Exploring Veteran Success Through State-Level Administrative Data Sets

2017 ◽  
Vol 2016 (171) ◽  
pp. 87-95
Author(s):  
Tod Massa ◽  
Laura Gogia
Keyword(s):  
Administrative Data ◽  
State Level ◽  
Data Sets

scholarly journals Linking Spinal Cord Injury Data Sets to Describe the Patient Journey Following Injury: A Protocol

2020 ◽  
Vol 26 (4) ◽  
pp. 232-242
Author(s):  
Vanessa K. Noonan ◽  
Susan B. Jaglal ◽  
Suzanne Humphreys ◽  
Shawna Cronin ◽  
Zeina Waheed ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Administrative Data ◽  
Clinical Data ◽  
Linked Data ◽  
Population Data ◽  
Data Sets ◽  
Patient Journey ◽  
Cord Injury ◽  
Incident Cases

Background: To optimize traumatic spinal cord injury (tSCI) care, administrative and clinical linked data are required to describe the patient’s journey. Objectives: To describe the methods and progress to deterministically link SCI data from multiple databases across the SCI continuum in British Columbia (BC) and Ontario (ON) to answer epidemiological and health service research questions. Methods: Patients with tSCI will be identified from the administrative Hospital Discharge Abstract Database using International Classification of Diseases (ICD) codes from Population Data BC and ICES data repositories in BC and ON, respectively. Admissions for tSCI will range between 1995–2017 for BC and 2009-2017 for ON. Linkage will occur with multiple administrative data holdings from Population Data BC and ICES to create the “Admin SCI Cohorts.” Clinical data from the Rick Hansen SCI Registry (and VerteBase in BC) will be transferred to Population Data BC and ICES. Linkage of the clinical data with the incident cases and administrative data at Population Data BC and ICES will create subsets of patients referred to as the “Clinical SCI Cohorts” for BC and ON. Deidentified patient-level linked data sets will be uploaded to a secure research environment for analysis. Data validation will include several steps, and data analysis plans will be created for each research question. Discussion: The creation of provincially linked tSCI data sets is unique; both clinical and administrative data are included to inform the optimization of care across the SCI continuum. Methods and lessons learned will inform future data-linking projects and care initiatives.

scholarly journals Longitudinal child data: What can be gained by linking administrative data and cohort data?

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Leanne Findlay ◽  
Elizabeth Beasley ◽  
Jungwee Park ◽  
Dafna Kohen ◽  
Yann Algan ◽  
...  
Keyword(s):  
Administrative Data ◽  
Well Being ◽  
Evidence Base ◽  
Kindergarten Children ◽  
Data Sets ◽  
Original Cohort ◽  
Financial Outcomes ◽  
Probabilistic Linkage ◽  
Statistics Canada ◽  
Relationship Of

IntroductionLinked administrative data sets are an emerging tool for studying the health and well-being of the population. Previous papers have described methods for linking Canadian data, although few have specifically focused on children, nor have they described linkage between tax outcomes and a cohort of children who are particularly at risk for poor financial outcomes. Objective and methodsThis paper describes a probabilistic linkage performed by Statistics Canada linking the Montreal Longitudinal Experimental Study (MLES) and the Quebec Longitudinal Study of Kindergarten Children (QLSKC) survey cohorts and administrative tax data from 1992 through 2012. ResultsThe number of valid cases in the original cohort file with valid tax records was approximately 84\%. Rates of false positives, false negatives, sensitivity, and specificity of the linkage were all acceptable. Using the linked file, the relationship of childhood behavioural indicators and adult income can be investigated in future studies. ConclusionsInnovative methods for creating longitudinal datasets on children will assist in examining long-term outcomes associated with early childhood risk and protective factors as well as an evidence base for interventions that promote child well-being and positive outcomes.

scholarly journals Dr Foster global frailty score: an international retrospective observational study developing and validating a risk prediction model for hospitalised older persons from administrative data sets

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e026759 ◽  
Author(s):  
John T Y Soong ◽  
Jurgita Kaubryte ◽  
Danny Liew ◽  
Carol Jane Peden ◽  
Alex Bottle ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Administrative Data ◽  
Older Persons ◽  
Cohort Analysis ◽  
Risk Prediction Model ◽  
Data Sets ◽  
Predictive Capacity ◽  
Data Set ◽  
Frailty Score

