virus infected animals treated with oseltamivir, H275Y resistant variants emerged within a week after infection. Unexpectedly, oseltamivir therapy still proved to be partially protective in animals infected with resistant virus. Immunocompromised ferrets offer an attractive alternative to study efficacy of novel antiviral therapies

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar