Three Es of linked injury data: Episodes, Encounters and Events

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia

BACKGROUND

Linked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records.

METHODS

We analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant RESULTS: Of 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %).

CONCLUSION

The extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Avoiding double counting: the effect of bundling hospital events in administrative datasets for the interpretation of rural-urban differences in Aotearoa New Zealand

BACKGROUND AND OBJECTIVES

All publicly funded hospital discharges in Aotearoa New Zealand are recorded in the National Minimum Dataset (NMDS). Movement of patients between hospitals (and occasionally within the same hospital) results in separate records (discharge events) within the NMDS and if these consecutive health records are not accounted for hospitalization (encounters) rates might be overestimated. The aim of this study was to determine the impact of four different methods to bundle multiple discharge events in the NMDS into encounters on the relative comparison of rural and urban Ambulatory Sensitive Hospitalization (ASH) rates.

METHODS

NMDS discharge events with an admission date between July 1, 2015, and December 31, 2019, were bundled into encounters using either using a) no method, b) an "admission flag", c) a "discharge flag", or d) a date-based method. ASH incidence rate ratios (IRRs), the mean total length of stay and the percentage of interhospital transfers were estimated for each bundling method. These outcomes were compared across 4 categories of the Geographic Classification for Health.

RESULTS

Compared with no bundling, using the date-based method resulted in an 8.3% reduction (150 less hospitalizations per 100,000 person years) in the estimated incidence rate for ASH in the most rural (R2-3) regions. There was no difference in the interpretation of the rural-urban IRR for any bundling methodology. Length of stay was longer for all bundling methods used. For patients that live in the most rural regions, using a date-based method identified up to twice as many interhospital transfers (5.7% vs 12.4%) compared to using admission flags.

CONCLUSION

Consecutive events within hospital discharge datasets should be bundled into encounters to estimate incidence. This reduces the overestimation of incidence rates and the undercounting of interhospital transfers and total length of stay.

Community Opioid Dispensing after Injury (CODI): Cohort characteristics and opioid dispensing patterns

BACKGROUND

Despite a focus of opioid-related research internationally, there is limited understanding of long-term opioid use in adults following injury. We analysed data from the 'Community Opioid Dispensing after Injury' data linkage study.

AIMS

This paper aims to describe the baseline characteristics of the injured cohort and report opioid dispensing patterns following injury-related hospitalisations.

METHODS

Retrospective cohort study of adults hospitalised after injury (ICD-10AM: S00-S99, T00-T75) in Queensland, Australia between 1 January 2014 and 31 December 2015, prior to implementation of opioid stewardship programs. Data were person-linked between hospitalisation, community opioid dispensing and mortality collections. Data were extracted for 90-days prior to the index hospital admission, to establish opiate naivety, to 720 days after discharge. Median daily oral morphine equivalents (i.e., dose) were averaged for each 30-day interval. Cumulative duration of dispensing and dose were compared by demographic and clinical characteristics, stratified by drug dependency status.

RESULTS

Of the 129,684 injured adults, 61.3 % had no opioids dispensed in the 2-year follow-up period. Adults having any opioids dispensed in the community (38.7 %) were more likely older, female, to have fracture injuries and injuries with a higher severity, compared to those with no opioids dispensed. Longer durations and higher doses of opioids were seen for those with pre-injury opioid use, more hospital readmissions and repeat surgeries, as well as those who died in the 2-year follow-up period. Median dispensing duration was 24-days with a median daily end dose of 13 oral morphine equivalents. If dispensing occurred prior to the injury, duration increased 10-fold and oral morphine equivalents doubled. Adults with a documented dependency prior to, or after, the injury had significantly longer durations of use and higher doses than the rest of the cohort receiving opioids. Approximately 7 % of the total cohort continued to be dispensed opioids at 2-years post injury.

CONCLUSION

This is a novel population-level profile of opioid dispensing patterns following injury-related hospitalisation, described for the time period prior to the implementation of opioid stewardship programs and regulatory changes in Queensland. Detailed understanding of this pre-implementation period is critical for evaluating the impact of these changes moving forward.

