Administrative Data Sources Research Articles

Epidemiology and healthcare utilization of First Nations peoples living with spinal cord injury in Alberta: an observational study to explore health inequities

Study designRetrospective observational cohort study.ObjectivesEstimate spinal cord injury (SCI) prevalence in First Nations and non-First Nations populations and compare healthcare utilization as an indirect marker of health inequities.SettingAlberta, Canada.MethodsWe created a prevalent adult SCI cohort by identifying cases between April 1, 2002 and December 31, 2017 who were followed for common SCI complications and location of healthcare access from January 1, 2018 to December 31, 2019 using administrative data sources housed within Alberta Health Services (AHS). First Nations and non-First Nations SCI cohorts were divided into SCI etiology: traumatic SCI (TSCI) and non-traumatic SCI (NTSCI). Statistical analyses compared prevalence, demographics, healthcare utilization, and SCI complication rates. A secondary analysis was performed using case matching for demographics, injury type, injury level, and comorbidities.ResultsTSCI prevalence: 248 and 117 per 100,000 in First Nations and non-First Nations cohorts, respectively. NTSCI prevalence: 74 and 50 per 100,000 in First Nations and non-First Nations cohorts, respectively. Visit rates were higher in the TSCI First Nations cohort for visits to General Practitioner (GP), Emergency Department (ED), inpatient visits, and inpatient days with higher complication rates due to pulmonary, genitourinary, skin, and ‘other’ causes after case matching. Visits rates were higher in the NTSCI First Nations cohort for GP and specialists without differences in complication types after case matching.ConclusionsSignificant differences exist between First Nations and non-First Nations cohorts living with SCI in Alberta, suggesting healthcare inequities against First Nations Peoples in this province.

Využití administrativních zdrojů dat při vymezení obyvatelstva ve sčítání lidu 2021 v Česku

The article presents the administrative data sources used in the 2021 population census in Czechia and describes the main steps in data processing leading from raw input data to the final census population. Special attention is paid to signs-of-life analysis, which deals with the issue of overcoverage in the population register. The article ties in with a previous article (published in 2022) that discussed the use of registers in European population censuses and explained the process of combining data sources in the previous 2011 Czech population census.

Clinical validation of in-hospital stroke diagnosis

ObjectiveIn-hospital stroke cases occur during hospitalization for another diagnosis and reflect a clinically distinct cohort from community-onset stroke. The objective was to validate the diagnostic accuracy of in-hospital stroke identification in stroke audit data at a large teaching hospital. MethodsA retrospective clinical validation of in-hospital stroke diagnoses from two linked data sources was completed for a 2-year period from January 1st 2020 to December 31st 2021. The linked data sources include the Hospital Inpatient Enquiry system which assigns coded stroke diagnoses at discharge and/or the local stroke audit coordinators who work clinically in stroke teams and input additional specific clinical data. Diagnostic sensitivity, specificity and the level of agreement using an unweighted Cohen's Kappa were calculated. ResultsThere were 597 strokes admitted during the 2-year period. The median age was 72 years and 55% occurred in males. In total, 88 cases of in-hospital stroke were clinically validated yielding an in-hospital stroke rate of 15%. The clinical audit coordinator identified in-hospital stroke with higher sensitivity (86%; 95% CI 77%-93%) whereas the coding process was more specific at 96% (95% CI 85% to 99%). Levels of agreement with the clinically validated gold standard sample were moderate for the audit coordinator and coding process with κ = 0.57 and K = 0.42 respectively. When both data sources were combined the level of agreement was substantial (κ = 0.65; p < .000). ConclusionsClinical validation studies are required to reinforce data quality within stroke registers. Combining clinical and administrative data sources improves diagnostic accuracy for in-hospital stroke identification.

Mortality inequality, spatial differences and health care access.

