Use of administrative data in healthcare research

Multimorbidity clusters potentially superior to individual diseases for stratifying fracture risk in older people: a nationwide cohort study

RATIONALE

Comorbidities are common in fracture patients, but the interaction between fracture and comorbidities remains unclear. This study aimed to define specific multimorbidity clusters in older adults and quantify the association between the multimorbidity clusters and fracture risk.

METHODS

This nationwide cohort study includes 1.7 million adults in Denmark aged ≥50 years who were followed from 2001 through 2014 for an incident low-trauma fracture. Chronic diseases and fractures were identified from the Danish National Hospital Discharge Register. Latent class analysis and Cox's regression were conducted to define the clusters and quantify fracture risk, respectively.

RESULTS

The study included 793 815 men (age: 64 ± 10) and 873 524 women (65.5 ± 11), with a third having ≥1 chronic disease. The pre-existent chronic diseases grouped individuals into low-multimorbidity (80.3% in men, 83.6% in women), cardiovascular (12.5%, 10.6%), malignant (4.1%, 3.8%), diabetic (2.4%, 2.0%) and hepatic clusters (0.7%, men only). These clusters distinguished individuals with advanced, complex, or late-stage disease from those having earlier-stage disease. During a median follow-up of 14 years (IQR: 6.5, 14), 95 372 men and 212 498 women sustained an incident fracture. The presence of multimorbidity was associated with a significantly greater risk of fracture, independent of age and sex. Importantly, the multimorbidity clusters had the highest discriminative performance in assessing fracture risk, whereas the strength of their association with fracture risk equalled or exceeded that of both the individual chronic diseases most prevalent in each cluster and of counts-based comorbidity indices.

CONCLUSIONS

Future fracture prevention strategies should take comorbidities into account. Multimorbidity clusters may provide greater insight into fracture risk than individual diseases or counts-based comorbidity indices.

Patterns of Mental Health Service Utilisation: A Population-Based Linkage of Over 17 Years of Health Administrative Records

A cross-sectoral partnership was formed in 2021 in support of the recommendations in an audit on access to state-funded mental health services. In this first paper, we aimed to describe the demographic and service utilisation of adults with a mental health diagnosis in the Western Australian state-funded health system from 2005 to 2021. Inpatient, emergency department, specialised (ambulatory) community mental health service, and death records were linked in individuals aged ≥ 18 years with a mental health diagnosis in Western Australia. Altogether, 392,238 individuals with at least one mental health service contact between 1st January 2005 and 31st December 2021 were included for analysis. Females, Aboriginal and/or Torres Strait Islander people, and those who lived outside major cities or in the most disadvantaged areas were more likely to access state-funded mental health services. While the number of individuals who accessed community mental health services increased over time (from 28,769 in 2005 to 50,690 in 2021), the percentage increase relative to 2005 was notably greater for emergency department attendances (127% for emergency department; 76% for community; and 63% for inpatient). Conditions that contributed to the increase for emergency department were mainly alcohol disorder, reaction to severe stress and adjustment disorders, and anxiety disorders. Sex differences were observed between conditions. The pattern of access increased for emergency department and the community plus emergency department combination. This study confirmed that the patterns of access of state-funded mental health services have changed markedly over time and the potential drivers underlying these changes warrant further investigation.

A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data

Patient safety is a key quality issue for health systems. Healthcare acquired adverse events (AEs) compromise safety and quality; therefore, their reporting and monitoring is a patient safety priority. Although administrative datasets are potentially efficient tools for monitoring rates of AEs, concerns remain over the accuracy of their data. Chart review validation studies are required to explore the potential of administrative data to inform research and health policy. This review aims to present an overview of the methodological approaches and strategies used to validate rates of AEs in administrative data through chart review. This review was conducted in line with the Joanna Briggs Institute methodological framework for scoping reviews. Through database searches, 1054 sources were identified, imported into Covidence, and screened against the inclusion criteria. Articles that validated rates of AEs in administrative data through chart review were included. Data were extracted, exported to Microsoft Excel, arranged into a charting table, and presented in a tabular and descriptive format. Fifty-six studies were included. Most sources reported on surgical AEs; however, other medical specialties were also explored. Chart reviews were used in all studies; however, few agreed on terminology for the study design. Various methodological approaches and sampling strategies were used. Some studies used the Global Trigger Tool, a two-stage chart review method, whilst others used alternative single-, two-stage, or unclear approaches. The sources used samples of flagged charts (n = 24), flagged and random charts (n = 11), and random charts (n = 21). Most studies reported poor or moderate accuracy of AE rates. Some studies reported good accuracy of AE recording which highlights the potential of using administrative data for research purposes. This review highlights the potential for administrative data to provide information on AE rates and improve patient safety and healthcare quality. Nonetheless, further work is warranted to ensure that administrative data are accurate. The variation of methodological approaches taken, and sampling techniques used demonstrate a lack of consensus on best practice; therefore, further clarity and consensus are necessary to develop a more systematic approach to chart reviewing.

Hospice and palliative care utilization in 16 004 232 medicare claims: comparing trauma to surgical and medical inpatients

Background

Hospice and palliative care (PC) utilization is increasing in geriatric inpatients, but limited research exists comparing rates among trauma, surgical and medical specialties. The goal of this study was to determine whether there are differences among these three groups in rates of hospice and PC utilization.

Methods

Patients from Centers for Medicare & Medicaid Services (CMS) Inpatient Standard Analytical Files for 2016-2020 aged ≥65 years were analyzed. Patients with a National Trauma Data Standard-qualifying ICD-10 injury code with abbreviated injury score ≥2 were classified as 'trauma'; the rest as 'surgical' or 'medical' using CMS MS-DRG definitions. Patients were classified as having PC if they had an ICD-10 diagnosis code for PC (Z51.5) and as hospice discharge (HD) if their hospital disposition was 'hospice' (home or inpatient). Use proportions for specialties were compared by group and by subgroups with increasing risk of poor outcome.

Results

There were 16M hospitalizations from 1024 hospitals (9.3% trauma, 26.3% surgical and 64.4% medical) with 53.7% women, 84.5% white and 38.7% >80 years. Overall, 6.2% received PC and 4.1% a HD. Both rates were higher in trauma patients (HD: 3.6%, PC: 6.3%) versus surgical patients (HD: 1.5%, PC: 3.0%), but lower than in medical patients (HD: 5.2%, PC: 7.5%). PC rates increased in higher risk patient subgroups and were highest for inpatient HD.

Conclusions

In this large study of Medicare patients, HD and PC rates varied significantly among specialties. Trauma patients had higher HD and PC utilization rates than surgical, but lower than medical. The presence of comorbidities, frailty and/or severe traumatic brain injury (in addition to advanced age) may be valuable criteria in selection of trauma patients for hospice and PC services. Further studies are needed to inform the most efficient use of hospice and PC resources, with particular focus on both timing and selection of subgroups most likely to benefit from these valuable yet limited resources.

Level of evidence

Level III, therapeutic/care management.

End-of-life expenditure on health care for the older population: a scoping review

BACKGROUND

The existing evidence shows that the pattern of health expenditure differs considerably between people at the end-of-life and people in other periods of their lives. The awareness of these differences, combined with a detailed analysis of future mortality rates is one of the key pieces of information needed for health spending prognoses. The general objective of this review was to identify and map the existing empirical evidence on end-of-life expenditure related to health care for the older population.

METHODS

To achieve the objective of the study a systematic scoping review was performed. There were 61 studies included in the analysis. The project has been registered through the Open Science Framework.

RESULTS

The included studies cover different kinds of expenditure in terms of payers, providers and types of services, although most of them include analyses of hospital spending and nearly 60% of analyses were conducted for insurance expenditure. The studies provide very different results, which are difficult to compare. However, all of the studies analyzing expenditure by survivorship status indicate that expenditure on decedents is higher than on survivors. Many studies indicate a strong relationship between health expenditure and proximity to death and indicate that proximity to death is a more important determinant of health expenditure than age per se. Drawing conclusions on the relationship between end-of-life expenditure and socio-economic status would be possible only by placing the analysis in a broader context, including the rules of a health system's organization and financing. This review showed that a lot of studies are focused on limited types of care, settings, and payers, showing only a partial picture of health and social care systems in the context of end-of-life expenditure for the older population.

CONCLUSION

The results of studies on end-of-life expenditure for the older population conducted so far are largely inconsistent. The review showed a great variety of problems appearing in the area of end-of-life expenditure analysis, related to methodology, data availability, and the comparability of results. Further research is needed to improve the methods of analyses, as well as to develop some analysis standards to enhance research quality and comparability.

Opioid-related overdose deaths among people experiencing homelessness, 2017 to 2021: A population-based analysis using coroner and health administrative data from Ontario, Canada

AIMS

To measure the change in proportion of opioid-related overdose deaths attributed to people experiencing homelessness and to compare the opioid-related fatalities between individuals experiencing homelessness and not experiencing homelessness at time of death.

DESIGN, SETTING AND PARTICIPANTS

Population-based, time-trend analysis using coroner and health administrative databases from Ontario, Canada from 1 July 2017 and 30 June 2021.

MEASUREMENTS

Quarterly proportion of opioid-related overdose deaths attributed to people experiencing homelessness. We also obtained socio-demographic and health characteristics of decedents, health-care encounters preceding death, substances directly contributing to death and circumstances surrounding deaths.

