Predictors of hospital readmission after trauma: A retrospective cohort study in New Zealand.

INTRODUCTION: Understanding predictors of hospital readmission following major trauma is important as readmissions are costly and some are potentially avoidable. This study describes the incidence of, and sociodemographic, injury-related and treatment-related factors predictive of, hospital readmission related to: a) all-causes, b) the index trauma injury, and c) subsequent injury events in the 30 days and 12 months following discharge for major trauma patients nationally in New Zealand. METHODS: Data from the New Zealand Trauma Registry (NZTR) was linked with Ministry of Health hospital discharge data. Hospital readmissions were examined for all patients entered into the NZTR for an injury event between 1 January and 31 December 2018. Readmissions were examined for the 12-months following the discharge date for participant's index trauma injury. RESULTS: Of 1986 people, 42% had ≥1 readmission in the 12 months following discharge; 15% within 30 days. Seven percent had ≥1 readmission related to the index trauma within 30 days of discharge; readmission was 3.43 (95% CI 1.87, 6.29) times as likely if the index trauma was self-inflicted compared to unintentional, and 1.64 (95% CI 1.15, 2.34) times as likely if the index trauma involved intensive care unit admission. Those admitted to hospital for longer for their index trauma were less likely to be readmitted due to their index trauma injury within 30 days compared to those admitted for 0-1 day. Seventeen percent were readmitted for a subsequent injury event within 12 months, with readmission more likely for older people (>65 years), those with comorbidities, Maori compared with non-Maori and those with higher trauma injury severity. CONCLUSION: A substantial proportion of people are readmitted after discharge for major trauma. Factors identified in this study will be useful to consider when developing interventions to reduce preventable readmissions including those related to the index trauma injury, readmissions from other causes and subsequent injury-related readmissions. Further research specifically examining planned and unplanned readmissions is warranted.

Antidepressant dispensing before, during, and after pregnancy in New Zealand, 2005-2014.

BACKGROUND: Depression during pregnancy is associated with a number of negative impacts on maternal and infant health, therefore good control of depression in pregnant women is crucial. There is a lack of population-level information about patterns of antidepressant use during pregnancy in New Zealand. AIM: To describe antidepressant dispensing patterns before, during, and after pregnancy in New Zealand, 2005-2014. MATERIALS AND METHODS: Antidepressant dispensing records from 270 days prior to pregnancy through to 360 days after pregnancy end were linked with 805 990 pregnancies in the New Zealand Pregnancy Cohort. Proportions (and 95% confidence intervals) with at least one dispensing were calculated for the periods before, during, and after pregnancy and compared over time and by maternal characteristics. RESULTS: Dispensing during the first trimester was lower than in the pre-pregnancy and post-pregnancy periods, and dropped further in later trimesters. The proportion of pregnancies during which an antidepressant was dispensed rose from 3.1 to 4.9% over the study years. Around 80% of those with a dispensing received a selective serotonin reuptake inhibitor. Dispensing before, during, and after pregnancy varied by ethnicity, age, smoking status, and body mass index. Among women taking an antidepressant before pregnancy, younger women and those of Maori, Pacific, or Asian ethnicity were less likely to continue therapy during pregnancy. CONCLUSIONS: This study has established a baseline for antidepressant use around pregnancy in New Zealand, documented increasing use over time, and demonstrated that known ethnic differences in antidepressant use are also evident in the pregnant population.

Private choices, public costs: Evaluating cost-shifting between private and public health sectors in New Zealand.

New Zealand's dual public-private health system allows individuals to purchase health services from the private sector rather than relying solely upon publicly-funded services. However, financial boundaries between the public and private sectors are not well defined and patients receiving privately-funded care may subsequently seek follow-up care within the public health system, in effect shifting costs to the public sector. This study evaluates this phenomenon, examining whether cost-shifting between the private and public hospital systems is a significant issue in New Zealand. We used inpatient discharge data from 2013/14 to identify private events with a subsequent admission to a public hospital within seven days of discharge. We examined the frequency of subsequent public admissions, the demographic and clinical characteristics of the patients and estimated the direct costs of inpatient care incurred by the public health system. Approximately 2% of private inpatient events had a subsequent admission to a public hospital. Overall, the costs to the public system amounted to NZ$11.5 million, with a median cost of NZ$2800. At least a third of subsequent admissions were related to complications of a medical procedure. Although only a small proportion of private events had a subsequent public admission, the public health system incurred significant costs, highlighting the need for greater understanding and discussion around the interface between the public and private health systems.

