Show me the money! Associations between tree canopy and hospital costs in cities for cardiovascular disease events in a longitudinal cohort study of 110,134 participants.

Health benefits from urban greening are assumed to translate into reduced healthcare expenditure, yet few studies have tested this. A total of 110,134 participants in the Sax Institute's 45 and Up Study in the Australian cities of Sydney, Newcastle, or Wollongong were linked with hospital cost data for cardiovascular disease (CVD) events (e.g., acute myocardial infarctions) up to 30 June 2018. Associations between percentages of total green space, tree canopy, and open grass within 1.6 km of participants homes and annual per person measured CVD-related hospital costs were analysed using generalised linear model (GLM) with gamma density as a component of a two-part mixture model, adjusting for confounders. Overall, 26,243 participants experienced a CVD-related hospitalisation. Incidence was lower among participants with 10 % more tree canopy (OR 0.98, 95 %CI 0.96, 0.99), but not with higher total green space or open grass percentages. Total costs of hospitalisations per year were lower with 10 % more tree canopy (means ratio 0.96, 95 %CI 0.95, 0.98), but also higher with 10 % more open grass (means ratio 1.04, 95 %CI 1.02, 1.06). It was estimated that raising tree canopy cover to 30 % or more for individuals with currently less than 10 % could lead to a within-sample annual saving per person of AU$ 193 overall and AU$ 569 for those who experienced one or more CVD-related hospital admissions. This projects to an estimated annual health sector cost reduction of AU$ 19.3 million per 100,000 individuals for whom local tree canopy cover is increased from less than 10 % to 30 % or higher. In conclusion, this longitudinal study is among the first to analyse measured healthcare cost data in relation to urban green space in general, and with differentiation between major types of greenery relevant to urban planning policies in cities around the world. In sum, this study advances an increasingly important and international focus of research by reporting on the lower burden of CVD and fewer associated hospitalisations stemming from upstream investments that protect and restore urban tree canopy, which not only translates into substantial reduced costs for the health sector, but also helps to create regenerative cities and flourishing communities.

Is urban green space associated with lower mental healthcare expenditure?

INTRODUCTION: While the evidence of mental health benefits from investing in green space accumulates, claims of reduced healthcare expenditure are rarely supported by evidence from analyses of actual healthcare data. Additionally, the question of 'who pays?' has been ignored. We addressed these gaps using person-level data in three Australian cities. METHODS: 55,339 participants with a mean follow-up time of 4.97 years in the Sax Institute's 45 and Up Study (wave 2, collected 2012-2015) were linked to fee-for-service records of antidepressant prescriptions and talking therapy subsidised by the Australian Government (including data on per unit fee, state subsidy, and individual co-payment). Total green space, tree canopy and open grass within 1.6 km road network distances were linked to each participant. Multilevel logistic, negative binomial, and generalised linear models with gamma distribution adjusted for demographic and socioeconomic confounders were used to assess association between each green space variable and prescribing/referral and costs of antidepressants and talking therapy. RESULTS: Prescription of at least one course of antidepressants occurred for 20.01% (n = 11,071). Referral for at least one session of talking therapy occurred in 8.95% (n = 4954). 13,482 participants (24.4%) had either a prescription or a referral. A 10% increase in green space was associated with higher levels of antidepressant prescribing (e.g. incident rate ratio (IRR) = 1.06, 95%CI = 1.04-1.08). Tree canopy was not associated with antidepressant prescribing or referrals for talking therapy. Open grass was associated with higher odds (OR = 1.17, 95%CI = 1.13-1.20) and counts (IRR = 1.05, 95%CI = 1.02-1.08) of antidepressant prescriptions. Open grass was also associated with lower odds (OR = 0.87, 95%CI = 0.82-0.92) and counts (IRR = 0.93, 95%CI = 0.90-0.96) of talking therapy referrals. Open grass was associated with higher total and mean per-person levels of expenditure on antidepressant prescriptions. CONCLUSION: Although green space supports mental health, these unexpected results provide pause for reflection on whether greening strategies will always result in purported reductions in mental healthcare expenditure.

Multilevel modeling of geographic variation in general practice consultations.

OBJECTIVE: To test relatively simple and complex models for examining model fit, higher-level variation in, and correlates of, GP consultations, where known nonhierarchical data structures are present. SETTING: New South Wales (NSW), Australia. DESIGN: Association between socioeconomic circumstances and geographic remoteness with GP consultation frequencies per participant was assessed using single-level, hierarchical, and multiple membership cross-classified (MMCC) models. Models were adjusted for age, gender, and a range of socioeconomic and demographic confounds. DATA COLLECTION/EXTRACTION METHODS: A total of 261,930 participants in the Sax Institute's 45 and Up Study were linked to all GP consultation records (Medicare Benefits Schedule; Department of Human Services) within 12 months of baseline (2006-2009). PRINCIPAL FINDINGS: Deviance information criterion values indicated the MMCC negative binomial regression was the best fitting model, relative to an MMCC Poisson equivalent and simpler hierarchical and single-level models. Between-area variances were relatively consistent across models, even when between GP variation was estimated. Lower rates of GP consultation outside of major cities were only observed once between-GP variation was assessed simultaneously with between-area variation in the MMCC models. CONCLUSIONS: Application of the MMCC model is necessary for estimation of variances and effect sizes in sources of big data on primary care in which complex nonhierarchical clustering by geographical area and GP is present.

A new clinical complexity model for the Australian Refined Diagnosis Related Groups.

BACKGROUND: The Australian Refined Diagnosis Related Groups (AR-DRG) underwent a major review in 2014 with changes implemented in Version 8.0 of the classification. The core to the changes was the development of a new methodology to estimate the Diagnosis Complexity Level (DCL) and to aggregate the complexity level of individual diagnoses to the complexity of an entire episode, resulting in an Episode Clinical Complexity Score (ECCS). This paper provides an overview of the new methodology and its application in Version 8.0. METHOD: The AR-DRG V8.0 refinement project was overseen by a Classifications Clinical Advisory Group and a Diagnosis Related Groups (DRG) Technical Group. Admitted Patient Care National Minimum Dataset and the National Hospital Cost Data Collection were used for complexity modelling and analysis. RESULT: In total, Version 8.0 comprised 807 DRGs, including 3 error DRGs. Of the 321 Adjacent DRGs (ADRGs) that had a split, 315 ADRGs used ECCS as the only splitting variable while the remaining 6 ADRGs used splitting variables other than ECCS: 2 used age and 4 used transfer. DISCUSSION AND CONCLUSION: A new episode clinical complexity (ECC) model was developed and introduced in AR-DRG V8.0, replacing the original model introduced in the 1990s. Clear AR-DRG structure principles were established for revising the system. The new complexity model is conceptually based and statistically derived, and results in an improved relationship with actual variations in resource use due to episode complexity.