RESUMEN
For centuries, the study of traumatic brain injury (TBI) has been centred on historical observation and analyses of personal, social, and environmental processes, which have been examined separately. Today, computation implementation and vast patient data repositories can enable a concurrent analysis of personal, social, and environmental processes, providing insight into changes in health status transitions over time. We applied computational and data visualization techniques to categorize decade-long health records of 235,003 patients with TBI in Canada, from preceding injury to the injury event itself. Our results highlighted that health status transition patterns in TBI emerged along with the projection of comorbidity where many disorders, social and environmental adversities preceding injury are reflected in external causes of injury and injury severity. The strongest associations between health status preceding TBI and health status at the injury event were between multiple body system pathology and advanced age-related brain pathology networks. The interwoven aspects of health status on a time continuum can influence post-injury trajectories and should be considered in TBI risk analysis to improve prevention, diagnosis, and care.
Asunto(s)
Lesiones Traumáticas del Encéfalo , Lesiones Encefálicas , Lesiones Traumáticas del Encéfalo/epidemiología , Canadá/epidemiología , Comorbilidad , Estado de Salud , HumanosRESUMEN
BACKGROUND: The interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods. METHODS: We drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms. RESULTS: Based on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated. CONCLUSIONS: The study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.
Asunto(s)
Lesiones Traumáticas del Encéfalo , Visualización de Datos , Lesiones Traumáticas del Encéfalo/terapia , Minería de Datos , Femenino , Humanos , Masculino , Ontario/epidemiologíaRESUMEN
OBJECTIVE: To understand how pre-injury health status present five-years preceding traumatic brain injury (TBI) affects direct medical cost two years post-injury. METHODS: Patients age ≥19 years in the emergency department (ED) or acute care for a TBI between April 1, 2007 and March 31, 2014 in Ontario, Canada (N = 55,669) were identified from population-based health administrative data. Forty-three factors of pre-injury health status (i.e., comorbidities and personal, social, and environmental factors) that were internally validated for the TBI population were assessed in this study. The outcome of interest was direct medical cost within two years of discharge. Sex-specific multivariable linear regressions were conducted to understand the associations between direct medical cost within two years of discharge and pre-injury health status. RESULTS: Patients who received care in the ED (81.9% of total sample) incurred a median cost of $2,492/male patient (average $12,342/patient) and $3,508/female patient (average $65,285/patient) within two years of injury; 37 pre-injury factors were significantly associated with increased direct medical costs. Patients who first received care for their TBI in acute care (18.1%) incurred a median cost of $25,081/male patient (average $63,060/patient) and $30,277/female patient (average $65,285/patient) within two years of injury; 21 factors were significantly associated with increased direct medical costs. Among more prevalent factors, those associated with increased medical cost by at least 50% included mental health disorders, substance abuse, disorders or medical conditions frequently observed among the elderly, cardiovascular disorders, stroke and emergencies involving the brain, metabolic disorders and abdominal symptoms, conditions and symptoms of abdomen and pelvis, genitourinary disorders and disorders of prostate, and pulmonary abdominal and other emergencies. CONCLUSIONS: Direct medical costs two years post-TBI differed significantly between patients with and without adverse pre-existing health status. Interdisciplinary teams to promote early identification of pre-existing health conditions and appropriate management and integration of these conditions in TBI care across the continuum of healthcare may be opportunities to reduce direct medical costs post-injury.
Asunto(s)
Lesiones Traumáticas del Encéfalo/economía , Estado de Salud , Adulto , Anciano , Anciano de 80 o más Años , Lesiones Traumáticas del Encéfalo/epidemiología , Costo de Enfermedad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Ontario/epidemiología , Factores SexualesRESUMEN
OBJECTIVES: To understand how health status preceding traumatic brain injury (TBI) affects relative functional gain after inpatient rehabilitation using a data mining approach. DESIGN: Population-based, sex-stratified, retrospective cohort study using health administrative data from Ontario, Canada (39% of the Canadian population). SETTING: Inpatient rehabilitation. PARTICIPANTS: Patients 14 years or older (N=5802; 63.4% male) admitted to inpatient rehabilitation within 1 year of a TBI-related acute care discharge between April 1, 2008, and March 31, 2015. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Relative functional gain (RFG) in percentage, calculated as ([discharge FIM-admission FIM]/[126-admission FIM]×100). Health status prior to TBI was identified and internally validated using a data mining approach that categorized all International Classification of Diseases, 10th revision, codes for each patient. RESULTS: The average RFG was 52.8%±27.6% among male patients and 51.6%±27.1% among female patients. Sex-specific Bonferroni adjusted multivariable linear regressions identified 10 factors of preinjury health status related to neurology, emergency medicine, cardiology, psychiatry, geriatrics, and gastroenterology that were significantly associated with reduced RFG in FIM for male patients. Only 1 preinjury health status category, geriatrics, was significantly associated with RFG in female patients. CONCLUSIONS: Comorbid health conditions present up to 5 years preceding the TBI event were significantly associated with RFG. These findings should be considered when planning and executing interventions to maximize functional gain and to support an interdisciplinary approach. Best practices guidelines and clinical interventions for older male and female patients with TBI should be developed given the increasingly aging population with TBI.