FINDINGS

A total of 6644 individuals (median age = 40 years, interquartile range = 31-51; 74.1% male) experienced an accidental opioid-related overdose death, among whom 884 (13.3%) were identified as experiencing homelessness at the time of death. The quarterly proportion of opioid-related overdose deaths attributed to people experiencing homelessness increased from 7.2% (26/359) in July-September 2017 to 16.8% (97/578) by April-June 2021 (trend test P < 0.01). Compared with housed decedents, those experiencing homelessness were younger (61.3 versus 53.1% aged 25-44), had higher prevalence of mental health or substance use disorders (77.1 versus 67.1%) and more often visited hospitals (32.1 versus 24.5%) and emergency departments (82.6 versus 68.5%) in the year prior to death. Fentanyl and its analogues more often directly contributed to death among people experiencing homelessness (94.0 versus 81.4%), as did stimulants (67.4 versus 51.6%); in contrast, methadone was less often present (7.8 versus 12.4%). Individuals experiencing homelessness were more often in the presence of a bystander during the acute toxicity event that led to death (55.8 versus 49.7%); and where another individual was present, more often had a resuscitation attempted (61.7 versus 55.1%) or naloxone administered (41.2 versus 28.9%).

CONCLUSIONS

People experiencing homelessness account for an increasing proportion of fatal opioid-related overdoses in Ontario, Canada, reaching nearly one in six such deaths in 2021.

The Carolina hysterectomy cohort (CHC): a novel case series of reproductive-aged hysterectomy patients across 10 hospitals in the US south

BACKGROUND

Hysterectomy is a common surgery among reproductive-aged U.S. patients, with rates highest among Black patients in the South. There is limited insight on causes of these racial differences. In the U.S., electronic medical records (EMR) data can offer richer detail on factors driving surgical decision-making among reproductive-aged populations than insurance claims-based data. Our objective in this cohort profile paper is to describe the Carolina Hysterectomy Cohort (CHC), a large EMR-based case-series of premenopausal hysterectomy patients in the U.S. South, supplemented with census and surgeon licensing data. To demonstrate one strength of the data, we evaluate whether patient and surgeon characteristics differ by insurance payor type.

METHODS

We used structured and abstracted EMR data to identify and characterize patients aged 18-44 years who received hysterectomies for non-cancerous conditions between 10/02/2014-12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients' socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured).

RESULTS

Of 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups.

CONCLUSION

This novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.

Development and validation of a digital biomarker predicting acute kidney injury following cardiac surgery on an hourly basis

Objectives

To develop and validate a digital biomarker for predicting the onset of acute kidney injury (AKI) on an hourly basis up to 24 hours in advance in the intensive care unit after cardiac surgery.

Methods

The study analyzed data from 6056 adult patients undergoing coronary artery bypass graft and/or valve surgery between April 1, 2012, and December 31, 2018 (development phase, training, and testing) and 3572 patients between January 1, 2019, and June 30, 2022 (validation phase). The study used 2 dynamic predictive modeling approaches, namely logistic regression and bootstrap aggregated regression trees machine (BARTm), to predict AKI. The mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values across all lead times before the occurrence of AKI were reported. The clinical practicality was assessed using calibration.

Results

Of all included patients, 8.45% and 16.66% had AKI in the development and validation phases, respectively. When applied to testing data, AKI was predicted with the mean AUC of 0.850 and 0.802 by BARTm and logistic regression, respectively. When applied to validation data, BARTm and LR resulted in a mean AUC of 0.844 and 0.786, respectively.

Conclusions

This study demonstrated the successful prediction of AKI on an hourly basis up to 24 hours in advance. The digital biomarkers developed and validated in this study have the potential to assist clinicians in optimizing treatment and implementing preventive strategies for patients at risk of developing AKI after cardiac surgery in the intensive care unit.

Effectiveness of Sotrovimab in Preventing COVID-19-Related Hospitalizations or Deaths Among US Veterans During Omicron BA.1

Background

The real-world clinical effectiveness of sotrovimab in preventing coronavirus disease 2019 (COVID-19)-related hospitalization or mortality among high-risk patients diagnosed with COVID-19, particularly after the emergence of the Omicron variant, needs further research.

Method

Using data from the US Department of Veterans Affairs (VA) health care system, we adopted a target trial emulation design in our study. Veterans aged ≥18 years, diagnosed with COVID-19 between December 1, 2021, and April 4, 2022, were included. Patients treated with sotrovimab (n = 2816) as part of routine clinical care were compared with all eligible but untreated patients (n = 11,250). Cox proportional hazards modeling estimated the hazard ratios (HRs) and 95% CIs for the association between receipt of sotrovimab and outcomes.

Results

Most (90%) sotrovimab recipients were ≥50 years old, and 64% had ≥2 mRNA vaccine doses or ≥1 dose of Ad26.COV2. During the period that BA.1 was dominant, compared with patients not treated, sotrovimab-treated patients had a 70% lower risk of hospitalization or mortality within 30 days (HR, 0.30; 95% CI, 0.23-0.40). During BA.2 dominance, sotrovimab-treated patients had a 71% (HR, 0.29; 95% CI, 0.08-0.98) lower risk of 30-day COVID-19-related hospitalization, emergency room visits, or urgent care visits (defined as severe COVID-19) compared with patients not treated.

Conclusions

Using national real-world data from high-risk and predominantly vaccinated veterans, administration of sotrovimab, compared with contemporary standard treatment regimens, was associated with reduced risk of 30-day COVID-19-related hospitalization or all-cause mortality during the Omicron BA.1 period.

Impact of consensus guideline publication on the timing of elective pediatric umbilical hernia repair

BACKGROUND

The American Academy of Pediatrics published consensus guidelines advising observation of asymptomatic umbilical hernias until age 4 or 5, given unnecessary risks of early intervention and substantial practice variation. Yet, the impact of guidelines on early repair (age <4) or if certain groups remain at risk for avoidable intervention is unclear.

METHODS

This retrospective study used data from children's hospitals participating in the Pediatric Health Information System database. Children aged 17 years and younger who underwent umbilical hernia repair from July 2017 to August 2022 were eligible for inclusion. Children with recurrent hernias, an emergency, or urgent presentation were excluded. An interrupted time series using segmented multivariable logistic regression estimated the association of guideline publication in November 2019 with the odds of guideline-adherent repair (age ≥4) after adjusting for sociodemographic characteristics and hospital-level random effects.

RESULTS

16,544 children underwent repair, of which 3,115 (18.8%) were children <4 years old. After adjustment, guideline publication was associated with an immediate increase in guideline-adherent repairs (odds ratio = 1.25 95% confidence interval = 1.05-1.49). The interrupted time series found that each month after publication was associated with a 2% increase in the odds of guideline-adherent repair (odds ratio = 1.02, 95% confidence interval = 1.01-1.03). Children with public insurance were nearly 20% less likely to receive guideline-adherent repair than privately insured children (odds ratio = 0.82, 95% confidence interval = 0.74-0.91). Children in the Midwest had lower odds of guideline-adherent repair (Midwest versus Northeast: odds ratio = 0.45. 95% confidence interval = 0.24-0.84).

CONCLUSION

Guideline publication was associated with greater odds of guideline-adherent repair, yet public insurance coverage and Midwest location remain significant predictors of early repair against recommendations.

Tixagevimab/cilgavimab for preventing COVID-19 during the Omicron surge: retrospective analysis of National Veterans Health Administration electronic data

Little is known regarding the effectiveness of tixagevimab/cilgavimab in preventing SARS-CoV-2 infection in vaccinated immunocompromised patients, particularly after the emergence of the Omicron variant. In this retrospective cohort study with exact matching and propensity score adjustment within the U.S. Department of Veterans Affairs (VA) healthcare system, we selected immunocompromised veterans age ≥18 years as of 1 January 2022, receiving VA healthcare. We compared a cohort of 1,878 patients treated with at least one dose of intramuscular tixagevimab/cilgavimab to 7,014 matched controls selected from patients who met study criteria but were not treated. Patients were followed through 15 June 2022, or until death, whichever occurred earlier. The primary outcome was a composite of SARS-CoV-2 infection, COVID-19-related hospitalization, and all-cause mortality. We used Cox proportional hazards modeling to estimate the hazard ratios (HRs) and 95% CI for the association between receipt of tixagevimab/cilgavimab and outcomes. Most (73%) tixagevimab/cilgavimab recipients were ≥65 years old, and 80% had ≥3 mRNA vaccine doses or two doses of Ad26.COV2. Compared to matched controls, recipients had a lower incidence of the composite COVID-19 outcome (49/1,878 [2.6%] versus 312/7,014 [4.4%]; HR 0.35; 95% CI, 0.24-0.52), and individually SARS-CoV-2 infection (HR 0.44; 95% CI, 0.22-0.88), COVID-19 hospitalization (HR 0.24; 95% CI, 0.10-0.59), and all-cause mortality (HR 0.32; 95% CI, 0.19-0.55). In conclusion, tixagevimab/cilgavimab was associated with lower rates of SARS-CoV-2 infection and severe COVID-19 during the Omicron BA.1, BA.2, and BA.2.12.1 surge. IMPORTANCE SARS-CoV-2 remains an ongoing global health crisis that justifies continued efforts to validate and expand, when possible, knowledge on the efficacy of available vaccines and treatments. Clinical trials have been limited due to fast tracking of medications for mitigation of the COVID-19 pandemic for the general population. We present a real-world analysis, using electronic health record data, of the effectiveness of tixagevimab/cilgavimab for the prevention of COVID-19 infection in the unique population of U.S. veterans. Unlike those in the PROVENT clinical trial from which the emergency use authorization for tixagevimab/cilgavimab as a preventative treatment arose, the veterans population is highly immunocompromised and nearly 96% totally vaccinated. These demographics allowed us to analyze the effectiveness of tixagevimab/cilgavimab in preventing COVID-19 under different conditions in a more fragile population than that of the initial clinical trial.

