Visualising linked health data to explore health events around preventable hospitalisations in NSW Australia

Objective: To explore patterns of health service use in the lead-up to, and following, admission for a ‘preventable’ hospitalisation. Setting: 266 950 participants in the 45 and Up Study, New South Wales (NSW) Australia Methods: Linked data on hospital admissions, general practitioner (GP) visits and other health events were used to create visual representations of health service use. For each participant, health events were plotted against time, with different events juxtaposed using different markers and panels of data. Various visualisations were explored by patient characteristics, and compared with a cohort of non-admitted participants matched on sociodemographic and health characteristics. Health events were displayed over calendar year and in the 90 days surrounding first preventable hospitalisation. Results: The visualisations revealed patterns of clustering of GP consultations in the lead-up to, and following, preventable hospitalisation, with 14% of patients having a consultation on the day of admission and 27% in the prior week. There was a clustering of deaths and other hospitalisations following discharge, particularly for patients with a long length of stay, suggesting patients may have been in a state of health deterioration. Specialist consultations were primarily clustered during the period of hospitalisation. Rates of all health events were higher in patients admitted for a preventable hospitalisation than the matched non-admitted cohort. Conclusions: We did not find evidence of limited use of primary care services in the lead-up to a preventable hospitalisation, rather people with preventable hospitalisations tended to have high levels of engagement with multiple elements of the healthcare system. As such, preventable hospitalisations might be better used as a tool for identifying sicker patients for managed care programmes. Visualising longitudinal health data was found to be a powerful strategy for uncovering patterns of health service use, and such visualisations have potential to be more widely adopted in health services research

Using weighted hospital service area networks to explore variation in preventable hospitalization

Objective: To demonstrate the use of multiple-membership multilevel models, which analytically structure patients in a weighted network of hospitals, for exploring between-hospital variation in preventable hospitalizations. Data Sources: Cohort of 267,014 people aged over 45 in NSW, Australia. Study Design: Patterns of patient flow were used to create weighted hospital service area networks (weighted-HSANs) to 79 large public hospitals of admission. Multiple-membership multilevel models on rates of preventable hospitalization, modeling participants structured within weighted-HSANs, were contrasted with models clustering on 72 hospital service areas (HSAs) that assigned participants to a discrete geographic region. Data Collection/Extraction Methods: Linked survey and hospital admission data. Principal Findings: Between-hospital variation in rates of preventable hospitalization was more than two times greater when modeled using weighted-HSANs rather than HSAs. Use of weighted-HSANs permitted identification of small hospitals with particularly high rates of admission and influenced performance ranking of hospitals, particularly those with a broadly distributed patient base. There was no significant association with hospital bed occupancy. Conclusion: Multiple-membership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. Weighted-HSANs have broad potential application in health services research and can be used across methods for creating patient catchments

Do hospitals influence geographic variation in admission for preventable hospitalisation? A data linkage study in New South Wales, Australia

Objective: Preventable hospitalisations are used internationally as a performance indicator for primary care, but the influence of other health system factors remains poorly understood. This study investigated between-hospital variation in rates of preventable hospitalisation. Setting: Linked health survey and hospital admissions data for a cohort study of 266 826 people aged over 45 years in the state of New South Wales, Australia. Method: Between-hospital variation in preventable hospitalisation was quantified using cross-classified multiple-membership multilevel Poisson models, adjusted for personal sociodemographic, health and area-level contextual characteristics. Variation was also explored for two conditions unlikely to be influenced by discretionary admission practice: emergency admissions for acute myocardial infarction (AMI) and hip fracture. Results: We found significant between-hospital variation in adjusted rates of preventable hospitalisation, with hospitals varying on average 26% from the state mean. Patients served more by community and multipurpose facilities (smaller facilities primarily in rural areas) had higher rates of preventable hospitalisation. Community hospitals had the greatest between-hospital variation, and included the facilities with the highest rates of preventable hospitalisation. There was comparatively little between-hospital variation in rates of admission for AMI and hip fracture. Conclusions: Geographic variation in preventable hospitalisation is determined in part by hospitals, reflecting different roles played by community and multipurpose facilities, compared with major and principal referral hospitals, within the community. Care should be taken when interpreting the indicator simply as a performance measure for primary care

Closing the Aboriginal child injury gap: targets for injury prevention

Objective: To describe the leading mechanisms of hospitalised unintentional injury in Australian Aboriginal children and identify the injury mechanisms with the largest inequalities between Aboriginal and non-Aboriginal children. Methods: We used linked hospital and mortality data to construct a whole of population birth cohort including 1,124,717 children (1,088,645 non-Aboriginal and 35,749 Aboriginal) born in the state of New South Wales (NSW), Australia, between 1 July 2000 and 31 December 2012. Injury hospitalisation rates were calculated per person years at risk for injury mechanisms coded according to the ICD10-AM classification. Results: The leading injury mechanisms in both groups of children were falls from playground equipment. For 66 of the 69 injury mechanisms studied, Aboriginal children had a higher rate of hospitalisation compared with non-Aboriginal children. The largest relative inequalities were observed for injuries due to exposure to fire and flame, and the largest absolute inequalities for injuries due to falls from playground equipment. Conclusion: Aboriginal children in NSW experience a significant higher burden of unintentional injury compared with their non Aboriginal counterparts. Implications for Public Health: We suggest the implementation of targeted injury prevention measures aimed at injury mechanism and age groups identified in this study

