Creating a national register of childhood type 1 diabetes using routinely collected hospital data

Hodgson, Susan; Beale, Linda; Parslow, Roger C; Feltbower, Richard; Järup, Lars

doi:10.1111/j.1399-5448.2011.00815.x

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…Only five conditions were investigated in more than one study: diabetes (10.6%) [13-16], inflammatory bowel disease (5.4%) [17,18], asthma (5.4%) [19,20], rotavirus infection (5.4%) [21,22], and tuberculosis (5.4%) [23,24]. However, a diverse range of conditions were investigated in single studies, including obesity [25], vaccine-related illness [26], injuries [27], autism [28], febrile neutropenia in oncology patients [29], high risk conditions [30], dermatologic conditions [31-33], congenital anomalies [34], cardiac defects [35], respiratory illnesses excluding asthma [36-38], neurologic conditions [39], other gastrointestinal conditions [40-43], genitourinary conditions [44,45], serum sickness [46], thrombosis [47], maternal/perinatal conditions [48], and drug-related anaphylaxis [49].…”

Section: Resultsmentioning

confidence: 99%

Validation of administrative health data for the pediatric population: a scoping review

Shiff

Jama

Boden

et al. 2014

BMC Health Serv Res

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BackgroundThe purpose of this research was to perform a scoping review of published literature on the validity of administrative health data for ascertaining health conditions in the pediatric population (≤20 years).MethodsA comprehensive search of OVID Medline (1946 - present), CINAHL (1937 - present) and EMBASE (1947 - present) was conducted. Characteristics of validation studies that were abstracted included the study population, health condition, topic of the validation (e.g., single diagnosis code versus case-finding algorithm), administrative and validation data sources. Inter-rater agreement was measured using Cohen’s κ. Extracted data were analyzed using descriptive statistics.ResultsA total of 37 articles met the study inclusion criteria. Cohen’s κ for study inclusion/exclusion and data abstraction was 0.88 and 0.97, respectively. Most studies validated administrative data from the USA (43.2%) and Canada (24.3%), and focused on inpatient records (67.6%). Case-finding algorithms (56.7%) were more frequently validated than diagnoses codes alone (37.8%). Five conditions were validated in more than one study: diabetes mellitus, inflammatory bowel disease, asthma, rotavirus infection, and tuberculosis.ConclusionsThis scoping review identified a number of gaps in the validation of administrative health data for pediatric populations, including limited investigation of outpatient populations and older pediatric age groups.

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Section: Resultsmentioning

confidence: 99%

Validation of administrative health data for the pediatric population: a scoping review

Shiff

Jama

Boden

et al. 2014

BMC Health Serv Res

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“…As highlighted by a study of Hodgson et al in the UK, despite the fact that HDR represents a useful instrument in exploring the epidemiology of diabetes, it is crucial to establish dedicated diabetes registries. A diabetes registry could incorporate additional data for undertaking etiological research into this important childhood condition [ 38 ]. Although the Apulian RCOD has shown a level of clinical documentation completeness >90%, a sensitivity of 39% is still low to ensure reliable epidemiological data, firstly because of the limited number of physicians involved in the activities.…”

Section: Discussionmentioning

confidence: 99%

Incidence of Type 1 Diabetes among Children and Adolescents in Italy between 2009 and 2013: The Role of a Regional Childhood Diabetes Registry

Fortunato

Cappelli

Vece

et al. 2016

Journal of Diabetes Research

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Background. Surveillance represents a key strategy to control type 1 diabetes mellitus (T1DM). In Italy, national data are missing. This study aimed at evaluating the incidence of T1DM in subjects <18 year olds in Apulia (a large southeastern region, about 4,000,000 inhabitants) and assessing the sensitivity of the regional Registry of Childhood-Onset Diabetes (RCOD) in the 2009–2013 period. Methods. We performed a retrospective study matching records from regional Hospital Discharge Registry (HDR), User Fee Exempt Registry (UFER), and Drugs Prescription Registry (DPR) and calculated T1DM incidence; completeness of each data source was also estimated. In order to assess the RCOD sensitivity we compared cases from the registry to those extracted from HDR-UFER-DPR matching. Results. During 2009–2013, a total of 917 cases (about 184/year) in at least one of the three sources and an annual incidence of 25.2 per 100,000 were recorded, lower in infant, increasing with age and peaked in 5- to 9-year-olds. The completeness of DPR was 78.7%, higher than that of UFER (64.3%) and of HDR (59.6%). The RCOD's sensitivity was 39.05% (360/922; 95% CI: 34.01%–44.09%). Conclusions. Apulia appeared as a high-incidence region. A full, active involvement of physicians working in paediatric diabetes clinics would be desirable to improve the RCOD performance.