Risk of neurodegenerative disease or dementia in adults with attention-deficit/hyperactivity disorder: a systematic review

Purpose of review

Several psychiatric disorders have been associated with an increased risk of developing a neurodegenerative disease and/or dementia. Attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental disorder, has been understudied in relation to dementia risk. We summarized existing literature investigating the risk of incident neurodegenerative disease or dementia associated with ADHD.

Recent findings

We searched five databases for cohort, case-control, and clinical trial studies investigating associations between ADHD and neurodegenerative diseases/dementia in May 2023. Study characteristics were extracted by two independent raters, and risk of bias was assessed using the Newcastle Ottawa Scale. Search terms yielded 2,137 articles, and seven studies (five cohort and two case-control studies) ultimately met inclusion criteria. Studies examined the following types of neurodegeneration: all-cause dementia, Alzheimer's disease, Parkinson's and Lewy body diseases, vascular dementia, and mild cognitive impairment. Heterogeneity in study methodology, particularly covariates used in analyses and types of ratios for risk reported, prevented a meta-analysis and data were therefore summarized as a narrative synthesis. The majority of studies (4/7) demonstrated an overall low risk of bias.

Summary

The current literature on risk of developing a neurodegenerative disease in ADHD is limited. Although the studies identified present evidence for a link between ADHD and subsequent development of dementia, the magnitude of the direct effect of ADHD on neurodegeneration is yet to be determined and better empirically designed studies are first needed. Furthermore, the mechanism of how or why ADHD is associated with an increased risk of developing a neurocognitive disorder is still unclear and should be explored in future studies.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022348976, the PROSPERO number is CRD42022348976.

Incidence and prevalence of traumatic spinal cord injury in Canada using health administrative data

INTRODUCTION

Incidence and prevalence data are needed for the planning, funding, delivery and evaluation of injury prevention and health care programs. The objective of this study was to estimate the Canadian traumatic spinal cord injury (TSCI) incidence, prevalence and trends over time using national-level health administrative data.

METHODS

ICD-10 CA codes were used to identify the cases for the hospital admission and discharge incidence rates of TSCI in Canada from 2005 to 2016. Provincial estimates were calculated using the location of the admitting facility. Age and sex-specific incidence rates were set to the 2015/2016 rates for the 2017 to 2019 estimates. Annual incidence rates were used as input for the prevalence model that applied annual survivorship rates derived from life expectancy data.

RESULTS

For 2019, it was estimated that there were 1,199 cases (32.0 per million) of TSCI admitted to hospitals, with 123 (10% of admissions) in-hospital deaths and 1,076 people with TSCI (28.7 per million) were discharged in Canada. The estimated number of people living with TSCI was 30,239 (804/million); 15,533 (52%) with paraplegia and 14,706 (48%) with tetraplegia. Trends included an increase in the number of people injured each year from 874 to 1,199 incident cases (37%), an older average age at injury rising from 46.6 years to 54.3 years and a larger proportion over the age of 65 changing from 22 to 38%, during the 15-year time frame.

CONCLUSION

This study provides a standard method for calculating the incidence and prevalence of TSCI in Canada using national-level health administrative data. The estimates are conservative based on the limitations of the data but represent a large Canadian sample over 15 years, which highlight national trends. An increasing number of TSCI cases among the elderly population due to falls reported in this study can inform health care planning, prevention strategies, and future research.

Long-term health-care utilisation in older patients with cancer and the association with the Geriatric 8 screening tool: a retrospective analysis using linked clinical and population-based data in Belgium

BACKGROUND

Little evidence is available on the long-term health-care utilisation of older patients with cancer and whether this is associated with geriatric screening results. We aimed to evaluate long-term health-care utilisation among older patients after cancer diagnosis and the association with baseline Geriatric 8 (G8) screening results.

METHODS

For this retrospective analysis, we included data from three cohort studies for patients (aged ≥70 years) with a new cancer diagnosis who underwent G8 screening between Oct 19, 2009 and Feb 27, 2015, and who survived more than 3 months after G8 screening. The clinical data were linked to cancer registry and health-care reimbursement data for long-term follow-up. The occurrence of outcomes (inpatient hospital admissions, emergency department visits, use of intensive care, contacts with general practitioner [GP], contacts with a specialist, use of home care, and nursing home admissions) was assessed in the 3 years after G8 screening. We assessed the association between outcomes and baseline G8 score (normal score [>14] or abnormal [≤14]) using adjusted rate ratios (aRRs) calculated from Poisson regression and using cumulative incidence calculated as a time-to-event analysis with the Kaplan-Meier method.

FINDINGS

7556 patients had a new cancer diagnosis, of whom 6391 patients (median age 77 years [IQR 74-82]) met inclusion criteria and were included. 4110 (64·3%) of 6391 patients had an abnormal baseline G8 score (≤14 of 17 points). In the first 3 months after G8 screening, health-care utilisation peaked and then decreased over time, with the exception of GP contacts and home care days, which remained high throughout the 3-year follow-up period. Compared with patients with a normal baseline G8 score, patients with an abnormal baseline G8 score had more hospital admissions (aRR 1·20 [95% CI 1·15-1·25]; p<0·0001), hospital days (1·66 [1·64-1·68]; p<0·0001), emergency department visits (1·42 [1·34-1·52]; p<0·0001), intensive care days (1·49 [1·39-1·60]; p<0·0001), general practitioner contacts (1·19 [1·17-1·20]; p<0·0001), home care days (1·59 [1·58-1·60]; p<0·0001), and nursing home admissions (16·7% vs 3·1%; p<0·0001) in the 3-year follow-up period. At 3 years, of the 2281 patients with a normal baseline G8 score, 1421 (62·3%) continued to live at home independently and 503 (22·0%) had died. Of the 4110 patients with an abnormal baseline G8 score, 1057 (25·7%) continued to live at home independently and 2191 (53·3%) had died.

INTERPRETATION

An abnormal G8 score at cancer diagnosis was associated with increased health-care utilisation in the subsequent 3 years among patients who survived longer than 3 months.

FUNDING

Stand up to Cancer, the Flemish Cancer Society.

Liver-related mortality among people with hepatitis B and C: Evaluation of definitions based on linked healthcare administrative datasets

Routinely collected and linked healthcare administrative datasets could be used to monitor mortality among people with hepatitis B (HBV) and C (HCV). This study aimed to evaluate the concordance in records of liver-related mortality among people with an HBV or HCV notification, between data on hospitalization for end-stage liver disease (ESLD) and death certificates. In New South Wales, Australia, HBV and HCV notifications (1993-2017) were linked to hospital admissions (2001-2018), all-cause mortality (1993-2018) and cause-specific mortality (1993-2016) datasets. Hospitalization for ESLD was defined as a first-time hospital admission due to decompensated cirrhosis (DC) or hepatocellular carcinoma (HCC). Consistency of liver death definition of mortality following hospitalization for ESLD was compared with two death certificate-based definitions of liver deaths coded among primary and secondary cause-specific mortality data, including ESLD-related (deaths due to DC and HCC) and all-liver deaths (ESLD-related and other liver-related causes). Of 63,292 and 107,430 individuals with an HBV and HCV notification, there were 4478 (2.6%) post-ESLD hospitalization deaths, 5572 (3.3%) death certificate liver disease deaths and 2910 (1.7%) death certificate ESLD deaths. Between 2001 and 2016, among HBV post-ESLD hospitalization deaths (n = 891), 63% (562) had death certificate ESLD recorded, and 83% (741) had death certificate liver disease recorded. Between 2001 and 2016, among HCV post-ESLD hospitalization deaths (n = 3587), 58% (2082) had death certificate ESLD recorded, and 87% (3135) had death certificate liver disease recorded. At least one-third of death certificates with DC and HCC as cause of death had no mention of HBV, HCV or viral hepatitis. Our study identified limitations in estimating and tracking HBV and HCV liver disease mortality using death certificate-based data only. The optimum data for this purpose is either ESLD hospitalisations with vital status information or a combination of these with cause-specific death certificate data.

'Skeletal Age' for mapping the impact of fracture on mortality

Background

Fragility fracture is associated with an increased risk of mortality, but mortality is not part of doctor-patient communication. Here, we introduce a new concept called 'Skeletal Age' as the age of an individual's skeleton resulting from a fragility fracture to convey the combined risk of fracture and fracture-associated mortality for an individual.

Methods

We used the Danish National Hospital Discharge Register which includes the whole-country data of 1,667,339 adults in Denmark born on or before January 1, 1950, who were followed up to December 31, 2016 for incident low-trauma fracture and mortality. Skeletal age is defined as the sum of chronological age and the number of years of life lost (YLL) associated with a fracture. Cox's proportional hazards model was employed to determine the hazard of mortality associated with a specific fracture for a given risk profile, and the hazard was then transformed into YLL using the Gompertz law of mortality.

Results

During the median follow-up period of 16 years, there had been 307,870 fractures and 122,744 post-fracture deaths. A fracture was associated with between 1 and 7 years of life lost, with the loss being greater in men than women. Hip fractures incurred the greatest loss of life years. For instance, a 60-year-old individual with a hip fracture is estimated to have a skeletal age of 66 for men and 65 for women. Skeletal Age was estimated for each age and fracture site stratified by gender.

Conclusions

We propose 'Skeletal Age' as a new metric to assess the impact of a fragility fracture on an individual's life expectancy. This approach will enhance doctor-patient risk communication about the risks associated with osteoporosis.