Although Australia maintains relatively high standards of health and healthcare, there exists disparity in health outcomes and longevity among different segments of the population. Internationally, there is growing evidence that life expectancy gains are not being shared equally among the rich and the poor. In this paper we examine the evolution of mortality inequality in Australia between 2001 and 2018. Using a spatial inequality model and combining data from several administrative data sources, we document significant mortality inequality between the rich and the poor in Australia. For most age groups, mortality inequality has remained unchanged over the last 20 years. However, mortality inequality is increasing for middle-aged men and women. In part, this can be explained by improvements in longevity which favor urban over rural Australians. Another contributing factor we identify is differential access to healthcare in rich and poor regions. Although Australia's socioeconomic gradient of mortality is flatter than in the US, due to universal health coverage, the fact that mortality inequality is increasing for some groups accentuates the importance of safeguarding health care accessibility.

A cohort of patients in New York State with an alcohol use disorder and subsequent treatment information – A merging of two administrative data sources

ObjectiveDespite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders. MethodsData from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims. ResultsA total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition. ConclusionThe integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.

Qualitative interviews to understand methods and systems used to collect ethnicity information in health administrative data sources in England.

Background:This article is one of a series aiming to inform analytical methods to improve comparability of estimates of ethnic health disparities based on different sources. This article explores the quality of ethnicity data and identifies potential sources of bias when ethnicity information is collected in three key NHS data sources. Future research can build on these findings to explore analytical methods to mitigate biases. Methods:Thematic analysis of semi-structured qualitative interviews to explore potential sources of error and bias in the process of collecting ethnicity information across three NHS data sources: General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR), Hospital Episode Statistics (HES) and Improving Access to Psychological Therapies (IAPT). The study included feedback from 22 experts working on different aspects of health admin data collection for England (including staff from NHS Digital, IT system suppliers and relevant healthcare service providers). Results:Potential sources of error and bias were identified across data collection, data processing and quality assurance processes. Similar issues were identified for all three sources. Our analysis revealed three main themes which can result in bias and inaccuracies in ethnicity data recorded: data infrastructure challenges, human challenges, and institutional challenges. Conclusions:Findings highlighted that analysts using health admin data should be aware of the main sources of potential error and bias in health admin data, and be mindful that the main sources of error identified are more likely to affect the ethnicity data for ethnic minority groups. Where possible, analysts should describe and seek to account for this bias in their research.

Evaluation of a Flemish Active Labour Market Policy in the framework of the European Social Fund. Results and challenges

The aim of this paper is to evaluate the “Work Experience for Young Persons” (WIJ) programme, which was implemented in Flanders between 2015 and 2018. The WIJ programme is partially financed through the European Social Fund (ESF) and targets unqualified, young, and unemployed people with the aim of facilitating their entry to the labour market. The programme involves a trajectory of intensive guidance that includes labour market orientation and coaching as first phase, plus competence strengthening activities as second phase. This analysis is based on the Flemish Public Employment Service’s administrative data sources, which allows us to exploit detailed information on previous labour market history. We apply Coarsened Exact Matching to evaluate the WIJ’s impact on young unemployed people specifically in terms of their probability of being employed, unemployed or inactive, and of re-entering into education. Our results suggest that those who have participated in the WIJ programme have lower employment and education probabilities with respect to those who participated in the standard activities proposed by the public employment service. However, we found a positive employment effect if we only consider those who participated in the standard trajectory, i.e. those whose WIJ intervention lasted fewer than 14 months; this means that the negative effects are driven by those young individuals following the extended trajectories.

For-profit outsourcing and its effects on placement stability and locality for children in care in England, 2011–2022: A longitudinal ecological analysis

BackgroundThe responsibility of local authorities in England to provide children in care with stable, local placements has become increasingly difficult due to the rising number of children in need of care and a shortage of available placements. It is unclear if the trend of outsourcing children's social care to private companies has exacerbated this challenge. This paper examines how the outsourcing of children's social care to the private market has influenced placement locality and long-term stability over time. MethodsWe created a novel dataset of multiple administrative data sources on the outsourcing, placement locality and stability, and characteristics of children in care between 2011 and 2022. We conducted time-series fixed-effects regression analysis of the impact of for-profit outsourcing on placement locality and stability from 2011 to 2022. ResultsOur fully adjusted models demonstrate that for-profit outsourcing is consistently associated with more children being placed outside their home local authority and greater placement instability. We found that an increase of 1 % point of for-profit outsourcing was associated with an average increase of 0.10 % points (95 % CI 0.02–0.17; p = 0.01) more children experiencing placement disruption, and 0.23 % points (95 % CI 0.15–0.30; p < 0.001) more children being placed outside their home local authority. We estimate that an additional 17,001 (95 % CI 9015–24,987) out-of-area placements can be attributed to increases in for-profit provision. DiscussionOur analyses show that placement stability and distance have deteriorated or stagnated over the last decade, and that the local authorities that rely most on outsourcing have the highest rates of placement disruptions and out-of-area placements.