ObjectivesThis study aimed to examine the prevalence of frailty coding within the Dr Foster Global Comparators (GC) international database. We then aimed to develop and validate a risk prediction model, based on frailty syndromes, for key outcomes using the GC data set.DesignA retrospective cohort analysis of data from patients over 75 years of age from the GC international administrative data. A risk prediction model was developed from the initial analysis based on seven frailty syndrome groups and their relationship to outcome metrics. A weighting was then created for each syndrome group and summated to create the Dr Foster Global Frailty Score. Performance of the score for predictive capacity was compared with an established prognostic comorbidity model (Elixhauser) and tested on another administrative database Hospital Episode Statistics (2011-2015), for external validation.Setting34 hospitals from nine countries across Europe, Australia, the UK and USA.ResultsOf 6.7 million patient records in the GC database, 1.4 million (20%) were from patients aged 75 years or more. There was marked variation in coding of frailty syndromes between countries and hospitals. Frailty syndromes were coded in 2% to 24% of patient spells. Falls and fractures was the most common syndrome coded (24%). The Dr Foster Global Frailty Score was significantly associated with in-hospital mortality, 30-day non-elective readmission and long length of hospital stay. The score had significant predictive capacity beyond that of other known predictors of poor outcome in older persons, such as comorbidity and chronological age. The score’s predictive capacity was higher in the elective group compared with non-elective, and may reflect improved performance in lower acuity states.ConclusionsFrailty syndromes can be coded in international secondary care administrative data sets. The Dr Foster Global Frailty Score significantly predicts key outcomes. This methodology may be feasibly utilised for case-mix adjustment for older persons internationally.

Estimation of Average Payloads from Weigh-in-Motion Data

2017 ◽  
Vol 2644 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Sarah Hernandez
Keyword(s):  
State Level ◽  
Gaussian Mixture ◽  
Data Sets ◽  
Body Type ◽  
Motion Data ◽  
Weigh In Motion ◽  
Measured Weight ◽  
Gross Vehicle Weight ◽  
Definition Of ◽  
Weight Data

Average payloads define the ton-to-truck conversion factors that are critical inputs to commodity-based freight forecasting models. However, average payloads are derived primarily from outdated, unrepresentative truck surveys. With increasing technological and methodological means of concurrently measuring truck configurations, commodity types, and weights, there are now viable alternatives to truck surveys. In this paper, a method to derive average payloads by truck body type and using weight data from weigh-in-motion (WIM) sensors is presented. Average payloads by truck body type are found by subtracting an estimated average empty weight from an estimated average loaded weight. Empty and loaded weights are derived from a Gaussian mixture model fit to a gross vehicle weight distribution. An analysis of truck body type distributions, loaded weights, empty weights, and resulting payloads of five-axle tractor trailer (FHWA Class 9 or 3-S2) trucks is presented to compare national and state-level Vehicle Inventory and Use Survey (VIUS) data and the WIM-based approach. Results show statistically significant differences between the three data sets in each of the comparison categories. A challenge in this analysis is the definition of a correct set of payloads because the WIM and survey data are subject to their own inherent misrepresentations. WIM data, however, provide a continuous source of measured weight data that overcome the drawback of using out-of-date surveys. Overall, average payloads from measured weights are lower than those for the national or California VIUS estimates.

scholarly journals How Administrative Data Collection and Analysis Can Better Reflect Racial and Ethnic Identities

2020 ◽  
Vol 44 (1) ◽  
pp. 301-331
Author(s):  
Samantha Viano ◽  
Dominique J. Baker
Keyword(s):  
Data Collection ◽  
Administrative Data ◽  
Race And Ethnicity ◽  
Ethnic Disparities ◽  
Ethnic Identities ◽  
Birth Certificates ◽  
Data Sets ◽  
Race Ethnicity ◽  
Equivalent Sources ◽  
Racial Ethnic

Measuring race and ethnicity for administrative data sets and then analyzing these data to understand racial/ethnic disparities present many logistical and theoretical challenges. In this chapter, we conduct a synthetic review of studies on how to effectively measure race/ethnicity for administrative data purposes and then utilize these measures in analyses. Recommendations based on this synthesis include combining the measure of Hispanic ethnicity with the broader racial/ethnic measure and allowing individuals to select more than one race/ethnicity. Data collection should rely on self-reports but could be supplemented using birth certificates or equivalent sources. Collecting data over time, especially for young people, will help identify multiracial and American Indian populations. For those with more complex racial/ethnic identities, including measures of country of origin, language, and recency of immigration can be helpful in addition to asking individuals which racial/ethnic identity they most identify with. Administrative data collection could also begin to incorporate phenotype measures to facilitate the calculation of disparities within race/ethnicity by skin tone. Those analyzing racial/ethnic disparities should understand how these measures are created and attempt to develop fieldwide terminology to describe racial/ethnic identities.

