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When chronic conditions become emergencies - a report from regional Queensland.

ABSTRACT: To describe chronic conditions and injuries as a proportion of total emergency presentations to a large public hospital in regional Queensland, and to investigate differences in presentation rates associated with Indigenous status.Cross-sectional analysis using Emergency Department Information System data between 1 July 2012 and 30 June 2014.Regional Queensland, Australia.A total of 95 238 emergency presentations were generated by 50 083 local residents living in the 10 statistical local areas (SLAs) immediately around the hospital.Emergency presentations for chronic conditions and injuries identified from discharge ICD-10-AM principal diagnosis. Age-standardised presentation rates were calculated using the Australian 2001 reference population.Approximately half of all presentations were for chronic conditions (20.2%) and injuries (28.8%). Two-thirds of all chronic condition presentations were for mental and behavioural disorders (34.6%) and circulatory diseases (33.2%). Head injuries accounted for the highest proportion of injuries (18.9%). Age-standardised rates for major diagnostic groups were consistently higher for Indigenous residents, whose presentations were lower in mean age (95% CI) by 7.7 (7.3-8.1) years, 23% less likely to be potentially avoidable GP-type presentations [RR (95% CI) = 0.77 (0.75-0.80)], 30% more likely to arrive by ambulance [1.31 (1.28-1.33)] and 11% more likely to require hospital admission [1.11 (1.08-1.13)].Opportunities exist to enhance current coordinated hospital avoidance and primary health services in regional Queensland targeting common mental and circulatory disorders, especially for Indigenous Australians.

SUBMITTER: Harriss LR

PROVIDER: S-EPMC5215535 | biostudies-other | 2016 Dec

REPOSITORIES: biostudies-other

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When chronic conditions become emergencies - a report from regional Queensland.

Harriss Linton R LR Thompson Fintan F Dey Arindam A Mills Jane J Watt Kerrianne K McDermott Robyn R

The Australian journal of rural health 20160905 6

<h4>Objective</h4>To describe chronic conditions and injuries as a proportion of total emergency presentations to a large public hospital in regional Queensland, and to investigate differences in presentation rates associated with Indigenous status.<h4>Design</h4>Cross-sectional analysis using Emergency Department Information System data between 1 July 2012 and 30 June 2014.<h4>Setting</h4>Regional Queensland, Australia.<h4>Participants</h4>A total of 95 238 emergency presentations were generate ...[more]

PMID: 27596837

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Project description:ObjectivesEvolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.MethodsA Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.ResultsTwo major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.ConclusionsThese findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

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When chronic conditions become emergencies - a report from regional Queensland.

Publications

When chronic conditions become emergencies - a report from regional Queensland.

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