Improving Self-reported Prescription Medicine Data Quality with a Commercial Database Lookup Tool and Claims Matching

Estimating prescription medicine use is challenging due to recall bias associated with surveys and coverage bias in administrative data. This study assesses how making operational improvements and combining both survey and administrative data sources can increase data quality on filled prescriptions. We use data from the Medicare Current Beneficiary Survey (MCBS) and administrative data from the Centers for Medicare and Medicaid Services (CMS). First, we investigate improvements from a prescription medicine lookup (PMLU) tool integrating a commercial medicine database into the MCBS. We then examine impacts of matching survey-reported medicines to Part D claims. We find that the PMLU improves accuracy and reduces measurement bias. Claims matching identifies additional medicines, especially for beneficiaries with more chronic conditions and medicines. This study shows that integrating a commercial database and supplementing with administrative data improves data quality and reduces sources of error.

Disparities in the Pretrial Process: Race, Ethnicity, and Citizenship

Prior work establishes that Black and Latino people face harsher treatment during the pretrial phase of the justice system. Yet, the mechanisms underlying pretrial racial and ethnic disparities remain unclear. Using multiple administrative data sources from a large jurisdiction in the southeast, we examined the influence of race, ethnicity, and citizenship on judicial decisions and defendant outcomes among people booked for a felony (n = 29,735). Of particular interest was the extent to which previously observed racial and ethnic disparities may be attributable to citizenship. Our results indicate that (1) Black and Latino people receive more punitive judicial decisions and experience worse pretrial outcomes than Whites, net of citizenship status; (2) noncitizens receive more lenient judicial decisions and experience better pretrial outcomes than citizens; and (3) citizenship status interacts with ethnicity, such that among noncitizens, Latinos suffer the worst outcomes of all groups. Implications for research and theorizing are discussed.

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data.

High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data. Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not. A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%). Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.

Social inequalities and long-term health impact of COVID-19 in Belgium: protocol of the HELICON population data linkage

IntroductionData linkage systems have proven to be a powerful tool in support of combating and managing the COVID-19 pandemic. However, the interoperability and the reuse of different data sources may...

A comprehensive framework to estimate the frequency, duration, and risk factors for diagnostic delays using bootstrapping-based simulation methods

BackgroundThe incidence of diagnostic delays is unknown for many diseases and specific healthcare settings. Many existing methods to identify diagnostic delays are resource intensive or difficult to apply to different diseases or settings. Administrative and other real-world data sources may offer the ability to better identify and study diagnostic delays for a range of diseases.MethodsWe propose a comprehensive framework to estimate the frequency of missed diagnostic opportunities for a given disease using real-world longitudinal data sources. We provide a conceptual model of the disease-diagnostic, data-generating process. We then propose a bootstrapping method to estimate measures of the frequency of missed diagnostic opportunities and duration of delays. This approach identifies diagnostic opportunities based on signs and symptoms occurring prior to an initial diagnosis, while accounting for expected patterns of healthcare that may appear as coincidental symptoms. Three different bootstrapping algorithms are described along with estimation procedures to implement the resampling. Finally, we apply our approach to the diseases of tuberculosis, acute myocardial infarction, and stroke to estimate the frequency and duration of diagnostic delays for these diseases.ResultsUsing the IBM MarketScan Research databases from 2001 to 2017, we identified 2,073 cases of tuberculosis, 359,625 cases of AMI, and 367,768 cases of stroke. Depending on the simulation approach that was used, we estimated that 6.9–8.3% of patients with stroke, 16.0-21.3% of patients with AMI and 63.9–82.3% of patients with tuberculosis experienced a missed diagnostic opportunity. Similarly, we estimated that, on average, diagnostic delays lasted 6.7–7.6 days for stroke, 6.7–8.2 days for AMI, and 34.3–44.5 days for tuberculosis. Estimates for each of these measures was consistent with prior literature; however, specific estimates varied across the different simulation algorithms considered.ConclusionsOur approach can be easily applied to study diagnostic delays using longitudinal administrative data sources. Moreover, this general approach can be customized to fit a range of diseases to account for specific clinical characteristics of a given disease. We summarize how the choice of simulation algorithm may impact the resulting estimates and provide guidance on the statistical considerations for applying our approach to future studies.