Sociodemographic and Health Characteristics, Rather Than Primary Care Supply, are Major Drivers of Geographic Variation in Preventable Hospitalizations in Australia

ACKNOWLEDGMENTS: The authors thank the many thousands of people participating in the 45 and Up Study. The authors also thank the Sax Institute, the NSW Ministry of Health, and the NSW Register of Births, Deaths, and Marriages for allowing access to the data, and the Centre for Health Record Linkage for conducting the probabilistic linkage of records.Peer reviewedPublisher PD

Cumulative incidence of child protection system contacts among a cohort of Western Australian Aboriginal children born 2000 to 2013

Background: Reducing the over-representation of Aboriginal children in the child protection system is a key target for the Australian government. Objective: We aimed to provide more recent evidence on the population-level cumulative incidence of contacts for Aboriginal children with child protective services (CPS) in Western Australia (WA). Participants and Setting: Linked administrative data was provided for WA CPS between 2000 and 2015 for 33,709 Aboriginal children born in WA between 2000 and 2013. Methods: Descriptive summaries and cumulative incidence estimates were used to examine changes in CPS contact trends over time and within sibling groups. Results: There was an increase in early-childhood contacts for children born more recently, with 7.6 % and 2.3 % of children born in 2000–2001 having a notification and placement in out-of-home care by age one, respectively, compared to 15.1 % and 4.3 % of children born in 2012–2013. Among sibling groups where at least one sibling had a CPS contact, approximately half of children had their first contacts on the same date as another sibling. For children born after one of their siblings had been placed in out-of-home care, 31.9 % had themselves been placed in out-of-home care by age one. Conclusions: Multiple children tend to be placed into out-of-home care when at least one sibling is, which is likely to have a significant impact on families affected. The additional risk of placement also carries over to children born after the first removal in a sibling group, highlighting the need for further support to prevent future removals

AusTraits – a curated plant trait database for the Australian flora

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.Daniel Falster ... Raymond J. Carpenter ... Matthew D. Denton ... Gregory R. Guerin ... Juergen Kellermann ... Samantha E. Munroe ... Benjamin D. Sparrow ... et al

Is analysing the nitrogen use at the plant canopy level a matter of choosing the right optimization criterion?

Optimization theory in combination with canopy modeling is potentially a powerful tool for evaluating the adaptive significance of photosynthesis-related plant traits. Yet its successful application has been hampered by a lack of agreement on the appropriate optimization criterion. Here we review how models based on different types of optimization criteria have been used to analyze traits—particularly N reallocation and leaf area indices—that determine photosynthetic nitrogen-use efficiency at the canopy level. By far the most commonly used approach is static-plant simple optimization (SSO). Static-plant simple optimization makes two assumptions: (1) plant traits are considered to be optimal when they maximize whole-stand daily photosynthesis, ignoring competitive interactions between individuals; (2) it assumes static plants, ignoring canopy dynamics (production and loss of leaves, and the reallocation and uptake of nitrogen) and the respiration of nonphotosynthetic tissue. Recent studies have addressed either the former problem through the application of evolutionary game theory (EGT) or the latter by applying dynamic-plant simple optimization (DSO), and have made considerable progress in our understanding of plant photosynthetic traits. However, we argue that future model studies should focus on combining these two approaches. We also point out that field observations can fit predictions from two models based on very different optimization criteria. In order to enhance our understanding of the adaptive significance of photosynthesis-related plant traits, there is thus an urgent need for experiments that test underlying optimization criteria and competing hypotheses about underlying mechanisms of optimization

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Factors associated with paradoxical immune response to antiretroviral therapy in HIV infected patients: a case control study

<p>Abstract</p> <p>Background</p> <p>A paradoxical immunologic response (PIR) to Highly Active Antiretroviral Therapy (HAART), defined as viral suppression without CD4 cell-count improvement, has been reported in the literature as 8 to 42%, around 15% in most instances. The present study aims to determine, in a cohort of HIV infected patients in Brazil, what factors were independently associated with such a discordant response to HAART.</p> <p>Methods</p> <p>A case-control study (1:4) matched by gender was conducted among 934 HIV infected patients on HAART in Brazil. Cases: patients with PIR, defined as CD4 < 350 cells/mm³(hazard ratio for AIDS or death of at least 8.5) and undetectable HIV viral load on HAART for at least one year. Controls: similar to cases, but with CD4 counts ≥ 350 cells/mm³. Eligibility criteria were applied. Data were collected from medical records using a standardized form. Variables were introduced in a hierarchical logistic regression model if a p-value < 0.1 was determined in a bivariate analysis.</p> <p>Results</p> <p>Among 934 patients, 39 cases and 160 controls were consecutively selected. Factors associated with PIR in the logistic regression model were: total time in use of HAART (OR 0.981; CI 95%: 0.96-0.99), nadir CD4-count (OR 0.985; CI 95%: 0.97-0.99), and time of undetectable HIV viral load (OR 0.969; CI 95%: 0.94-0.99).</p> <p>Conclusions</p> <p>PIR seems to be related to a delay in the management of immunodeficient patients, as shown by its negative association with nadir CD4-count. Strategies should be implemented to avoid such a delay and improve the adherence to HAART as a way to implement concordant responses.</p