A population-based epidemiological and health economic analysis of fracture-related infection

Aims

The aim of this study was to perform the first population-based description of the epidemiological and health economic burden of fracture-related infection (FRI).

Methods

This is a retrospective cohort study of operatively managed orthopaedic trauma patients from 1 January 2007 to 31 December 2016, performed in Queensland, Australia. Record linkage was used to develop a person-centric, population-based dataset incorporating routinely collected administrative, clinical, and health economic information. The FRI group consisted of patients with International Classification of Disease 10th Revision diagnosis codes for deep infection associated with an implanted device within two years following surgery, while all others were deemed not infected. Demographic and clinical variables, as well as healthcare utilization costs, were compared.

Results

There were 111,402 patients operatively managed for orthopaedic trauma, with 2,775 of these (2.5%) complicated by FRI. The development of FRI had a statistically significant association with older age, male sex, residing in rural/remote areas, Aboriginal or Torres Strait Islander background, lower socioeconomic status, road traffic accident, work-related injuries, open fractures, anatomical region (lower limb, spine, pelvis), high injury severity, requiring soft-tissue coverage, and medical comorbidities (univariate analysis). Patients with FRI had an eight-times longer median inpatient length of stay (24 days vs 3 days), and a 2.8-times higher mean estimated inpatient hospitalization cost (AU$56,565 vs AU$19,773) compared with uninfected patients. The total estimated inpatient cost of the FRI cohort to the healthcare system was AU$156.9 million over the ten-year period.

Conclusion

The results of this study advocate for improvements in trauma care and infection management, address social determinants of health, and highlight the upside potential to improve prevention and treatment strategies.

Understanding compensable and non-compensable patient profiles, pathways and physical outcomes for transport and work-related injuries in Queensland, Australia through data linkage

INTRODUCTION

In many jurisdictions, people experiencing an injury often pursue compensation to support their treatment and recovery expenses. Healthcare costs form a significant portion of payments made by compensation schemes. Compensation scheme regulators need accurate and comprehensive data on injury severity, treatment pathways and outcomes to enable scheme modelling, monitoring and forecasting. Regulators routinely rely on data provided by insurers which have limited healthcare information. Health data provide richer information and linking health data with compensation data enables the comparison of profiles, patterns, trends and outcomes of injured patients who claim and injured parties who are eligible but do not claim.

METHODS AND ANALYSIS

This is a retrospective population-level epidemiological data linkage study of people who have sought ambulatory, emergency or hospital treatment and/or made a compensation claim in Queensland after suffering a transport or work-related injury, over the period 1 January 2011 to 31 December 2021. It will use person-linked data from nine statewide data sources: (1) Queensland Ambulance Service, (2) Emergency Department, (3) Queensland Hospital Admitted Patients, (4) Retrieval Services, (5) Hospital Costs, (6) Workers' Compensation, (7) Compulsory Third Party Compensation, (8) National Injury Insurance Scheme and (9) Queensland Deaths Registry. Descriptive, parametric and non-parametric statistical methods and geospatial analysis techniques will be used to answer the core research questions regarding the patient's health service use profile, costs, treatment pathways and outcomes within 2 years postincident as well as to examine the concordance and accuracy of information across health and compensation databases.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Royal Brisbane and Women's Hospital Human Research Ethics Committee, and governance approval was obtained via the Public Health Act 2005, Queensland. The findings of this study will be used to inform key stakeholders across the clinical, research and compensation regulation area, and results will be disseminated through peer-reviewed journals, conference presentations and reports/seminars with key stakeholders.

Costs and 30-day readmission after lower limb fractures from motorcycle crashes in Queensland, Australia: A linked data analysis

BACKGROUND

Lower limb trauma is the most common injury sustained in motorcycle crashes. There are limited data describing this cohort in Australia and limited international data establishing costs due to lower limb trauma following motorcycle crashes.

