Australia's seizure divide - indigenous versus non-indigenous seizure hospitalization

Epilepsy in children exposed to family and domestic violence in the first 5 years of life

AIM

To investigate childhood (0-18 years) hospitalisation and emergency department (ED) contacts for epilepsy in Western Australian (WA) children exposed to family and domestic violence (FDV) pre 5 years of age compared to children with no FDV exposure.

METHODS

A retrospective, population-based cohort study included children born 1987-2010 who were identified as being exposed to FDV (n = 7018) from two sources: WA Police Information Management System and WA Hospital Morbidity Data Collection (HMDC) and a non-exposed comparison group (n = 41 996). Epilepsy contact was identified in HMDC and ED Data Collection records. Cox regression was used to estimate the adjusted and unadjusted hazard ratio and 95% confidence interval (CI) for epilepsy contact; adjustment was made for a range of demographic characteristics known to impact health outcomes. Analyses were stratified by Aboriginal and Torres Strait Islander status to account for higher rates of FDV and epilepsy hospital admissions in Aboriginal and Torres Strait Islander children.

RESULTS

Children exposed to FDV had a 62% (HR 1.62, 95% CI: 1.33-1.98) increased risk of epilepsy contact than non-exposed counterparts. Furthermore, the children exposed to FDV had a 50% longer average hospital stay for epilepsy than non-exposed children (4.7 days vs. 3 days, P = 0.006). When stratified by Aboriginal status, we found that Aboriginal children exposed to FDV stayed (on average) 2 days longer in hospital for epilepsy than their non-exposed counterparts (5.1 days vs. 3.1 days, P = 0.018).

CONCLUSIONS

FDV exposure in early childhood is associated with increased risk of requiring secondary health care and longer hospital stays for childhood epilepsy.

Effect of an ageing population on services for the elderly in the Northern Territory

Objective The aim of the present study was to describe the elderly population of the Northern Territory (NT), explore the challenges of delivering aged care services to this population and implications for the acute care sector. Methods Data gathered from a variety of sources were used to describe the demographic and health profile of elderly Territorians, the aged care structure and services in the NT, and admission trends of elderly patients in NT hospitals. Information regarding NT community and residential aged care services was sourced from government reports. NT public hospital admissions from 2001 to 2015 were adjusted by the estimated Aboriginal and non-Aboriginal populations. Results In 2015, elderly people constituted 9.2% of the NT population and this number is predicted to increase. Between 2001 and 2015, the number and rate of elderly admissions to NT public hospitals increased significantly. Compared with other jurisdictions, aged care in the NT is dominated by community services, which are of limited scope. Important geographical and economic factors affect the availability of residential aged care beds. This, in turn, affects the ability of elderly people to transition from hospital settings. Conclusions The NT has a relatively small but growing elderly population with increasing needs. This population is markedly different compared with its counterparts in other Australian states and territories, but receives aged care services based on national policies. Recent changes to community-based services and increases in residential beds should improve services and care, although remaining challenges and gaps need to be addressed. What is known about the topic? Increasing health and care needs of elderly people will place significant stress across the health and aged care system. In Australia, most aged care services are apportioned and funded under a national system. The NT has a markedly different population profile compared with the rest of Australia, which gives rise to unique considerations, but its aged care structure is based on nationally developed policies. What does this paper add? Elderly people in the NT are increasingly using acute care services. Aged care services in the NT have higher ratios of community-based services to residential aged care facilities (RACF) as a consequence of a 'younger' cohort of Aboriginal elderly people who live remotely. In addition, economic factors affect the low number of RACF places. As evidenced in past years, a small pool of beds can adversely affect the numbers and length of stay of elderly people waiting in hospitals. What are the implications for practitioners? The NT has a small but growing population of elderly people, which will place an increasing burden on acute care services that are ill equipped to manage their specific needs. Recent RACF and flexible care bed approvals may alleviate past difficulties to transition hospital patients awaiting RACF placement. Significant changes at the national level to community-based care services that increase flexibility for providers may bring about better outcomes for remote elderly recipients. However, high costs and issues with remote servicing will remain. Psychogeriatrics remains a major underserviced area in the NT with no prospective solution.

Influence of a Comorbid Diagnosis of Seizure on 30-Day Readmission Rates Following Hospitalization for an Index Stroke

OBJECTIVE

To examine the association of a comorbid seizure diagnosis with early hospital readmission rates following an index hospitalization for stroke in the United States.

METHODS

Retrospective analysis of the 2014 National Readmission Database. The study population included adult patients (age >18 years old) with stroke, identified using the International Classification of Disease Ninth Revision, Clinical Modification (ICD-9-CM) codes 433.X1, 434.X1, and 436 for ischemic stroke as well as 430, 431, 432.0, 432.1, and 432.9 for hemorrhagic stroke. A subgroup of patients with a secondary discharge diagnosis of seizures was identified using the ICD-9-CM codes 780.39 and 345.X. We computed all-cause 30-day readmission rates for all strokes and by stroke type (ischemic versus hemorrhagic). Finally, we used a multivariable logistic regression model to examine the independent association between seizure and readmission by stroke type.

RESULTS

Of 271,148 stroke patients, 6.3% (16,970) had a secondary discharge diagnosis of seizures including 5.0% (11,562) of patients with ischemic stroke and 13.4% (5,409) with hemorrhagic stroke. Overall readmission rate for stroke patients was 11.9% (hemorrhagic stroke: 14.2% versus ischemic strokes: 11.6%). Thirty-day readmission rate was higher in patients with seizures for all strokes (15.6% versus 11.7%, P value <.001), ischemic strokes (15.0% versus11.4%, P value <.001), and hemorrhagic strokes (16.7% versus 13.8%, P value <.001). After adjusting for several patient-specific and healthcare system-specific confounders, hospitalized stroke patients with comorbid seizure diagnosis were more likely than those without seizures to be readmitted within 30 days (OR: 1.20, 95% CI: 1.14-1.25).