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“…children treated outside the NHS setting. We have shown previously that HES-based data show good concordance with well-ascertained regional register data, especially from the year 2000 onwards, and for children aged 0-9 years [14], which influenced our choice of time period and age groups for this study. Type 1 diabetes is a heterogeneous disease [1,49], and environmental triggers may differ in those with early onset vs those who present with diabetes at an older age.…”

Section: Strengths and Limitationsmentioning

confidence: 72%

“…We identified all inpatient admissions for children aged 0-9 years with a primary diagnosis of type 1 or type unknown diabetes (ICD-9 codes: 250X1, 250X3, 250X, 250X9 [www. icd9data.com/2007/Volume1]; ICD-10 codes: E10X, E12X, E13X, E14X [http://apps.who.int/classifications/icd10/ browse/2016/en]) admitted between 1 January 1992 and 31 March 2011, to create a dataset of incident diabetes during the period 1 April 2000 to 31 March 2011 based on first admission for each unique patient [14]. Readmissions were excluded on the basis of the unique patient identifier (HES-ID) and for those with missing HES-ID (predominantly affecting admissions prior to 1997), from unique combinations of date of birth, sex and pseudonymised postcode.…”

Section: Health Datamentioning

confidence: 99%

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Childhood type 1 diabetes: an environment-wide association study across England

et al. 2020

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Aims/hypothesis Type 1 diabetes is an autoimmune disease affecting~400,000 people across the UK. It is likely that environmental factors trigger the disease process in genetically susceptible individuals. We assessed the associations between a wide range of environmental factors and childhood type 1 diabetes incidence in England, using an agnostic, ecological environmentwide association study (EnWAS) approach, to generate hypotheses about environmental triggers. Methods We undertook analyses at the local authority district (LAD) level using a national hospital episode statistics-based incident type 1 diabetes dataset comprising 13,948 individuals with diabetes aged 0-9 years over the period April 2000 to March 2011. We compiled LAD level estimates for a range of potential demographic and environmental risk factors including meteorological, land use and environmental pollution variables. The associations between type 1 diabetes incidence and risk factors were assessed via Poisson regression, disease mapping and ecological regression. Results Case counts by LAD varied from 1 to 236 (median 33, interquartile range 24-46). Overall type 1 diabetes incidence was 21.2 (95% CI 20.9, 21.6) per 100,000 individuals. The EnWAS and disease mapping indicated that 15 out of 53 demographic and environmental risk factors were significantly associated with diabetes incidence, after adjusting for multiple testing. These included air pollutants (particulate matter, nitrogen dioxide, nitrogen oxides, carbon monoxide; all inversely associated), as well as lead in soil, radon, outdoor light at night, overcrowding, population density and ethnicity. Disease mapping revealed spatial heterogeneity in type 1 diabetes risk. The ecological regression found an association between type 1 diabetes and the living environment domain of the Index of Multiple Deprivation (RR 0.995; 95% credible interval [CrI] 0.991, 0.998) and radon potential class (RR 1.044; 95% CrI 1.015, 1.074). Conclusions/interpretation Our analysis identifies a range of demographic and environmental factors associated with type 1 diabetes in children in England.

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Creating a national register of childhood type 1 diabetes using routinely collected hospital data

Abstract: ABSTRACT:Introduction: There is no national register of childhood type 1 diabetes mellitus for England. Our

Cited by 8 publications

References 34 publications

Validation of administrative health data for the pediatric population: a scoping review

Validation of administrative health data for the pediatric population: a scoping review

Incidence of Type 1 Diabetes among Children and Adolescents in Italy between 2009 and 2013: The Role of a Regional Childhood Diabetes Registry

Childhood type 1 diabetes: an environment-wide association study across England

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