Funding

National Health and Medical Research Council in Australia and Amgen Competitive Grant Program 2019.

Performance of French medico-administrative databases in epidemiology of infectious diseases: a scoping review

The development of medico-administrative databases over the last few decades has led to an evolution and to a significant production of epidemiological studies on infectious diseases based on retrospective medical data and consumption of care. This new form of epidemiological research faces numerous methodological challenges, among which the assessment of the validity of targeting algorithm. We conducted a scoping review of studies that undertook an estimation of the completeness and validity of French medico-administrative databases for infectious disease epidemiological research. Nineteen validation studies and nine capture-recapture studies were identified. These studies covered 20 infectious diseases and were mostly based on the evaluation of hospital claimed data. The evaluation of their methodological qualities highlighted the difficulties associated with these types of research, particularly those linked to the assessment of their underlying hypotheses. We recall several recommendations relating to the problems addressed, which should contribute to the quality of future evaluation studies based on medico-administrative data and consequently to the quality of the epidemiological indicators produced from these information systems.

Variation in hospice use among trauma centers may impact analysis of geriatric trauma outcomes: An analysis of 1,961,228 Centers for Medicare and Medicaid Services hospitalizations from 2,317 facilities

BACKGROUND

Defining discharges to hospice as "deaths" is vital for properly assessing trauma center outcomes. This is critical with older patients as a higher proportion is discharged to hospice. The goals of this study were to measure rates of hospice use, evaluate hospice discharge rates by trauma center level, and identify variables affecting hospice use in geriatric trauma.

METHODS

Patients from the Centers for Medicare and Medicaid Services Inpatient Standard Analytical Files for 2017 to 2019, 65 years or older, with ≥1 injury International Classification of Diseases, Tenth Revision , code, at hospitals with ≥50 trauma patients per year were selected. Total deaths was defined as inpatient deaths plus hospice discharges. Dominance analysis identified the most important contributors to a model of hospice use.

RESULTS

A total of 1.96 million hospitalizations from 2,317 hospitals (Level I, 10%; II, 14%; III, 18%; IV, 7%; none, 51%) were included. Level I's had significantly lower raw hospice discharge values compared with Levels II and III (I, 0.030; II, 0.035; III, 0.035; p < 0.05) but not Level IV (0.032) or nontrauma centers (0.030) ( p > 0.05). Adjusted Level I hospice discharge rates were lower than all other facility types (Level I, 0.026; II, 0.031; III, 0.034; IV, 0.033; nontrauma, 0.030; p < 0.05). Hospice discharges as a proportion of total deaths varied by level and were lowest (0.38) at Level I centers. Dominance analysis showed that proportion of patients with Injury Severity Score of >15 contributed most to explaining hospice utilization rates (3.2%) followed by trauma center level (2.3%), proportion White (1.9%), proportion female (1.5%), and urban/rural setting (1.4%).

CONCLUSION

In this near population-based geriatric trauma analysis, Level I centers had the lowest hospice discharge rate, but hospice discharge rates varied significantly by trauma level and should be included in mortality assessments of hospital outcomes. As the population ages, accurate assessment of geriatric trauma outcomes becomes more critical. Further studies are needed to evaluate the optimal utilization of hospice in end-of-life decision making for geriatric trauma.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level II.

Tracking clusters of patients over time enables extracting information from medico-administrative databases

CONTEXT

Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data.

OBJECTIVE

We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases.

MATERIAL AND METHODS

We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort.

RESULTS

Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches.

CONCLUSION

The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.

Predictive model for early functional outcomes following acute care after traumatic brain injuries: A machine learning-based development and validation study

INTRODUCTION

Few studies on early functional outcomes following acute care after traumatic brain injury (TBI) are available. The aim of this study was to develop and validate a predictive model for functional outcomes at discharge for TBI patients using machine learning methods.

PATIENTS AND METHODS

In this retrospective study, data from 5281 TBI patients admitted for acute care who were identified in the Beijing hospital discharge abstract database were analysed. Data from 4181 patients in 52 tertiary hospitals were used for model derivation and internal validation. Data from 1100 patients in 21 secondary hospitals were used for external validation. A poor outcome was defined as a Barthel Index (BI) score ≤ 60 at discharge. Logistic regression, XGBoost, random forest, decision tree, and back propagation neural network models were used to fit classification models. Performance was evaluated by the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AP), calibration plots, sensitivity/recall, specificity, positive predictive value (PPV)/precision, negative predictive value (NPV) and F1-score.

RESULTS

Compared to the other models, the random forest model demonstrated superior performance in internal validation (AUC of 0.856, AP of 0.786, and F1-score of 0.724) and external validation (AUC of 0.779, AP of 0.630, and F1-score of 0.604). The sensitivity/recall, specificity, PPV/precision, and NPV of the model were 71.8%, 69.2%, 52.2%, and 84.0%, respectively, in external validation. The BI score at admission, age, use of nonsurgical treatment, neurosurgery status, and modified Charlson Comorbidity Index were identified as the top 5 predictors for functional outcome at discharge.

CONCLUSIONS

We established a random forest model that performed well in predicting early functional outcomes following acute care after TBI. The model has utility for informing decision-making regarding patient management and discharge planning and for facilitating health care quality assessment and resource allocation for TBI treatment.

Impacts of medication non-adherence to major modifiable stroke-related diseases on stroke prevention and mortality: a meta-analysis

BACKGROUND

Medication adherence is one of the crucial attempts in primary stroke prevention. The available evidence lacks comprehensive reviews exploring the association of medication adherence with stroke prevention.

OBJECTIVES

To investigate the effects of non-adherence to medications used to treat the modifiable risk of diseases on stroke-associated outcomes in primary stroke prevention.

METHODS

Study records were searched from PubMed, Embase, and CINAHL. Those studies reported risks relevant to stroke-associated outcomes and medication non-adherence for patients diagnosed with four modifiable stroke-related diseases (atrial fibrillation [AF], hyperlipidemia, hypertension, and type 2 diabetes mellitus) but without stroke history were included for meta-analysis and further subgroup, sensitivity, and publication bias analyses. A random effect model was performed to analyse the pooled risk estimates of relative risk (RR) and 95% confidence intervals (CIs).

RESULTS

Thirty-nine studies (with 2,117,789 participants in total) designed as cohort or case-control studies were included. Those patients presenting with four stroke-related diseases and categorised as medication non-adherent tended to result in stroke and/or associated death (all pooled RR ≥ 1 and 95% CI did not include 1). The findings of stratification and sensitivity analysis for each stroke-related disease showed a similar trend. Non-adherent patients with AF were prone to stroke occurrence (RR 1.852; 95% CI 1.583-2.166) but inclined to reduced bleeding (RR 0.894; 95% CI 0.803-0.996). The existence of publication bias warrants further interpretation.

CONCLUSIONS

Non-adherence to medications for the four stroke-related diseases contributes to the development of stroke and/or mortality in primary stroke prevention. More efforts are needed to improve patients' medication adherence.

Thought Leader Perspectives on the Benefits, Barriers, and Enablers for Routinely Collected Electronic Health Data to Support Professional Development: Qualitative Study

BACKGROUND

Hospitals routinely collect large amounts of administrative data such as length of stay, 28-day readmissions, and hospital-acquired complications; yet, these data are underused for continuing professional development (CPD). First, these clinical indicators are rarely reviewed outside of existing quality and safety reporting. Second, many medical specialists view their CPD requirements as time-consuming, having minimal impact on practice change and improving patient outcomes. There is an opportunity to build new user interfaces based on these data, designed to support individual and group reflection. Data-informed reflective practice has the potential to generate new insights about performance, bridging the gap between CPD and clinical practice.

OBJECTIVE

This study aims to understand why routinely collected administrative data have not yet become widely used to support reflective practice and lifelong learning.

METHODS

We conducted semistructured interviews (N=19) with thought leaders from a range of backgrounds, including clinicians, surgeons, chief medical officers, information and communications technology professionals, informaticians, researchers, and leaders from related industries. Interviews were thematically analyzed by 2 independent coders.

RESULTS

Respondents identified visibility of outcomes, peer comparison, group reflective discussions, and practice change as potential benefits. The key barriers included legacy technology, distrust with data quality, privacy, data misinterpretation, and team culture. Respondents suggested recruiting local champions for co-design, presenting data for understanding rather than information, coaching by specialty group leaders, and timely reflection linked to CPD as enablers to successful implementation.

CONCLUSIONS

Overall, there was consensus among thought leaders, bringing together insights from diverse backgrounds and medical jurisdictions. We found that clinicians are interested in repurposing administrative data for professional development despite concerns with underlying data quality, privacy, legacy technology, and visual presentation. They prefer group reflection led by supportive specialty group leaders, rather than individual reflection. Our findings provide novel insights into the specific benefits, barriers, and benefits of potential reflective practice interfaces based on these data sets. They can inform the design of new models of in-hospital reflection linked to the annual CPD planning-recording-reflection cycle.

The burden of geriatric traumatic brain injury on trauma systems: Analysis of 348,800 Medicare inpatient claims

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability in older adults. The aim of this study was to characterize the burden of TBI in older adults by describing demographics, care location, diagnoses, outcomes, and payments in this high-risk group.

METHODS

Using 2016-2019 Centers for Medicare & Medicaid Services (CMS) Inpatient Standard Analytical Files (IPSAF), patients >65 years with TBI (>1 injury ICD-10 starting with "S06") were selected. Trauma center levels were linked to the IPSAF file via American Hospital Association Hospital Provider ID and fuzzy-string matching. Patient variables were compared across trauma center levels.