CONCLUSION

The presence of a comorbid diagnosis of seizure disorder in a hospitalized stroke patient significantly raises the occurrence of early hospital readmission in the United States.

Readmission after seizure discharge in a nationally representative sample

OBJECTIVE

To determine the 30-day readmission rate after seizure-related discharge in a nationally representative sample, as well as patient, clinical, and hospital characteristics associated with readmission.

METHODS

Retrospective cohort study of adults discharged alive from a nonelective hospitalization for epilepsy or seizure, sampled from the Healthcare Cost and Utilization Project's 2014 Nationwide Readmissions Database. Descriptive statistics and logistic regression models were built to quantify and characterize nonelective readmission within 30 days.

RESULTS

A total of 139,800 admissions met inclusion criteria, of which 15,094 (10.8%) were readmitted within 30 days. Patient characteristics associated with readmission included comorbid disease burden (Elixhauser score 2: adjusted odds ratio [AOR] [95% confidence interval (CI)] 1.38 [1.21-1.57]; Elixhauser score 3: AOR 1.52 [1.34-1.73]; Elixhauser score >4: AOR 2.28 [2.01-2.58] as compared to 1) and participation in public insurance programs (Medicare: AOR 1.39 [1.26-1.54]; Medicaid: AOR 1.39 [1.26-1.54] as compared to private insurance). Adverse events (AOR 1.17 [1.05-1.30]) and prolonged length of stay, as well as nonroutine discharge (AOR 1.32 [1.23-1.42]), were also associated with increased adjusted odds of readmission. The most common primary reason for readmission was epilepsy or convulsion (17%).

CONCLUSIONS

Patients hospitalized with seizure are frequently readmitted. While readmitted patients are more likely to have multiple medical comorbidities, our study demonstrated that inpatient adverse events were also significantly associated with readmission. The most common reason for readmission was seizure or epilepsy. Together, these 2 findings suggest that a proportion of readmissions are related to modifiable care process factors and may therefore be avoidable. Further study into understanding preventable drivers of readmission in this population presents an opportunity to improve patient outcomes and health.

Seizure-related hospital admissions, readmissions and costs: Comparisons with asthma and diabetes in South Australia

PURPOSE

Seizures are listed as an Ambulatory Care Sensitive Condition (ACSC), where, in some cases, hospitalisation may be avoided with appropriate preventative and early management in primary care. We examined the frequencies, trends and financial costs of first and subsequent seizure-related hospital admissions in the adult and paediatric populations, with comparisons to bronchitis/asthma and diabetes admissions in South Australia between 2012 and 2014.

METHODS

De-identified hospital separation data from five major public hospitals in metropolitan South Australia were analysed to determine the number of children and adults admitted for the following Australian Refined Diagnosis Related Groups: seizure related conditions; bronchitis/asthma; and diabetes. Additional data included length of hospital stay and type of admission. Demographic data were analysed to identify whether social determinants influence admission, and a macro costing approach was then applied to calculate the financial costs to the Health Care System.

RESULTS

The rate of total seizure hospitalizations was 649 per 100,000; lower than bronchitis/asthma (751/100,000), yet higher than diabetes (500/100,000). The highest proportions of subsequent separations were recorded by children with seizures regardless of complexity (47% +CSCC; 17% -CSCC) compared with asthma (11% +CSCC; 14% -CSCC) or diabetes (14% +CSCC; 13% -CSCC), and by adults with seizures with catastrophic or severe complications/comorbidity (25%), compared with diabetes (22%) or asthma (14%). The mean cost per separation in both children and adults was highest for diabetes (AU$4438/$7656), followed by seizures (AU$2408/$5691) and asthma (AU$2084/$3295).

CONCLUSIONS

Following the lead of well-developed and resourced health promotion initiatives in asthma and diabetes, appropriate primary care, community education and seizure management services (including seizure clinics) should be targeted in an effort to reduce seizure related hospitalisations which may be avoidable, minimise costs to the health budget, and maximise health care quality.

Development and validation of an epidemiologic case definition of epilepsy for use with routinely collected Australian health data

OBJECTIVES

We report the diagnostic validity of a selection algorithm for identifying epilepsy cases.

STUDY DESIGN AND SETTING

Retrospective validation study of International Classification of Diseases 10th Revision Australian Modification (ICD-10AM)-coded hospital records and pharmaceutical data sampled from 300 consecutive potential epilepsy-coded cases and 300 randomly chosen cases without epilepsy from 3/7/2012 to 10/7/2013. Two epilepsy specialists independently validated the diagnosis of epilepsy. A multivariable logistic regression model was fitted to identify the optimum coding algorithm for epilepsy and was internally validated.

RESULTS

One hundred fifty-eight out of three hundred (52.6%) epilepsy-coded records and 0/300 (0%) nonepilepsy records were confirmed to have epilepsy. The kappa for interrater agreement was 0.89 (95% CI=0.81-0.97). The model utilizing epilepsy (G40), status epilepticus (G41) and ≥1 antiepileptic drug (AED) conferred the highest positive predictive value of 81.4% (95% CI=73.1-87.9) and a specificity of 99.9% (95% CI=99.9-100.0). The area under the receiver operating curve was 0.90 (95% CI=0.88-0.93).

CONCLUSION

When combined with pharmaceutical data, the precision of case identification for epilepsy data linkage design was considerably improved and could provide considerable potential for efficient and reasonably accurate case ascertainment in epidemiological studies.