Population-based assessment of cardiovascular complications of rheumatic heart disease in Fiji: a record-linkage analysis

ObjectiveTo determine population-based rates of non-fatal complications of rheumatic heart disease (RHD).DesignRetrospective cohort study based on multiple sources of routine clinical and administrative data amalgamated by probabilistic record-linkage.SettingFiji, an upper-middle-income...

Examination of validity of identifying congenital heart disease from hospital discharge data without a gold standard: Using a data linkage approach.

Administrative health data has been used extensively to examine congenital heart disease (CHD). However, the accuracy and completeness of these data must be assessed. To use data linkage of multiple administrative data sources to examine the validity of identifying CHD cases recorded in hospital discharge data. We identified all liveborn infants born 2013-2017 in New South Wales, Australia with a CHD diagnosis up to age one, recorded in hospital discharge data. Using record linkage to multiple data sources, the diagnosis of CHD was compared with five reference standards: (i) multiple hospital admissions containing CHD diagnosis; (ii) receiving a cardiac procedure; (iii) CHD diagnosis in the Register of Congenital Conditions; (iv) cardiac-related outpatient health service recorded; and/or (v) cardiac-related cause of death. Positive predictive values (PPV) comparing CHD diagnosis with the reference standards were estimated by CHD severity and for specific phenotypes. Of 485,239 liveborn infants, there were 4043 infants with a CHD diagnosis identified in hospital discharge data (8.3 per 1000 live births). The PPV for any CHD identified in any of the five methods was 62.8% (95% confidence interval [CI] 60.9, 64.8), with PPV higher for severe CHD at 94.1% (95% CI 88.2, 100). Infant characteristics associated with higher PPVs included lower birthweight, presence of a syndrome or non-cardiac congenital anomaly, born to mothers aged <20 years and residing in disadvantaged areas. Using data linkage of multiple datasets is a novel and cost-effective method to examine the validity of CHD diagnoses recorded in one dataset. These results can be incorporated into bias analyses in future studies of CHD.

Cases of acquired hemophilia A following COVID-19 vaccines: Cogent safety signal or possible reporting/detection bias? Preliminary evidence from Tuscany, Italy.

Several case reports of acquired hemophilia A (AHA) following COVID-19 vaccines were recently published. A possible increased incidence of AHA during the COVID-19 vaccination campaign was also suggested. We aimed at generating evidence for the preliminary assessment of the association between AHA and COVID-19 vaccination through an ecological study in one Italian region, Tuscany. An ecological study was performed using the population-based administrative data source of Tuscany. Per each year between 2017 and 2021, we included patients aged 5+ and active into the database as of January 1. Temporal patterns of annual incidence of possible AHA cases and AHA-tested patients were respectively observed. The rates of possible AHA cases per AHA-tested patients were calculated in 2021 and 2017-2019, respectively (calendar year 2020 was excluded because non-representative of the pre-pandemic era). Age-sex standardization was applied. Poisson's 95% confidence intervals (95% CI) were estimated. Statically significant differences were defined as absence of 95% CI overlap. In 2021, standardized incidence of both possible AHA cases (5.6/million subjects/year; 95% CI = 3.4-8.7) and AHA-tested patients (60.7/1000 subjects/year; 95% CI = 60.4-60.9) showed the lowest point estimates, though only the latter was statistically different compared to previous calendar years. The standardized rate of possible AHA cases per AHA-tested patients was 9.2/100000 (95% CI = 5.6-14.3) in 2021 and 12.5/100000 (95% CI = 8.2-18.1) during 2017-2019. These preliminary findings do not support the hypothesis of an increased incidence of AHA cases during the COVID-19 vaccination campaign. However, in 2021, the still ongoing healthcare access restrictions might have contributed to the low incidence of AHA and laboratory tests observed. Therefore, large-scale multi-database studies are warranted.