RESULTS

Three hundred forty-eight thousand eight hundred inpatients (50.4% female; 87.1% white) from 2963 US hospitals were included. Level I/II trauma centers treated 66.9% of patients; non-trauma centers treated 21.5%. Overall inter-facility transfer rate was 19.2%; in Level I/II trauma centers transfers-in represented 23.3% of admissions. Significant TBI (Head AIS ≥3) was present in 70.0%. Most frequent diagnoses were subdural hemorrhage (56.6%) and subarachnoid hemorrhage (30.6%). Neurosurgical operations were performed in 10.9% of patients and operative rates were similar regardless of center level. Total unadjusted mortality for the sample was 13.9%, with a mortality of 8.1% for those who expired in-hospital, and an additional 5.8% for those discharged to hospice. Medicare payments totaled $4.91B, with the majority (73.4%) going to Level I/II trauma centers.

CONCLUSIONS

This study fills a gap in TBI research by demonstrating that although the majority of older adult TBI patients in the United States receive care at Level I/II trauma centers, a substantial percentage are managed at other facilities, despite 1 in 10 requiring neurosurgical operation regardless of level of trauma center. This analysis provides preliminary data on the function of regionalized trauma care for older adult TBI care. Future studies assessing the efficacy of early care guidelines in this population are warranted.

Burden of disease and management of osteoarthritis and chronic low back pain: healthcare utilization and sick leave in Sweden, Norway, Finland and Denmark (BISCUITS): study design and patient characteristics of a real world data study

OBJECTIVES

Osteoarthritis (OA) and chronic low back pain (CLBP) are common musculoskeletal disorders with substantial patient and societal burden. Nordic administrative registers offer a unique opportunity to study the impact of these conditions in the real-world setting. The Burden of Disease and Management of Osteoarthritis and Chronic Low Back Pain: Health Care Utilization and Sick Leave in Sweden, Norway, Finland and Denmark (BISCUITS) study was designed to study disease prevalence and the societal and economic burden in broad OA and CLBP populations.

METHODS

Patients in Sweden, Norway, Finland and Denmark with diagnoses of OA or CLBP (low back pain record plus ≥2 pain relief prescriptions to indicate chronicity) were identified in specialty care, in primary care (Sweden and Finland) and in a quality-of-care register (Sweden). Matched controls were identified for the specialty care cohort. Longitudinal data were extracted on prevalence, treatment patterns, patient-reported outcomes, social and economic burden.

RESULTS

Almost 1.4 million patients with OA and 0.4 million with CLBP were identified in specialty care, corresponding to a prevalence in the Nordic countries of 6.3 and 1.9%, respectively. The prevalence increased to 11-14% for OA and almost 6% for CLBP when adding patients identified in primary care. OA patients had a higher Elixhauser comorbidity index (0.66 vs. 0.46) and were using opioids (44.7 vs. 10.2%) or long-term nonsteroidal anti-inflammatory drug (NSAIDs) (20.9 vs. 4.5%) more than four times as often as compared to controls. The differences were even larger for CLBP patients compared to their controls (comorbidity index 0.89 vs. 0.39, opioid use 77.7 vs. 9.4%, and long-term NSAID use 37.2 vs. 4.8%).

CONCLUSIONS

The BISCUITS study offers an unprecedented, longitudinal healthcare data source to quantify the real-world burden of more than 1.8 million patients with OA or CLBP across four countries. In subsequent papers we aim to explore among others additional outcomes and subgroups of patients, primarily those patients who may benefit most from better healthcare management.

Air Pollution and the Risk of Dementia: The Rotterdam Study

BACKGROUND

Exposure to air pollution has been suggested to increase the risk of dementia, but studies on this link often lack a detailed screening for dementia and data on important confounders.

OBJECTIVE

To determine the association of exposure to air pollution with the risk of dementia and cognitive decline in the population-based Rotterdam Study.

METHODS

Between 2009 and 2010, we determined air pollutant concentrations at participants residential addresses using land use regression models. Determined air pollutants include particulate matter <10μm (PM10) and <2.5μm (PM2.5), a proxy of elemental carbon (PM2.5 absorbance), nitrogen oxide (NOx), and nitrogen dioxide (NO2). As the individual air pollutant levels were highly correlated (r = 0.71-0.98), we computed a general marker covering all air pollutants based on a principal component analysis. We followed participants up for dementia until 2018 and determined cognitive performance during two subsequent examination rounds. Using Cox and linear mixed models, we related air pollution to dementia and cognitive decline.

RESULTS

Of the 7,511 non-demented participants at baseline, 545 developed dementia during a median follow-up of 7 years. The general marker of all air pollutants was not associated with the risk of dementia (hazard ratio [95% confidence interval]: 1.04 [0.95-1.15]), neither were the individual air pollutants. Also, the general marker of all air pollutants or the individual air pollutant levels were not associated with cognitive decline.

CONCLUSION

In this study, we found no clear evidence for an association between exposure to air pollution and the risk of dementia or cognitive decline.

Using Clinical Vignettes and a Modified Expert Delphi Panel to Determine Parameters for Identifying Non-Traumatic Spinal Cord Injury in Health Administrative and Electronic Medical Record Databases

OBJECTIVE

To obtain expert consensus on the parameters and etiologic conditions required to retrospectively identify cases of non-traumatic spinal cord injury (NTSCI) in health administrative and electronic medical record (EMR) databases based on the rating of clinical vignettes.

DESIGN

A modified Delphi process included 2 survey rounds and 1 remote consensus panel. The surveys required the rating of clinical vignettes, developed after chart reviews and expert consultation. Experts who participated in survey rounds were invited to participate in the Delphi Consensus Panel.

SETTING

An international collaboration using an online meeting platform.

PARTICIPANTS

Thirty-one expert physicians and/or clinical researchers in the field of spinal cord injury (SCI).

MAIN OUTCOME MEASURE(S)

Agreement on clinical vignettes as NTSCI. Parameters to classify cases of NTSCI in health administrative and EMR databases.

RESULTS

In health administrative and EMR databases, cauda equina syndromes should be considered SCI and classified as a NTSCI or TSCI based on the mechanism of injury. A traumatic event needs to be listed for injury to be considered TSCI. To be classified as NTSCI, neurologic sufficient impairments (motor, sensory, bowel, and bladder) are required, in addition to an etiology. It is possible to have both a NTSCI and a TSCI, as well as a recovered NTSCI. If information is unavailable or missing in health administrative and EMR databases, the case may be listed as "unclassifiable" depending on the purpose of the research study.

CONCLUSION

The Delphi panel provided guidelines to appropriately classify cases of NTSCI in health administrative and EMR databases.

Association of Multimorbidity and Excess Mortality After Fractures Among Danish Adults

IMPORTANCE

Limited knowledge about interactions among health disorders impedes optimal patient care. Because comorbidities are common among patients 50 years and older with fractures, these fractures provide a useful setting for studying interactions among disorders.

OBJECTIVE

To define multimorbidity clusters at the time of fracture and quantify the interaction between multimorbidity and fracture in association with postfracture excess mortality.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide cohort study included 307 870 adults in Denmark born on or before January 1, 1951, who had an incident low-trauma fracture between January 1, 2001, and December 31, 2014, and were followed up through December 31, 2016. Data were analyzed from February 1 to March 31, 2022.

MAIN OUTCOMES AND MEASURES

Fracture and 32 predefined chronic diseases recorded within 5 years before the index fracture were identified from the Danish National Hospital Discharge Register. Death was ascertained from the Danish Register on Causes of Death. Latent class analysis was conducted to identify multimorbidity clusters. Relative survival analysis was used to quantify excess mortality associated with the combination of multimorbidity and fractures at specific sites.

RESULTS

Among the 307 870 participants identified with incident fractures, 95 372 were men (31.0%; mean [SD] age at fracture, 72.3 [11.2] years) and 212 498 were women (69.0%; mean [SD] age at fracture, 74.9 [11.2] years). During a median of 6.5 (IQR, 3.0-11.0) years of follow-up, 41 017 men (43.0%) and 81 727 women (38.5%) died. Almost half of patients with fractures (42.9%) had at least 2 comorbidities. Comorbidities at fracture were categorized as low-multimorbidity (60.5% in men and 66.5% in women), cardiovascular (23.7% in men and 23.5% in women), diabetic (5.6% in men and 5.0% in women), malignant (5.1% in men and 5.0% in women), and mixed hepatic and/or inflammatory (5.1% in men only) clusters. These clusters distinguished individuals with advanced, complex, or late-stage disease from those with earlier-stage disease. Multimorbidity and proximal or lower leg fractures were associated with increased mortality risk, with the highest excess mortality found in patients with hip fracture in the malignant cluster (1-year excess mortality: 40.8% [95% CI: 38.1%-43.6%]). The combination of multimorbidity and fracture compounded the association with mortality, conferring much greater risk than either alone.

CONCLUSIONS AND RELEVANCE

Concomitant illnesses were common and clustered into distinct multimorbidity clusters that were associated with excess postfracture mortality. The compound contribution of multimorbidity to postfracture excess mortality highlights the need for more comprehensive approaches in these high-risk patients. The analytical approach applied to fracture could also be used to examine other sentinel health events.

Seasonal pattern in elderly hospitalized with acute kidney injury: a retrospective nationwide study in Italy

Purpose

Acute kidney injury (AKI) frequently complicates hospitalization and is associated with in-hospital mortality (IHM). It has been reported a seasonal trend in different clinical conditions. The aim of this study was to evaluate the possible relationship between seasons of the year and IHM in elderly hospitalized patients with AKI.

