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

Inequalities in pediatric avoidable hospitalizations between Aboriginal and non-Aboriginal children in Australia: a population data linkage study

Background: Australian Aboriginal children experience a disproportionate burden of social and health disadvantage. Avoidable hospitalizations present a potentially modifiable health gap that can be targeted and monitored using population data. This study quantifies inequalities in pediatric avoidable hospitalizations between Australian Aboriginal and non-Aboriginal children. Methods: This statewide population-based cohort study included 1 121 440 children born in New South Wales, Australia, between 1 July 2000 and 31 December 2012, including 35 609 Aboriginal children. Using linked hospital data from 1 July 2000 to 31 December 2013, we identified pediatric avoidable, ambulatory care sensitive and non-avoidable hospitalization rates for Aboriginal and non-Aboriginal children. Absolute and relative inequalities between Aboriginal and non-Aboriginal children were measured as rate differences and rate ratios, respectively. Individual-level covariates included age, sex, low birth weight and/or prematurity, and private health insurance/patient status. Area-level covariates included remoteness of residence and area socioeconomic disadvantage. Results: There were 365 386 potentially avoidable hospitalizations observed over the study period, most commonly for respiratory and infectious conditions; Aboriginal children were admitted more frequently for all conditions. Avoidable hospitalization rates were 90.1/1000 person-years (95 % CI, 88.9–91.4) in Aboriginal children and 44.9/1000 person-years (44.8–45.1) in non-Aboriginal children (age and sex adjusted rate ratio = 1.7 (1.7–1.7)). Rate differences and rate ratios declined with age from 94/1000 person-years and 1.9, respectively, for children aged <2 years to 5/1000 person-years and 1.8, respectively, for ages 12- < 14 years. Findings were similar for the subset of ambulatory care sensitive hospitalizations, but in contrast, non-avoidable hospitalization rates were almost identical in Aboriginal (10.1/1000 person-years, (9.6–10.5)) and non-Aboriginal children (9.6/1000 person-years (9.6–9.7)). Conclusions: We observed substantial inequalities in avoidable hospitalizations between Aboriginal and non-Aboriginal children regardless of where they lived, particularly among young children. Policy measures that reduce inequities in the circumstances in which children grow and develop, and improved access to early intervention in primary care, have potential to narrow this gap

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

Claims-based measures of continuity of care have non-linear associations with health: data linkage study

Background Continuity of care (CoC) is considered a central element of good primary care and is often measured using medical claims data. Possible values of CoC depend on the number of claims which is related to health status. This study investigated the relationships between CoC and health status and risk of emergency hospitalisation. Methods Health insurance claims for consultations with general practitioners (GPs) in the 24 months following entry to the 45 and Up Study were used to calculate usual provider continuity (UPC) and the Continuity of Care Index (CoC Index). Relationships of CoC with number of claims, self-rated health and emergency hospitalisation were investigated using descriptive statistics and logistic regression models. Results Both measures of CoC were strongly related to number of claims and to measures of health status, which were in turn highly associated. Multivariable logistic regression models showed a weak positive relationship between CoC and odds of emergency hospitalisation for those with CoC less than 1, while individuals with perfect CoC had significantly lower odds of hospitalisation compared to all other categories of CoC. However, analyses stratified by, or adjusting for, number of claims showed no clear associations between CoC and risk of hospitalisation. Conclusions The pattern of association between CoC categories and emergency hospitalisation was non-linear and was confounded by the effect of number of claims. Future studies should apply caution in using claims-based measures of CoC as a continuous variable or employing an arbitrary cut-point, and should adjust for number of claims

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

BAAD: a Biomass And Allometry Database for woody plants

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub‐sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross‐section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world\u27s vegetation

Using Australia’s National Data Linkage Demonstration Project (NDLDP) to improve cardiac care: Towards a national, whole-of-population linked data resource for evidence-informed health policy

Introduction In Australia, the Commonwealth, State and Territory governments are each responsible for specific aspects of health care. Historically, cross-jurisdictional health data have not been linked routinely, resulting in significant blind spots in our understanding of the interplay between hospital and primary care, a major impediment to evidence-informed health policy. Objectives and Approach In December 2016, the Australian Health Ministers’ Advisory Council approved the NDLDP, to establish the value of national linked data to inform health planning and policy. The Australian Institute of Health and Welfare (AIHW) linked five years of hospital, emergency department, pharmaceutical dispensing, medical services claims and mortality data for Australia’s two most populous states (New South Wales and Victoria). The Victorian Agency for Health Innovation (VAHI) is leading the ‘Delivering better cardiac outcomes: Primary, specialist and hospital care’ project to demonstrate the value of the collection in identifying evidence-practice gaps and driving change in cardiac care. Results The NDLDP combined data for over 10 million individuals with over 7 billion records of health transactions, utilising a new strategy for confidentialising dates to protect patient privacy. The NDLDP is governed by a Steering Committee; the AIHW is the data custodian approving outputs from analyses within a secure host environment. VAHI established a Project Steering Committee to oversee roll-out, governance and capacity building of the approved cardiac project. The VAHI project was instigated by evidence that best-practice pharmacological treatments for cardiac care are underutilised in Australia, but with no quantification of the population-level extent of this gap. The project quantified significant variations and underuse of post-discharge pharmacological care for patients admitted to hospital with key cardiac conditions, including atrial fibrillation and acute myocardial infarction. Conclusion/Implications This collaboration between government, clinical networks and academic researchers demonstrated this novel data linkage can enable evaluation of patient care pathways across both hospital and community-based services. These linked data resources provide essential information to investigate variation in care in Australia and improve care integration in cardiac care and beyond

BAAD: A biomass and allometry database for woody plants.

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01– 100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world’s vegetation.EEA Santa CruzFil: Falster, Daniel S. Macquarie University. Biological Sciences; Australia.Fil: Duursma, Remko A. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Ishihara, Masae I. Hiroshima University. Graduate School for International Development and Cooperation; Japón.Fil: Barneche, Diego R. Macquarie University. Biological Sciences; Australia.Fil: FitzJohn, Richard G. Macquarie University. Biological Sciences; Australia.Fil: Vårhammar, Angelica. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Aiba, Masahiro. Tohoku University. Graduate School of Life Sciences; Japón.Fil: Ando, Makoto. Kyoto University. Field Science Education and Research Center; JapónFil: Anten, Niels. Centre for Crop Systems Analysis; Países BajosFil: Aspinwall, Michael J. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Gargaglione Verónica Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Gargaglione Verónica Beatriz. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: York, Robert A. University of California Berkeley. Center for Forestry; Estados Unido