Effect of socioeconomic status on patients undergoing elective abdominal aortic aneurysm repair in a publicly funded health care system.

The association between socioeconomic status (SES) and outcomes after abdominal aortic aneurysm (AAA) repair in publicly funded health care systems is poorly described. The purpose of this study was to determine the effect of SES on postoperative outcomes in patients who underwent AAA repair in Nova Scotia, Canada. We performed a retrospective analysis of all elective AAA repairs in Nova Scotia between November 2005 and March 2015 using administrative data sources. We compared postoperative 30-day outcomes and long-term survival across socio-economic quintiles, defined as the Pampalon Material Deprivation Index (MDI) and Social Deprivation Index (SDI). We also compared the relation between baseline characteristics, MDI quintile, SDI quintile and 30-day mortality. We used multivariable logistic regression and survival analysis to calculate adjusted 30-day mortality and long-term survival, respectively. A total of 1913 patients underwent AAA repair during the study period. The overall 30-day mortality rate was 2.6% (50 patients). Thirty-day outcomes including death (p = 0.8), stroke (p = 0.7), myocardial infarction (p = 0.06), length of stay (p = 0.3) and discharge disposition other than home (p = 0.8) were similar across MDI quintiles. Similarly, there was no statistically significant association between SDI quintile and postoperative outcomes. Multivariable analysis showed that age greater than 70 years (odds ratio [OR] 3.06, 95% confidence interval [CI] 1.55-6.06) and open repair (OR 3.22, 95% CI 1.59-6.52) but not MDI quintile (p = NS) or SDI quintile (p = NS) were associated with increased 30-day mortality. There was no effect of MDI or SDI quintile on long-term survival on univariable or multivariable analysis. Socioeconomic status does not appear to affect short- or long-term mortality after AAA repair in a publicly funded health care system. Further research is needed to address any existing gaps in screening and referral before repair.

A legal dispute resolution intervention for patients with substance use disorders: a study protocol for a randomized controlled trial

BackgroundSubstance use disorders (SUDs) represent major public health concerns and are linked to enhanced risk of legal consequences. Unresolved legal issues may prevent individuals with SUD from completing treatment. Interventions aimed at improving SUD treatment outcomes are limited. Filling that gap, this randomized controlled trial (RCT) tests the ability of a technology-assisted intervention to increase SUD treatment completion rates and improve post-treatment health, economic, justice-system, and housing outcomes.MethodsA randomized controlled trial with a two-year administrative follow-up period will be conducted. Eight hundred Medicaid eligible and uninsured adults receiving SUD treatment will be recruited at community-based non-profit health care clinics in Southeast, Michigan, USA. Using an algorithm embedded in a community-based case management system, we randomly assign all eligible adults to one of two groups. The treatment/intervention group will receive hands-on assistance with a technology aimed at resolving unaddressed legal issues and the control group receives no treatment. Upon enrollment into the intervention, both treatment (n = 400) and control groups (n = 400) retain traditional options to resolve unaddressed legal issues, such as hiring an attorney, but only the treatment group is targeted the technology and offered personalized assistance in navigating the online legal platform. To develop baseline and historical contexts for participants, we collect life course history reports from all participants and intend to link those in each group to administrative data sources. In addition to the randomized controlled trial (RCT), we used an exploratory sequential mixed methods and participatory-based design to develop, test, and administer our life course history instruments to all participants. The primary objective is to test whether targeting no-cost online legal resources to those experiencing SUD improves their long-term recovery and decreases negative health, economic, justice-system, and housing outcomes.DiscussionFindings from this RCT will improve our understanding of the acute socio-legal needs faced by those experiencing SUD and provide recommendations to help target resources toward the areas that best support long-term recovery. The public health impact includes making publicly available a deidentified, longitudinal dataset of uninsured and Medicaid eligible clients in treatment for SUD. Data include an overrepresentation of understudied groups including African American and American Indian Alaska Native persons documented to experience heightened risk for SUD-related premature mortality and justice-system involvement. Within these data, several intended outcome measures can inform the health policy landscape: (1) health, including substance use, disability, mental health diagnosis, and mortality; (2) financial health, including employment, earnings, public assistance receipt, and financial obligations to the state; (3) justice-system involvement, including civil and criminal legal system encounters; (4) housing, including homelessness, household composition, and homeownership.Trial registrationRetrospectively registered # NCT05665179 on December 27, 2022.