Methods

We selected all admissions complicated by AKI between 2000 and 2015 recorded in the Italian National Hospital Database. ICD-9-CM code 584.xx identified subjects with age ≥ 65 years and age, sex, comorbidity burden, need of dialysis treatment and IHM were compared in hospitalizations recorded during the four seasons. Moreover, we plotted the AKI observed/expected ratio and percentage of mortality during the study period.

Results

We evaluated 759,720 AKI hospitalizations (mean age 80.5 ± 7.8 years, 52.2% males). Patients hospitalized with AKI during winter months had higher age, prevalence of dialysis-dependent AKI, and number of deceased patients. In whole population IHM was higher in winter and lower in summer, while the AKI observed/expected ratio demonstrated two peaks, one in summer and one in winter. Logistic regression analysis demonstrated that parameters such as age, autumn, winter, comorbidity burden were positively associated with IHM.

Conclusion

We conclude that a seasonality exists in AKI, however, relationship between seasons and AKI could vary depending on the aspects considered. Both autumn and winter months are independent risk factors for IHM in patients with AKI regardless of age, sex and comorbidity burden. On the contrary, summer time reduces the risk of death during hospitalizations with AKI.

Attitudes of Australian breast cancer patients toward the secondary use of administrative and clinical trial data

AIM

Little is known about the attitudes of Australian patients with a history of breast cancer toward the reuse of administrative health data and clinical trial data. Issues of consent, privacy, and information security are key to the discussion. Cancer care and research provides an opportune setting to develop an understanding of attitudes toward data sharing and reuse in individuals with a history of breast cancer.

METHODS

An anonymous, online questionnaire for individuals with a history or diagnosis of breast cancer was distributed by two peak bodies (Breast Cancer Trials [BCT] and Breast Cancer Network of Australia [BCNA]) to their memberships between July 14, 2020 and October 17, 2020. Results were captured in RedCap; data analysis was undertaken using Stata, and a thematic analysis of free text responses was undertaken using NVivo.

RESULTS

One hundred and thirty-two complete responses were received. Twenty-three percent of respondents had participated in a clinical trial, and 12% were currently receiving treatment (chemotherapy, radiotherapy, surgery, or endocrine). Respondents were supportive of the secondary use of de-identified administrative health data and clinical trial data, but showed concern about data security and privacy. Respondents emphasized that the reuse of data should be for improved societal health outcomes, not profit. Many assumed secondary analysis was already undertaken on de-identified administrative health data and clinical trial data.

CONCLUSIONS

Respondents were supportive of the secondary use of de-identified administrative health and clinal trial data within the established bounds of good clinical practice and ethical oversight.

Length of stay as quality indicator in emergency departments: analysis of determinants in the German Emergency Department Data Registry (AKTIN registry)

Several indicators reflect the quality of care within emergency departments (ED). The length of stay (LOS) of emergency patients represents one of the most important performance measures. Determinants of LOS have not yet been evaluated in large cohorts in Germany. This study analyzed the fixed and influenceable determinants of LOS by evaluating data from the German Emergency Department Data Registry (AKTIN registry). We performed a retrospective evaluation of all adult (age ≥ 18 years) ED patients enrolled in the AKTIN registry for the year 2019. Primary outcome was LOS for the whole cohort; secondary outcomes included LOS stratified by (1) patient-related, (2) organizational-related and (3) structure-related factors. Overall, 304,606 patients from 12 EDs were included. Average LOS for all patients was 3 h 28 min (95% CI 3 h 27 min-3 h 29 min). Regardless of other variables, patients admitted to hospital stayed 64 min longer than non-admitted patients. LOS increased with patients' age, was shorter for walk-in patients compared to medical referral, and longer for non-trauma presenting complaints. Relevant differences were also found for acuity level, day of the week, and emergency care levels. We identified different factors influencing the duration of LOS in the ED. Total LOS was dependent on patient-related factors (age), disease-related factors (presentation complaint and triage level), and organizational factors (weekday and admitted/non-admitted status). These findings are important for the development of management strategies to optimize patient flow through the ED and thus to prevent overcrowding.

The public health burden of geriatric trauma: Analysis of 2,688,008 hospitalizations from Centers for Medicare and Medicaid Services inpatient claims

BACKGROUND

Geriatric trauma care (GTC) represents an increasing proportion of injury care, but associated public health research on outcomes and expenditures is limited. The purpose of this study was to describe GTC characteristics, location, diagnoses, and expenditures.

METHODS

Patients at short-term nonfederal hospitals, 65 years or older, with ≥1 injury International Classification of Diseases, Tenth Revision, were selected from 2016 to 2019 Centers for Medicare and Medicaid Services Inpatient Standard Analytical Files. Trauma center levels were linked to Inpatient Standard Analytical Files data via American Hospital Association Hospital ID and fuzzy string matching. Demographics, care location, diagnoses, and expenditures were compared across groups.

RESULTS

A total of 2,688,008 hospitalizations (62% female; 90% White; 71% falls; mean Injury Severity Score, 6.5) from 3,286 hospitals were included, comprising 8.5% of all Medicare inpatient hospitalizations. Level I centers encompassed 7.2% of the institutions (n = 236) but 21.2% of hospitalizations, while nontrauma centers represented 58.5% of institutions (n = 1,923) and 37.7% of hospitalizations. Compared with nontrauma centers, patients at Level I centers had higher Elixhauser scores (9.0 vs. 8.8) and Injury Severity Score (7.4 vs. 6.0; p < 0.0001). The most frequent primary diagnosis at all centers was hip/femur fracture (28.3%), followed by traumatic brain injury (10.1%). Expenditures totaled $32.9 billion for trauma-related hospitalizations, or 9.1% of total Medicare hospitalization expenditures and approximately 1.1% of the annual Medicare budget. The overall mortality rate was 3.5%.

CONCLUSION

Geriatric trauma care accounts for 8.5% of all inpatient GTC and a similar percentage of expenditures, the most common injury being hip/femur fractures. The largest proportion of GTC occurs at nontrauma centers, emphasizing their vital role in trauma care. Public health prevention programs and GTC guidelines should be implemented by all hospitals, not just trauma centers. Further research is required to determine the optimal role of trauma systems in GTC, establish data-driven triage guidelines, and define the impact of trauma centers and nontrauma centers on GTC mortality.

LEVEL OF EVIDENCE

Therapeutic/care management, Level III.

Building Infrastructure for Surveillance of Adverse and Positive Childhood Experiences: Integrated, Multimethod Approaches to Generate Data for Prevention Action

Adverse and positive childhood experiences have a profound impact on lifespan health and well-being. However, their incorporation into ongoing population-based surveillance systems has been limited. This paper outlines critical steps in building a comprehensive approach to adverse and positive childhood experiences surveillance, provides examples from the Preventing Adverse Childhood Experiences: Data to Action cooperative agreement, and describes improvements needed to optimize surveillance data for action. Components of a comprehensive approach to adverse and positive childhood experiences surveillance include revisiting definitions and measurement, including generating and using uniform definitions for adverse and positive childhood experiences across data collection efforts; conducting youth-based surveillance of adverse and positive childhood experiences; using innovative methods to gather and analyze near real-time data; leveraging available data, including from administrative sources; and integrating data on community- and societal-level risk and protective factors for adverse childhood experiences, including social and health inequities such as racism and poverty, as well as policies and conditions that create healthy environments for children and families. Comprehensive surveillance data on adverse and positive childhood experiences can inform data-driven prevention and intervention efforts, including focusing prevention programming and services to populations in greatest need. Data can be used to evaluate progress in reducing the occurrence of adverse childhood experiences and bolstering the occurrence of positive childhood experiences. Through expansion and improvement in adverse and positive childhood experiences surveillance-including at federal, state, territorial, tribal, and local levels-data-driven action can reduce children's exposure to violence and other adversities and improve lifelong health and well-being.

Morbidity and health care use among siblings of children with cancer: A population-based study

BACKGROUND

Childhood cancer impacts the entire family unit. We sought to investigate its impact on the long-term physical health outcomes of siblings of children with cancer.

PROCEDURE

Pediatric cancer patients diagnosed in Ontario, Canada between 1988 and 2016 were linked to biological siblings. Sibling cases were matched to population controls based on sex, age, geographic location, and number of other children in the family. After individual linkage to health services data, we compared several outcomes between sibling cases and controls: (a) physical health conditions (such as diabetes, hypertension, and death); (b) acute health care use (hospitalization, low- and high-acuity emergency department [ED] visits); and (c) preventive health care use (periodic health checkups, influenza vaccinations). Cox proportional hazards, recurrent event, or logistic regression models were used as appropriate.

RESULTS

We identified 8529 sibling cases and 30,364 matched controls (median age at index: 6 years, median age at last follow-up 17 years). Compared to controls, siblings were at increased risk of hypertension (hazard ratio [HR] 1.8; 95% confidence interval [CI] 1.1-2.9; p = .01), had higher rates of low- and high-acuity ED visits (rate ratio 1.1; 95% CI 1.1-1.2; p < .001), and increased risk of hospitalization (HR 1.1; 95% CI 1.1-1.2; p < .001). Sibling cases were also more likely to receive preventive health care (p < .05).

CONCLUSION

Increased risk of hypertension, high-acuity ED visits, and hospitalizations suggest that siblings may experience poorer health compared to controls. Counseling families about this potential increased risk and long-term follow-up of siblings to monitor their physical health may be justified.

