A comparison of cohorts of children with cerebral palsy from a population register and hospital admission data: A data linkage study.

BACKGROUND: Administrative health data, such as hospital admission data, are often used in research to identify children/young people with cerebral palsy (CP). OBJECTIVES: To compare sociodemographic, clinical details and mortality of children/young people identified as having CP in either a CP population registry or hospital admission data. METHODS: We identified two cohorts of children/young people (birth years 2001-2010, age at study end or death 2 months to 19 years 6 months) with a diagnosis of CP from either (i) the New South Wales (NSW)/Australian Capital Territory (ACT) CP Register or (ii) NSW hospital admission data (2001-2020). Using record linkage, these data sources were linked to each other and NSW Death, Perinatal, and Disability datasets. We determined the sensitivity and positive predictive value (PPV) of CP diagnosis in hospital admission data compared with the NSW/ACT CP Register (gold standard). We then compared the sociodemographic and clinical characteristics and mortality of the two cohorts available through record linkage using standardised mean difference (SMD). RESULTS: There were 1598 children/young people with CP in the NSW/ACT CP Register and 732-2439 children/young people with CP in hospital admission data, depending on the case definition used. The sensitivity of hospital admission data for diagnosis of CP ranged from 0.40-0.74 and PPV 0.47-0.73. Compared with children/young people with CP identified in the NSW/ACT CP Register, a greater proportion of those identified in hospital admission data (one or more admissions with G80 case definition) were older, lived in major cities, had comorbidities including epilepsy, gastrostomy use, intellectual disability and autism, and died during the study period (SMD > 0.1). CONCLUSIONS: Sociodemographic and clinical characteristics differ between cohorts of children/young people with CP identified using a CP register or hospital admission data. Those identified in hospital admission data have higher rates of comorbidities and death, suggesting some may have progressive conditions and not CP. These differences should be considered when planning and interpreting research using various data sources.

[Analysis of Hospital Care Registers due to Silicosis in Spain, 1997-2020]. / Análisis de los procesos asistenciales hospitalarios por silicosis en España, 1997-2020.

OBJECTIVE: The re-emergence of silicosis in Spain since 2007 has been identified by the increase in the number of occupational disease reports. The aim of our study was to analyse the silicosis care processes attended by the National Health System between 1997 and 2020 to better understand the epidemiological dimension of the problem. METHODS: Processes were obtained from the Registro de Actividad Sanitaria Especializada (RAE-CMBD), with ICD-9-CM codes 500 and 502 (1997-2016) and ICD-10-CM J60, J62.0 and J62.8 (2017-2020). Descriptive statistical methods and modelling by logistic regression and Joinpoint regression methodology were applied. RESULTS: A total of 111,325 records were obtained (ages twenty-one hundred years), 4.3% for silicosis as the main diagnosis (PD) and 95.7% as a secondary diagnosis (SD). Men accounted for 98% and women for 2%. The mean age for SD processes was 75.1, and 68.7 for PD processes. The median age increased by eight years for SD and decreased by three years for PD. Although the overall burden of care decreased, under-fifty PD procedures between 2006 and 2009 showed an upward trend (APC=27.01%). SD processes showed a non-significant upward trend (APC=1.92%) between 2005 and 2020. CONCLUSIONS: The upward trend in silicosis care processes in people under fifty years of age since 2005 confirms the healthcare impact of the re-emergence of silicosis in Spain. The associated burden of care constitutes a present and future public health problem given the decreasing age of those affected.