A statistical study of climate change in Ukraine under martial law

The article concentrates on the development of theoretical and methodological foundations of statistical research in the field of national environmental and economic accounting, which forms the basis for the development of indicators of climate change under martial law and shapes the adaptation to these changes. Given Russia’s military aggression against Ukraine, the national statistical system is faced with an urgent task of ensuring the continuity of the production of complete and high quality statistical information. The issue of forming a methodology for the production of statistical information in the field of ecology, i.e. climate change statistics, remains unresolved. Outlining ways to address this issue is an important condition for the improvement of modern state statistics of Ukraine and the development of statistical theory and practice, as this largely determines the quality of the information-related support for environmental management at all levels. The statistical estimation of the dynamics and structure of greenhouse gas emissions on the basis of the economic activity of enterprises was performed. It was pointed out that among the various related branch methods of obtaining information, the system of environmental and economic accounting is the most relevant and source of high quality data. One of the aspects of solving the complex problem of forming methodological bases for collecting statistical environmental information is the creation of a national system of indicators of climate change and preparing statistical informational products using administrative data sources. The paper studies issues of producing ecological information relating to Ukraine according to statistical data. Moreover, the main problems which arise during the construction of national environmental accounts were characterised. Finally, the paper identified the key factors which influence to the largest extent the quality of statistical data and calculations, and which are necessary for the transformation and development of the statistical estimation of climate change under Russian military aggression.

Psychological distress 12 years following injury in New Zealand: findings from the Prospective Outcomes of Injury Study-10 years on (POIS-10)

BackgroundInjuries can have detrimental impacts on mental health, even after physical recovery. In our Prospective Outcomes of Injury Study (POIS), 25% of participants experienced psychological distress (assessed using the Kessler 6) three months after a sentinel injury event (SIE), declining to 16% at 24 months post-SIE. Internationally, studies of hospitalised patients found distress persisted beyond 24 months post-injury and remained higher than the general population. However, most studies only assessed distress at one timepoint, relied on long-term recall, or were limited to small samples or specific injury types. Therefore, we aim to describe the prevalence of psychological distress 12 years post-SIE and to investigate pre-injury, injury-related and early post-injury characteristics associated with long-term distress.MethodsPOIS is a longitudinal cohort study of 2856 New Zealanders injured between 2007 and 2009, who were on the national injury insurer, Accident Compensation Corporation entitlement claims’ register. Of these, 2068 POIS participants completed an interview at 24 months and agreed to further contact. They were invited to a follow-up interview 12 years post-SIE which included the Kessler-6 (K6), the psychological distress outcome of interest. Data about a range of pre-injury, injury-related and early (3 months) post-injury characteristics were collected via earlier interviews or administrative data sources (e.g. hospital discharge data).ResultsTwelve years post-SIE, 1543 (75%) people were re-interviewed and 1526 completed the K6; n = 177 (12%) reported psychological distress. Multivariable modified Poisson regression models found pre-injury characteristics were associated with an increased risk of clinically relevant distress at 12 years, i.e. having inadequate income, identifying as Māori, Pacific or Asian and having one mental health condition. Early post-injury psychological distress and dissatisfaction with social relationships also increased risk. However, being older was associated with a reduced risk of distress.ConclusionClinically relevant distress persists long-term post-injury among adults with varying injury severity, types and causes, and at higher prevalence than in the general population. Early identification of injured people at risk of long-term psychological distress provides opportunities for timely interventions to reduce psychological distress.