HYPERGLYCEMIA IN NON-DIABETIC ADULT TRAUMA PATIENTS IS ASSOCIATED WITH WORSE OUTCOMES THAN DIABETIC PATIENTS: AN ANALYSIS OF 95,764 PATIENTS

INTRODUCTION

The adverse impact of acute hyperglycemia is well documented but its specific effects on non-diabetic trauma patients are unclear. The purpose of this study was to analyze the differential impact of hyperglycemia on outcomes between diabetic and non-diabetic trauma inpatients.

METHODS

Adults admitted 2018-19 to 46 Level I/II trauma centers with >2 blood glucose tests (BGT) were analyzed. Diabetes status was determined from ICD-10, trauma registry and/or HbA1c >6.5. Patients with and without >1 hyperglycemic result >180 mg/dL were compared. Logistic regression examined the effects of hyperglycemia and diabetes on outcomes, adjusting for age, gender, ISS & BMI.

RESULTS

There were 95,764 patients: male 54%, mean age 61, mean ISS 10, diabetic 21%. Patients with hyperglycemia had higher mortality and worse outcomes compared to those without hyperglycemia. Non-diabetic hyperglycemic patients had the highest odds of mortality (Diabetic: aOR: 3.11, 95% CI: 2.8-3.5, Non-diabetics aOR: 7.5, 95% CI: 6.8-8.4). Hyperglycemic non-diabetics experienced worse outcomes on every measure when compared to non-hyperglycemic non-diabetics, with higher rates of sepsis (1.1 vs 0.1%, P < .001), more SSIs (1.0 vs 0.1%, P < .001), longer mean hospital LOS (11.4 vs. 5.0, P < .001), longer mean ICU LOS (8.5 vs. 4.0, P < .001), higher rates of ICU use (68.6% vs. 35.1), and more ventilator use (42.4% vs. 7.3%).

CONCLUSIONS

Hyperglycemia is associated with increased odds of mortality in both diabetic and non-diabetic patients. Hyperglycemia during hospitalization in non-diabetics was associated with the worst outcomes and represents a potential opportunity for intervention in this high-risk group.

LEVEL OF EVIDENCE

Level II (therapeutic/care management).

Reporting transparency and completeness in trials: Paper 3 - trials conducted using administrative databases do not adequately report elements related to use of databases

OBJECTIVE

We evaluated reporting completeness and transparency in randomized controlled trials (RCTs) conducted using administrative data based on 2021 CONSORT Extension for Trials Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria.

STUDY DESIGN AND SETTING

MEDLINE and the Cochrane Methodology Register were searched (2011 and 2018). Eligible RCTs used administrative databases for identifying eligible participants or collecting outcomes. We evaluated reporting based on CONSORT-ROUTINE, which modified eight items from CONSORT 2010 and added five new items.

RESULTS

Of 33 included trials (76% used administrative databases for outcomes, 3% for identifying participants, 21% both), most were conducted in the United States (55%), Canada (18%), or the United Kingdom (12%). Of eight items modified in the extension; six were adequately reported in a majority (>50%) of trials. For the CONSORT-ROUTINE modification portion of those items, three items were reported adequately in >50% of trials, two in <50%, two only applied to some trials, and one only had wording modifications and was not evaluated. For five new items, four that address use of routine data in trials were reported inadequately in most trials.

CONCLUSION

How administrative data are used in trials is often sub-optimally reported. CONSORT-ROUTINE uptake may improve reporting.

Administrative data in pediatric critical care research-Potential, challenges, and future directions

Heterogenous patient populations with small case numbers constitute a relevant barrier to research in pediatric critical care. Prospective studies bring along logistic barriers and-if interventional-ethical concerns. Therefore, retrospective observational investigations, mainly multicenter studies or analyses of registry data, prevail in the field of pediatric critical care research. Administrative health care data represent a possible alternative to overcome small case numbers and logistic barriers. However, their current use is limited by a lack of knowledge among clinicians about the availability and characteristics of these data sets, along with required expertise in the handling of large data sets. Specifically in the field of critical care research, difficulties to assess the severity of the acute disease and estimate organ dysfunction and outcomes pose additional challenges. In contrast, trauma research has shown that classification of injury severity from administrative data can be achieved and chronic disease scores have been developed for pediatric patients, nurturing confidence that the remaining obstacles can be overcome. Despite the undoubted challenges, interdisciplinary collaboration between clinicians and methodologic experts have resulted in impactful publications from across the world. Efforts to enable the estimation of organ dysfunction and measure outcomes after critical illness are the most urgent tasks to promote the use of administrative data in critical care. Clever analysis and linking of different administrative health care data sets carry the potential to advance observational research in pediatric critical care and ultimately improve clinical care for critically ill children.

A Comparison of Confounder Selection and Adjustment Methods for Estimating Causal Effects Using Large Healthcare Databases

PURPOSE

Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges. The objective was to evaluate the capacity of such methods to select confounders and reduce unmeasured confounding bias.

METHODS

A simulation study with known true effects was conducted. Completely synthetic and partially synthetic data incorporating real large healthcare data were generated. We compared Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, Group Lasso and Doubly Robust Estimation, high-dimensional propensity score, and scalable collaborative targeted maximum likelihood algorithms. For the high-dimensional propensity score, two adjustment approaches targeting the effect in the whole population were considered: full matching and inverse probability weighting.

RESULTS

In scenarios without hidden confounders, most methods were essentially unbiased. The bias and variance of the high-dimensional propensity score varied considerably according to the number of variables selected by the algorithm. In scenarios with hidden confounders, substantial bias reduction was achieved by using machine learning methods to identify proxies as compared to adjusting only by observed confounders. High-dimensional propensity score and Group Lasso performed poorly in the partially synthetic simulation. Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, and scalable collaborative targeted maximum likelihood algorithms performed particularly well.

CONCLUSIONS

Machine learning can help to identify measured confounders in large healthcare databases. They can also capitalize on proxies of unmeasured confounders to substantially reduce residual confounding bias. This article is protected by copyright. All rights reserved.

Minimum Data Set for a Poisoning Registry: A Systematic Review

Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry. Therefore, the present systematic review was conducted in 2019 to identify the minimum data set for a poisoning registry. Searches were performed in four scientific databases, i.e., PubMed, Scopus, Web of Science, and Embase. The keywords used in the searches included minimum data set, "poison", and "registry". Two researchers independently evaluated the titles, abstracts, and texts of the papers. The data were collected from the related papers. Ultimately, the minimum data set was identified for the poisoning registry. Data elements extracted from the sources were classified into two general categories: administrative data and clinical data. Ninety-eight data elements in the administrative data category were subdivided into three sections: general data, admission data, and discharge data. One-hundred and thirty-one data elements in the clinical data category were subdivided into five sections: clinical observation data, clinical assessment data, past medical history data, diagnosis data, and treatment plan data. The minimum data set is a prerequisite for creating and using a poisoning registry and data system. It is suggested to evaluate and use the poisoning minimum data set in accordance with the national laws, needs, and standards based on the opinion of the local experts.

Evaluating Telehealth Implementation in the Context of Pediatric Chronic Pain Treatment during COVID-19

Telehealth has emerged as a promising healthcare delivery modality due to its ability to ameliorate traditional access-level barriers to treatment. In response to the onset of the novel coronavirus (COVID-19) pandemic, multidisciplinary pain clinics either rapidly built telehealth infrastructure from the ground up or ramped up existing services. As the use of telehealth increases, it is critical to develop data collection frameworks that guide implementation. This applied review provides a theoretically-based approach to capitalize on existing data sources and collect novel data to inform virtually delivered care in the context of pediatric pain care. Reviewed multisource data are (1) healthcare administrative data; (2) electronic chart review; (3) clinical health registries; and (4) stakeholder feedback. Preliminary telehealth data from an interdisciplinary pediatric chronic pain management clinic (PPMC) serving youth ages 8–17 years are presented to illustrate how relevant implementation outcomes can be extracted from multisource data. Multiple implementation outcomes were assessed, including telehealth adoption rates, patient clinical symptoms, and mixed-method patient-report telehealth satisfaction. This manuscript provides an applied roadmap to leverage existing data sources and incorporate stakeholder feedback to guide the implementation of telehealth in pediatric chronic pain settings through and beyond COVID-19. Strengths and limitations of the modeled data collection approach are discussed within the broader context of implementation science.

Stability over time of the "hospital effect" on 30-day unplanned readmissions: Evidence from administrative data

Past studies showed that hospital characteristics affect hospital performance in terms of 30-day unplanned readmissions, proving the existence of a "hospital effect". However, the stability over time of this effect has been under-investigated. This study offers new evidence about the stability over time of the hospital effect on 30-day unplanned readmissions. Using 78,907 heart failure (HF) records collected from 116 hospitals in the Lombardy Region (Northern Italy) over three years (2010-2012), this study analysed hospital performance in terms of 30-day unplanned readmissions. Hospitals with unusually high and low readmission rates were identified through multi-level regression that combined both patient and hospital covariates in each year. Our results confirm that although hospital covariates - and the connected managerial choices - affect the 30-day unplanned readmissions of a specific year, their effect is not stable in the short-term (3 years). This has important implications for pay-for-performance schemes and quality improvement initiatives.

A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research

OBJECTIV E

To summarise current evidence for the utility of interval imaging in monitoring disease in adult brain tumours, and to develop a position for future evidence gathering while incorporating the application of data science and health economics.

METHODS

Experts in 'interval imaging' (imaging at pre-planned time-points to assess tumour status); data science; health economics, trial management of adult brain tumours, and patient representatives convened in London, UK. The current evidence on the use of interval imaging for monitoring brain tumours was reviewed. To improve the evidence that interval imaging has a role in disease management, we discussed specific themes of data science, health economics, statistical considerations, patient and carer perspectives, and multi-centre study design. Suggestions for future studies aimed at filling knowledge gaps were discussed.