OBJECTIVE: La remergencia de la silicosis en España desde 2007 ha sido objetivada por el incremento de partes de enfermedad profesional. El objetivo de nuestro estudio fue analizar los procesos asistenciales por silicosis atendidos por el Sistema Nacional de Salud entre 1997 y 2020 para una mejor comprensión de la dimensión epidemiológica del problema. METHODS: Se empleó el RAE-CMBD, aplicando los códigos CIE-9-CM 500 y 502 (1997-2016) y CIE-10-CM J60, J62.0 y J62.8 (2017-2020). Se aplicaron métodos de estadística descriptiva y modelización por regresiones logísticas y metodología de regresión Joinpoint. RESULTS: Se obtuvieron 111.325 registros (veinte-cien años), el 4,3% por silicosis como diagnóstico principal (DP) y el 95,7% como diagnóstico secundario (DS). El 98% eran hombres y el 2% mujeres. La edad media de los procesos por DS fue de 75,1, y de 68,7 para los procesos por DP. La mediana de edad aumentó ocho años para los DS y disminuyó tres para los DP. Aunque la carga asistencial global disminuyó, los procesos en menores de cincuenta años por DP entre 2006 y 2009 registraron una tendencia ascendente (APC=27,01%). Los procesos por DS mostraron una tendencia ascendente no significativa (APC=1,92%) entre 2005 y 2020. CONCLUSIONS: La tendencia al crecimiento de los procesos asistenciales por silicosis en menores de cincuenta años desde 2005 confirma el impacto asistencial de la remergencia de la silicosis en España. La carga asistencial asociada constituye un problema de Salud Pública presente y futuro dada la reducción de edad de los afectados.

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.

Identifying traffic accident black spots with Poisson-Tweedie models.

This paper aims at the identification of black spots for traffic accidents, i.e. locations with accident counts beyond what is usual for similar locations, using spatially and temporally aggregated hospital records from Funen, Denmark. Specifically, we apply an autoregressive Poisson-Tweedie model, which covers a wide range of discrete distributions and handles zero-inflation as well as overdispersion. The estimated power parameter of the model was 1.6 (SE=0.06) suggesting a distribution close to the Pólya-Aeppli distribution. We identified nine black spots consistently standing out in all six considered calendar years and calculated by simulations a probability of p=0.03 for these to be chance findings. Altogether, our results recommend these sites for further investigation and suggest that our simple approach could play a role in future area based traffic accident prevention planning.

Validation of administrative hospital data for identifying incident pancreatic and periampullary cancer cases: a population-based study using linked cancer registry and administrative hospital data in New South Wales, Australia.

OBJECTIVES: Informing cancer service delivery with timely and accurate data is essential to cancer control activities and health system monitoring. This study aimed to assess the validity of ascertaining incident cases and resection use for pancreatic and periampullary cancers from linked administrative hospital data, compared with data from a cancer registry (the 'gold standard'). DESIGN, SETTING AND PARTICIPANTS: Analysis of linked statutory population-based cancer registry data and administrative hospital data for adults (aged ≥18 years) with a pancreatic or periampullary cancer case diagnosed during 2005-2009 or a hospital admission for these cancers between 2005 and 2013 in New South Wales, Australia. METHODS: The sensitivity and positive predictive value (PPV) of pancreatic and periampullary cancer case ascertainment from hospital admission data were calculated for the 2005-2009 period through comparison with registry data. We examined the effect of the look-back period to distinguish incident cancer cases from prevalent cancer cases from hospital admission data using 2009 and 2013 as index years. RESULTS: Sensitivity of case ascertainment from the hospital data was 87.5% (4322/4939), with higher sensitivity when the cancer was resected (97.9%, 715/730) and for pancreatic cancers (88.6%, 3733/4211). Sensitivity was lower in regional (83.3%) and remote (85.7%) areas, particularly in areas with interstate outflow of patients for treatment, and for cases notified to the registry by death certificate only (9.6%). The PPV for the identification of incident cases was 82.0% (4322/5272). A 2-year look-back period distinguished the majority (98%) of incident cases from prevalent cases in linked hospital data. CONCLUSIONS: Pancreatic and periampullary cancer cases and resection use can be ascertained from linked hospital admission data with sufficient validity for informing aspects of health service delivery and system-level monitoring. Limited tumour clinical information and variation in case ascertainment across population subgroups are limitations of hospital-derived cancer incidence data when compared with population cancer registries.