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

Mental health literacy: a cross-cultural approach to knowledge and beliefs about depression, schizophrenia and generalized anxiety disorder

Many families worldwide have at least one member with a behavioral or mental disorder, and yet the majority of the public fails to correctly recognize symptoms of mental illness. Previous research has found that Mental Health Literacy (MHL)—the knowledge and positive beliefs about mental disorders—tends to be higher in European and North American cultures, compared to Asian and African cultures. Nonetheless quantitative research examining the variables that explain this cultural difference remains limited. The purpose of our study was fourfold: (a) to validate measures of MHL cross-culturally, (b) to examine the MHL model quantitatively, (c) to investigate cultural differences in the MHL model, and (d) to examine collectivism as a predictor of MHL. We validated measures of MHL in European American and Indian samples. The results lend strong quantitative support to the MHL model. Recognition of symptoms of mental illness was a central variable: greater recognition predicted greater endorsement of social causes of mental illness and endorsement of professional help-seeking as well as lesser endorsement of lay help-seeking. The MHL model also showed an overwhelming cultural difference; namely, lay help-seeking beliefs played a central role in the Indian sample, and a negligible role in the European American sample. Further, collectivism was positively associated with causal beliefs of mental illness in the European American sample, and with lay help-seeking beliefs in the Indian sample. These findings demonstrate the importance of understanding cultural differences in beliefs about mental illness, particularly in relation to help-seeking beliefs

Assessing preventable hospitalisation indicators (APHID): protocol for a data-linkage study using cohort study and administrative data

Introduction Potentially preventable hospitalisation (PPH) has been adopted widely by international health systems as an indicator of the accessibility and overall effectiveness of primary care. The Assessing Preventable Hospitalisation InDicators (APHID) study will validate PPH as a measure of health system performance in Australia and Scotland. APHID will be the first large-scale study internationally to explore longitudinal relationships between primary care and PPH using detailed person-level information about health risk factors, health status and health service use. Methods and analysis APHID will create a new longitudinal data resource by linking together data from a large-scale cohort study (the 45 and Up Study) and prospective administrative data relating to use of general practitioner (GP) services, dispensing of pharmaceuticals, emergency department presentations, hospital admissions and deaths. We will use these linked person-level data to explore relationships between frequency, volume, nature and costs of primary care services, hospital admissions for PPH diagnoses, and health outcomes, and factors that confound and mediate these relationships. Using multilevel modelling techniques, we will quantify the contributions of person-level, geographic-level and service-level factors to variation in PPH rates, including socioeconomic status, country of birth, geographic remoteness, physical and mental health status, availability of GP and other services, and hospital characteristics. Ethics and dissemination Participants have consented to use of their questionnaire data and to data linkage. Ethical approval has been obtained for the study. Dissemination mechanisms include engagement of policy stakeholders through a reference group and policy forum, and production of summary reports for policy audiences in parallel with the scientific papers from the study.</p

Age differences in mental health literacy

BACKGROUND: The community's knowledge and beliefs about mental health problems, their risk factors, treatments and sources of help may vary as a function of age. METHODS: Data were taken from an epidemiological survey conducted during 2003–2004 with a national clustered sample of Australian adults aged 18 years and over. Following the presentation of a vignette describing depression (n = 1001) or schizophrenia (n = 997), respondents were asked a series of questions relating to their knowledge and recognition of the disorder, beliefs about the helpfulness of treating professionals and medical, psychological and lifestyle treatments, and likely causes. RESULTS: Participant age was coded into five categories and cross-tabulated with mental health literacy variables. Comparisons between age groups revealed that although older adults (70+ years) were poorer than younger age groups at correctly recognising depression and schizophrenia, young adults (18–24 years) were more likely to misidentify schizophrenia as depression. Differences were also observed between younger and older age groups in terms of beliefs about the helpfulness of certain treating professionals and medical and lifestyle treatments for depression and schizophrenia, and older respondents were more likely to believe that schizophrenia could be caused by character weakness. CONCLUSION: Differences in mental health literacy across the adult lifespan suggest that more specific, age appropriate messages about mental health are required for younger and older age groups. The tendency for young adults to 'over-identify' depression signals the need for awareness campaigns to focus on differentiation between mental disorders

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. Results: A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Conclusions: Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy

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

Factors associated with service use for young adolescents with mental health problems: findings from an Australian longitudinal study

The objective of the study wasto identify factors associated with use of services for adolescent mental health problems in an Australian community-based sample. Logistic regression analysis was conducted on data collected from 636 parents and their adolescent child to identify individual and family variables predicting parent report of service use for mental health problems in the adolescent 12 months later. The services most reported by parents to have been accessed were schoolbased ones. Multivariate analysis found that the following were associated with service use 12 months later: the adolescent being female, parent report of peer problems and hyperactivity, single-parent household, the parent being Australian born, and prior service use by the adolescent. Parental overcontrol was associated with reducedlikelihood of service use at followup. No association was found between service use at follow-up and parent gender, socioeconomic status, number of siblings, parent psychopathology, family social connectedness, and prior service use by the parent. No association was also found for family environment factors, parental attachment, or for the adolescent&rsquo;s emotional competence or use of social support. The results indicate that families provide a potential target for interventions aimed at increasing use of professional services for adolescent mental health problems

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 &lt;2 years to 5/1000 person-years and 1.8, respectively, for ages 12- &lt; 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

Validating Synthetic Health Datasets for Longitudinal Clustering

This paper appeared at the Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2013), Adelaide, Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol.142. K. Gray and A. Koronios, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.Clustering methods partition datasets into subgroups with some homogeneous properties, with information about the number and particular characteristics of each subgroup unknown a priori. The problem of predicting the number of clusters and quality of each cluster might be overcome by using cluster validation methods. This paper presents such an approach in-corporating quantitative methods for comparison be-tween original and synthetic versions of longitudinal health datasets. The use of the methods is demon-strated by using two different clustering algorithms, K-means and Latent Class Analysis, to perform clus-tering on synthetic data derived from the 45 and Up Study baseline data, from NSW in Australia