Using hospital discharge data for injury research or surveillance? An observational study illustrating the impact of administrative change.

INTRODUCTION: Hospital discharge data provide an important basis for determining priorities for injury prevention and monitoring trends in incidence. This study aims to illustrate the impact of a recent change in administrative practice on estimates of hospitalised injury incidence and to investigate the extent to which different case selection affects trends in injury incidence rates. METHODS: New Zealand (NZ) hospital discharges (2000-2014) with a primary diagnosis of injury were identified. Additional case selection criteria included first admissions only, and for serious injury, a high threat-to-life estimate. Comparisons were made, over time and by District Health Board, between hospitalised injury incidence estimates that included, or not, short-stay emergency department (SSED) discharges. RESULTS: Of the 1 229 772 injury hospital discharges, 365 114 were SSED; 16% of the annual total in 2000, 38% in 2014. Identification of readmissions prior to the exclusion of SSED discharges resulted in 30 724 cases being erroneously removed. Age-standardised rates of hospitalised injury over the 15-year period increased by, on average, 2.7% per year when SSED discharges were included; there was minimal secular change (-0.2%) when SSEDs were excluded. For serious hospitalised injury, the annual increase was 2.3% when SSED was included compared with 1.1% when SSEDs were excluded. CONCLUSION: Spurious trends in hospitalised injury incidence can result when administrative practices are not appropriately accounted for. Exclusion of SSED discharges before the identification of readmissions and the use of a severity threshold are recommended to minimise the reporting bias in NZ hospitalised injury incidence estimates.

Subsequent Injury Study (SInS): Improving outcomes for injured New Zealanders.

BACKGROUND: Subsequent injury (SI) is a major contributor to disability and costs for individuals and society. AIM: To identify modifiable risk factors predictive of SI and SI health and disability outcomes and costs. OBJECTIVES: To (1) describe the nature of SIs reported to New Zealand's no-fault injury insurer (the Accident Compensation Corporation (ACC)); (2) identify characteristics of people underaccessing ACC for SI; (3) determine factors predicting or protecting against SI; and (4) investigate outcomes for individuals, and costs to society, in relation to SI. DESIGN: Prospective cohort study. METHODS: Previously collected data will be linked including data from interviews undertaken as part of the earlier Prospective Outcomes of Injury Study (POIS), ACC electronic data and national hospitalisation data about SI. POIS participants (N=2856, including 566 Maori) were recruited via ACC's injury register following an injury serious enough to warrant compensation entitlements. We will examine SI over the following 24 months for these participants using descriptive and inferential statistics including multivariable generalised linear models and Cox's proportional hazards regression. DISCUSSION: Subsequent Injury Study (SInS) will deliver information about the risks, protective factors and outcomes related to SI for New Zealanders. As a result of sourcing injury data from New Zealand's 'all injury' insurer ACC, SInS includes people who have been hospitalised and not hospitalised for injury. Consequently, SInS will provide insights that are novel internationally as other studies are usually confined to examining trauma registries, specific injuries or injured workers who are covered by a workplace insurer rather than a 'real-world' injury population.

Estimating person-based injury incidence: accuracy of an algorithm to identify readmissions from hospital discharge data.

BACKGROUND: Effective use of routinely collected hospital discharge data (HDD) to estimate injury incidence requires a separate identification of new injuries from readmissions for a previous injury. The aim was to determine the accuracy of a computerised algorithm to identify injury readmissions in HDD. METHODS: A random sample of 2000 events ('key events') were selected from the 2006 injury subset of New Zealand's HDD. Discharge histories from 1989 to 2007 were extracted for individuals and manually reviewed by at least two people to determine the 'gold standard' readmission status of each key event. The algorithm relies on four variables: unique national person identifier, dates of injury, admission and discharge. Reviewers were provided with these variables as well as additional discharge information (eg, discharge type and external cause code narrative) recorded in the HDD. Results of the manual review were compared to those obtained from the algorithm. RESULTS: The algorithm assigned 1811 (90.6%) as incident admissions compared to 1800 (90.0%) classified by the gold standard. Agreement was 97.9%, and accuracy measures (sensitivity, specificity, negative predictive value and positive predictive value) ranged from 87% to 99%. No statistically significant differences between readmission assignation by the algorithm and the gold standard were observed by age, nature of injury, external cause of injury or body region. CONCLUSIONS: Any country with electronic HDD could readily identify readmissions and, thus, accurately estimate injury incidence from HDD, providing that a unique person identifier and the date of injury were included in addition to the obligatory dates of admission and discharge.