METHODS

This retrospective cohort study utilised administrative hospitalisation data from Queensland, Australia from 2011-2017. Eligible participants included those admitted with a principal diagnosis coded as lower extremity or pelvic fracture following a motorcycle crash (defined as the index admission). Multiply injured motorcyclists where the lower limb injury was not coded as the primary diagnosis (i.e. principal diagnosis was rather coded as head injury, internal organ injures etc.) were not included in the study. Hospitalisation data were also linked to clinical costing data. Logistic regression was used to determine risk factors for 30-day readmission. Costing data were compared between those readmitted and those who weren't, using bootstrapped t-tests and ANVOA.

RESULTS

A total of 3342 patients met eligibility, with the most common lower limb fracture being tibia/fibula fractures (40.8%). 212 participants (6.3%) were readmitted within 30-days of discharge. The following were found to predict readmission: male sex (OR 1.84, 95% CI 1.01-1.94); chronic anaemia (OR 2.19, 95% CI 1.41-3.39); current/ex-smoker (OR 1.60, 95% CI 1.21-2.12); emergency admission (OR 2.77, 95% CI 1.35-5.70) and tibia/fibula fracture type (OR 1.46, 95% CI 1.10-1.94). The most common reasons for readmission were related to ongoing fracture care, infection or post-operative complications. The average hospitalisation cost for the index admission was AU$29,044 (95% CI $27,235-$30,853) with significant differences seen between fracture types. The total hospitalisation cost of readmissions was almost AU$2 million over the study period, with an average cost of $10,977 (95% CI $9,131- $13,059).

CONCLUSIONS

Unplanned readmissions occur in 6.3% of lower limb fractures sustained in motorcycle crashes. Independent predictors of readmission within 30 days of discharge included male sex, chronic anaemia, smoking status, fracture type and emergency admission. Index admission and readmission hospitalisation costs are substantial and should prompt health services to invest in ways to reduce readmission.

Community Opioid Dispensing after Injury (CODI): Protocol for a retrospective population-based cohort study (Preprint)

Background

There is an urgent need to reduce preventable deaths and hospitalizations from prescription opioid harms and minimize the negative effect opioid misuse can have on injured individuals, families, and the wider community. Data linkage between administrative hospitalization records for injured patients and community opioid dispensing can improve our understanding of the health and surgical trajectories of injured persons and generate insights into corresponding opioid dispensing patterns.

Objective

The Community Opioid Dispensing after Injury (CODI) study aims to link inpatient hospitalization data with opioid dispensing data to examine the distribution and predictive factors associated with high or prolonged community opioid dispensing among adults, for 2 years following an injury-related hospital admission.

Methods

This is a retrospective population-based cohort study of adults aged 18 years or older hospitalized with an injury in Queensland, Australia. The study involves the linkage of statewide hospital admissions, opioid prescription dispensing, and mortality data collections. All adults hospitalized for an injury between January 1, 2014, and December 31, 2015, will be included in the cohort. Demographics and injury factors are recorded at the time of the injury admission. Opioid dispensing data will be linked and extracted for 3 months prior to the injury admission date to 2 years after the injury separation date (last date December 31, 2017). Deaths data will be extracted for the 2-year follow-up period. The primary outcome measure will be opioid dispensing (frequency and quantity) in the 2 years following the injury admission. Patterns and factors associated with community opioid dispensing will be examined for different injury types, mechanisms, and population subgroups. Appropriate descriptive statistics will be used to describe the cohort. Regression models will be used to examine factors predictive of levels and duration of opioid use. Nonparametric methods will be applied when the data are not normally distributed.

Results

The project is funded by the Royal Brisbane and Women’s Hospital Foundation. As of November 2021, all ethics and data custodian approvals have been granted. Data extraction and linkage has been completed. Data management and analysis is underway with results relating to an analysis for blunt chest trauma patients expected to be published in 2022.

Conclusions

Little is currently known of the true prevalence or patterns of opioid dispensing following injury across Queensland. This study will provide new insights about factors associated with high and long-term opioid dispensing at a population level. This information is essential to inform targeted public policy and interventions to reduce the risk of prolonged opioid use and dependence for those injured. The novel work undertaken for this project will be vital to planning, delivering, monitoring, and evaluating health care services for those injured. The findings of this study will be used to inform key stakeholders as well as clinicians and pain management services.

International Registered Report Identifier (IRRID)

RR1-10.2196/36357