RESULTS

Meningioma and glioma were identified as priorities for interval imaging utility analysis. The "monitoring biomarkers" most commonly used in adult brain tumour patients were standard structural MRI features. Interval imaging was commonly scheduled to provide reported imaging prior to planned, regular clinic visits. There is limited evidence relating interval imaging in the absence of clinical deterioration to management change that alters morbidity, mortality, quality of life, or resource use. Progression-free survival is confounded as an outcome measure when using structural MRI in glioma. Uncertainty from imaging causes distress for some patients and their caregivers, while for others it provides an important indicator of disease activity. Any study design that changes imaging regimens should consider the potential for influencing current or planned therapeutic trials, ensure that opportunity costs are measured, and capture indirect benefits and added value.

CONCLUSION

Evidence for the value, and therefore utility, of regular interval imaging is currently lacking. Ongoing collaborative efforts will improve trial design and generate the evidence to optimise monitoring imaging biomarkers in standard of care brain tumour management.

A Review on the Application and Limitations of Administrative Health Care Data for the Study of Acute Kidney Injury Epidemiology and Outcomes in Children

Administrative health care databases contain valuable patient information generated by health care encounters. These "big data" repositories have been increasingly used in epidemiological health research internationally in recent years as they are easily accessible and cost-efficient and cover large populations for long periods. Despite these beneficial characteristics, it is also important to consider the limitations that administrative health research presents, such as issues related to data incompleteness and the limited sensitivity of the variables. These barriers potentially lead to unwanted biases and pose threats to the validity of the research being conducted. In this review, we discuss the effectiveness of health administrative data in understanding the epidemiology of and outcomes after acute kidney injury (AKI) among adults and children. In addition, we describe various validation studies of AKI diagnostic or procedural codes among adults and children. These studies reveal challenges of AKI research using administrative data and the lack of this type of research in children and other subpopulations. Additional pediatric-specific validation studies of administrative health data are needed to promote higher volume and increased validity of this type of research in pediatric AKI, to elucidate the large-scale epidemiology and patient and health systems impacts of AKI in children, and to devise and monitor programs to improve clinical outcomes and process of care.

The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research.

Impact of laparoscopic approach on the short-term outcomes of elderly patients with colorectal cancer: a nationwide Italian experience

INTRODUCTION

The laparoscopic approach is increasingly adopted in colorectal cancer surgery; however, its role in elderly patients is controversial. We sought to examine the relationship between age and short-term outcomes following laparoscopic surgery for colorectal cancer (CRC).

METHODS

Data of patients 65 + years old who underwent laparoscopic surgery for CRC between 2002 and 2014 were retrieved from the administrative National Italian Hospital Discharge Dataset. Patients were divided into three age categories (65-74, 75-84, and 85  +). The impact of age on length of stay, 30-day readmission, in-hospital mortality, and postoperative complications was evaluated.

RESULTS

During the study period, 47,704 patients underwent laparoscopic surgery for CRC. The median postoperative length of stay was 9 days, and 30-day readmission and in-hospital mortality were 4.4% and 0.9%, respectively. Age was found to be an independent risk factor of prolonged length of stay and increased in-hospital mortality. With respect to patients in 65-74 years age category, patients aged 75-84 years and those aged 85 + years had a higher risk of complications (OR 1.43, 95% CI 1.36-1.50, and OR 2.00, 95% CI 1.83-2.17, respectively). However, no statistically significant association was found between age and anastomotic leakage or surgical site infection (p = 0.29, and p = 0.58, respectively).

CONCLUSIONS

In patients with CRC who underwent laparoscopic surgery, age was found to be an independent risk factor for prolonged length of stay, in-hospital mortality, and global postoperative complications. These findings should be considered when planning laparoscopic surgery in elderly patients.

Impact of discretization of the timeline for longitudinal causal inference methods

In longitudinal settings, causal inference methods usually rely on a discretization of the patient timeline that may not reflect the underlying data generation process. This article investigates the estimation of causal parameters under discretized data. It presents the implicit assumptions practitioners make but do not acknowledge when discretizing data to assess longitudinal causal parameters. We illustrate that differences in point estimates under different discretizations are due to the data coarsening resulting in both a modified definition of the parameter of interest and loss of information about time-dependent confounders. We further investigate several tools to advise analysts in selecting a timeline discretization for use with pooled longitudinal targeted maximum likelihood estimation for the estimation of the parameters of a marginal structural model. We use a simulation study to empirically evaluate bias at different discretizations and assess the use of the cross-validated variance as a measure of data support to select a discretization under a chosen data coarsening mechanism. We then apply our approach to a study on the relative effect of alternative asthma treatments during pregnancy on pregnancy duration. The results of the simulation study illustrate how coarsening changes the target parameter of interest as well as how it may create bias due to a lack of appropriate control for time-dependent confounders. We also observe evidence that the cross-validated variance acts well as a measure of support in the data, by being minimized at finer discretizations as the sample size increases.

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study

Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019. These data were used to assess the feasibility of predicting LOS and discharge destination using natural language processing and a variety of machine learning models. 313 patients were included in the study. The artificial neural network achieved the highest accuracy on the primary outcome of predicting whether a patient would remain in hospital for > 2 days (accuracy 0.82, area under the received operator curve 0.75, sensitivity 0.47 and specificity 0.97). When predicting LOS as an exact number of days, the artificial neural network achieved a mean absolute error of 2.9 and a mean squared error of 16.8 on the test set. For the prediction of home as a discharge destination (vs any non-home alternative), all models performed similarly with an accuracy of approximately 0.74. This study supports the feasibility of using natural language processing to predict general medical inpatient LOS and discharge destination. Further research is indicated with larger, more detailed, datasets from multiple centres to optimise and examine the accuracy that may be achieved with such predictions.

Autism spectrum disorder/Takiwātanga: An Integrated Data Infrastructure-based approach to autism spectrum disorder research in New Zealand

New Zealand has few estimates of the prevalence of autism spectrum disorder and no national registry. The use of administrative data sources is expanding and could be useful in autism spectrum disorder research. However, the extent to which autism spectrum disorder can be captured in these data sources is unknown. In this study, we utilised three linked administrative health data sources from the Integrated Data Infrastructure to identify cases of autism spectrum disorder among New Zealand children and young people. We then investigated the extent to which a range of mental health, neurodevelopmental and related problems co-occur with autism spectrum disorder. In total, 9555 unique individuals aged 0–24 with autism spectrum disorder were identified. The identification rate for 8-year-olds was 1 in 102. Co-occurring mental health or related problems were noted in 68% of the autism spectrum disorder group. The most common co-occurring conditions were intellectual disability, disruptive behaviours and emotional problems. Although data from the Integrated Data Infrastructure may currently undercount cases of autism spectrum disorder, they could be useful for monitoring service and treatment-related trends, types of co-occurring conditions and for examining social outcomes. With further refinement, the Integrated Data Infrastructure could prove valuable for informing the national incidence and prevalence of autism spectrum disorder and the long-term effectiveness of clinical guidelines and interventions for this group.

Understanding childhood injuries in rural areas: Using Rural Acute Hospital Data Register to address previous data deficiencies

OBJECTIVE

The state of childhood injury in rural areas of Victoria is poorly understood. Currently only data on those children transferred from smaller hospital settings to larger settings appear in existing government datasets, significantly underestimating the characteristics of injury.

METHODS

Detailed emergency presentation data (Victorian Emergency Minimum Dataset [VEMD] and non-VEMD) that makes up the Rural Acute Hospital Data Register database was collected and compared among children (aged 0-14 years) who have a principal diagnosis of injury.

RESULTS

Of the 8647 episodes of care identified for injured children aged 0-14 years, 3257 children were managed initially at smaller hospitals that do not report episode data to existing datasets.

CONCLUSIONS

The Rural Acute Hospital Data Register database captures the presentations at low-resource sites and highlights as much as a 35% deficit in the data that is currently available to inform injury prevention and safety initiatives in Victoria.

Incidence and prevalence of hyperthyroidism: a population-based study in the Piedmont Region, Italy

PURPOSE

Unrecognized and untreated hyperthyroidism leads to serious clinical complications with adverse outcomes for patients and increasing costs for the health care system. Hence, adequate knowledge of the epidemiological features of such condition is desirable to plan effective interventions. The aim of our study was to estimate incidence and prevalence of hyperthyroidism in the mildly iodine-deficient Italian Region of Piedmont.

METHODS

A retrospective cohort study was conducted using Administrative Health Databases of the Piedmont Region, Italy (2012-2018). Hyperthyroidism cases were defined as the subjects who had at least one of the following claims: (i) hospital discharge records with hyperthyroidism diagnosis code; (ii) exemption from co-payment for hyperthyroidism; (iii) prescription of one of the following medications: methimazole, propylthiouracil, or potassium perchlorate.

RESULTS

The overall prevalence was 756 per 100,000 inhabitants [95% CI 748-764], and the overall incidence was 81 per 100,000-person year [95% CI 80-82]. The prevalence and incidence increased with age and were two-fold higher among women than men. Women also showed two distinct peaks in incidence at the age of 30 and 50; after the age of 60, the trend became similar between sexes. With regard to the geographic distribution, an increasing gradient of incidence was observed from the northern to the south-western areas of the Region.

CONCLUSION

This is the first Italian study based on health databases to estimate the incidence and prevalence of hyperthyroidism in the general population. This approach can represent an inexpensive and simple method to monitor patterns of hyperthyroidism in iodine-deficient areas.