Using an Ever–English Learner Framework to Examine Disproportionality in Special Education

2017 ◽  
Vol 84 (1) ◽  
pp. 76-96 ◽  
Author(s):  
Ilana M. Umansky ◽  
Karen D. Thompson ◽  
Guadalupe Díaz
Keyword(s):  
Special Education ◽  
Students With Disabilities ◽  
Administrative Data ◽  
Discrete Time ◽  
English Learners ◽  
Secondary Level ◽  
Data Sets ◽  
English Learner ◽  
Disability Categories ◽  
Overrepresentation In Special Education

Whereas most existing research has examined the prevalence of current English learners (ELs) in special education, we propose and test the use of the ever-EL framework, which holds the subgroup of EL students stable by following all students who enter school classified as ELs. Drawing on two administrative data sets, discrete-time hazard analyses show that whereas current EL students are overrepresented in special education at the secondary level, students who enter school as ELs are significantly underrepresented in special education overall and within most disability categories. Reclassification patterns, in part, explain these findings: EL students with disabilities are far less likely than those without disabilities to exit EL services, resulting in large proportions of dually identified students at the secondary level. These findings shed new light on EL under- and overrepresentation in special education and offer insights into policies and practices that can decrease EL special education disproportionality.

Administrative Data Sets and Health Services Research on Hemoglobinopathies

2010 ◽  
Vol 38 (4) ◽  
pp. S557-S567 ◽  
Author(s):  
Scott D. Grosse ◽  
Sheree L. Boulet ◽  
Djesika D. Amendah ◽  
Suzette O. Oyeku
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Administrative Data ◽  
Data Sets ◽  
Services Research

Use of Administrative Data in Efficient Auditing of Hospital-Acquired Surgical Site Infections, New York State 2009–2010

2012 ◽  
Vol 33 (6) ◽  
pp. 565-571 ◽  
Author(s):  
Valerie B. Haley ◽  
Carole Van Antwerpen ◽  
Boldtsetseg Tserenpuntsag ◽  
Kathleen A. Gase ◽  
Peggy Hazamy ◽  
...  
Keyword(s):  
New York ◽  
Administrative Data ◽  
New York State ◽  
Surgical Site Infections ◽  
Health Staff ◽  
Data Sets ◽  
York State ◽  
Diagnosis Codes ◽  
Stratified Sample ◽  
Hospital Acquired

Objective.To efficiently validate the accuracy of surgical site infection (SSI) data reported to the National Healthcare Safety Network (NHSN) by New York State (NYS) hospitals.Design.Validation study.Setting.176 NYS hospitals.Methods.NYS Department of Health staff validated the data reported to NHSN by review of a stratified sample of medical records from each hospital. The four strata were (1) SSIs reported to NHSN; (2) records with an indication of infection from diagnosis codes in administrative data but not reported to NHSN as SSIs; (3) records with discordant procedure codes in NHSN and state data sets; (4) records not in the other three strata.Results.A total of 7,059 surgical charts (6% of the procedures reported by hospitals) were reviewed. In stratum 1, 7% of reported SSIs did not meet the criteria for inclusion in NHSN and were subsequently removed. In stratum 2, 24% of records indicated missed SSIs not reported to NHSN, whereas in strata 3 and 4, only 1% of records indicated missed SSIs; these SSIs were subsequently added to NHSN. Also, in stratum 3, 75% of records were not coded for the correct NHSN procedure. Errors were highest for colon data; the NYS colon SSI rate increased by 7.5% as a result of hospital audits.Conclusions.Audits are vital for ensuring the accuracy of hospital-acquired infection (HAI) data so that hospital HAI rates can be fairly compared. Use of administrative data increased the efficiency of identifying problems in hospitals' SSI surveillance that caused SSIs to be unreported and caused errors in denominator data.

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