Asunto(s)
Lesiones Traumáticas del Encéfalo/rehabilitación , Minería de Datos/métodos , Estado de Salud , Recuperación de la Función , Centros de Rehabilitación/estadística & datos numéricos , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Cognición , Comorbilidad , Femenino , Humanos , Pacientes Internos , Masculino , Persona de Mediana Edad , Ontario , Alta del Paciente , Estudios Retrospectivos , Factores Sexuales , Índices de Gravedad del TraumaRESUMEN
In this paper, we present a method to assess differences between microbiome communities that effectively models sparse count data and accounts for presence-absence bias frequently encountered when zeros are present. We assume that the observed data for each operational taxonomic unit is Poisson generated with the rate for each sample originating from an underlying rate distribution. We propose to model this distribution using a mixture model, specifying the components based on the posterior rate distribution of a count and estimating the optimal weights using a least squares objective function. The distribution incorporates varying resolutions of samples, a point mass for differentiating structural and nonstructural zeros, and a truncation point mass to account for high values that are too sparse to model. As mixture component specification is not always straightforward, a method to estimate a joint model from several mixture distributions using minimum distances of bootstrap iterates is proposed. Once the population rate distribution is approximated, we obtain sample-specific distributions by conditioning on the observed operational taxonomic unit count, resolution, and estimated mixture distribution and then use these to estimate pairwise distances for a permutation test. The method gives an accurate estimate of the true proportion of zeros for presence-absence, effectively models the distribution of low counts using the mixture distribution, and achieves good power for detecting differences in a variety of scenarios. The method is tested using a simulation study and applied to two microbiome datasets. In each case, the results are compared with a number of existing methods.
Asunto(s)
Bacterias/clasificación , Microbiota , Estadística como Asunto , Sesgo , Humanos , Análisis de los Mínimos Cuadrados , Modelos Estadísticos , Distribución de PoissonRESUMEN
BACKGROUND: Traumatic brain injury (TBI) is a primary cause of injury mortality in developed countries but less is known about the impact of TBI on postacute mortality in large study populations. This study investigates the rate and predictors of postacute mortality (1-9 years after the initial injury) of severely injured persons with TBI in the Province of Ontario from April 1, 1993 to March 31, 1995. METHOD: Cases were identified (n = 2,721) from the Ontario Trauma Registry Comprehensive Data Set based on lead trauma hospitals in the province which also provided data on predictors. Severely injured patients (n = 557) who had lower extremity injuries during the sample time period formed a control population. RESULTS: Poisson regression modeling showed that having a TBI was a significant predictor of premature death controlling for age and injury severity. Age, the number of comorbidities, injury severity, mechanism of injury, and discharge destination were significant predictors in the multivariate analyses for the TBI population. CONCLUSIONS: This research quantifies the elevated risk of premature death in the postacute period for seriously injured adults with TBI and identifies factors most associated with highest mortality rates in this population.
Asunto(s)
Lesiones Encefálicas/diagnóstico , Lesiones Encefálicas/mortalidad , Causas de Muerte , Mortalidad Hospitalaria/tendencias , Adolescente , Adulto , Distribución por Edad , Anciano , Anciano de 80 o más Años , Análisis de Varianza , Lesiones Encefálicas/terapia , Estudios de Cohortes , Terapia Combinada , Femenino , Escala de Coma de Glasgow , Humanos , Incidencia , Puntaje de Gravedad del Traumatismo , Masculino , Persona de Mediana Edad , Ontario/epidemiología , Distribución de Poisson , Valor Predictivo de las Pruebas , Sistema de Registros , Estudios Retrospectivos , Medición de Riesgo , Distribución por Sexo , Análisis de Supervivencia , Factores de TiempoRESUMEN
Factorial experiments were conducted using source waters from seven drinking water treatment plants in Ontario, Canada to develop statistically based model equations capable of predicting chlorine dioxide consumption and chlorite and chlorate formation upon chlorine dioxide application. The equations address raw water quality and operational parameters including pH, temperature, chlorine dioxide concentration, reaction time and water organic content (as described by non-purgeable organic carbon x ultraviolet absorbance measured at 254 nm, NPOC x UV254). Terms describing two-factor interaction effects were also included, improving the accuracy of the predictive equations in fitting measured response concentrations as evaluated through internal and external validations. Nearly 80% of the predictions for chlorine dioxide consumption and chlorite formation were observed to be within 20% of the measured levels. Over 90% of the predicted chlorate levels were within +/- 0.1 mg/L of the measured levels. Chlorine dioxide concentration and NPOC x UV254 were key parameters when developing the predictive models.