Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMHs) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well documented in populations of different ethnicities and/or from different geographical regions. METHODS: We investigated how WMHs were associated with vascular risk factors and cognition in both Whites and Asians, using data from five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1946). WMH volumes (whole brain, periventricular, and deep) were quantified with UBO Detector and harmonized using the ComBat model. We also harmonized various vascular risk factors and scores for global cognition and individual cognitive domains. RESULTS: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake, and insufficient physical activity. Hypertension and stroke had stronger associations with WMH volumes in Whites than in Asians. No associations between WMH volumes and cognitive performance were found after correction for multiple testing. CONCLUSION: The current study highlights ethnic differences in the contributions of vascular risk factors to WMHs.

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMH) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well-documented in populations of different ethnicities and/or from different geographical regions. METHOD: Magnetic resonance imaging data of five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1,946) were examined for WMH and their associations with vascular risk factors and cognition. RESULT: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake and insufficient physical activity. Participants with moderate or higher physical activity had less WMH than those who never exercised, but the former two groups did not differ. Hypertension and stroke had stronger associations with WMH volumes in the White, compared to Asian subsample. DISCUSSION: The current study highlighted the ethnic differences in the contributions of vascular risk factors to WMH.

Examining the cost and impact of dosing fees among clients in opioid agonist treatment: Results from a cross-sectional survey of Australian treatment clients.

INTRODUCTION: Opioid agonist treatment (OAT) clients frequently bear costs associated with their treatment, including dosing fees. This study aimed to explore the financial and social impact of dosing fees upon clients. METHODS: Cross-sectional survey of people who use opioids regularly (N = 402) between December 2017 and March 2018, conducted in Australia. Dosing fees were calculated and expressed as percentage of income, by OAT type. Consequences and strategies for difficulties making payments were examined as proportions. RESULTS: A total of N = 360 participants had ever been in OAT and N = 245 participants currently engaged in OAT reported data on dosing fees, of them 53% (n = 129) reported paying dosing fees. Compared to clients with high levels of dosing supervision, those with moderate or low levels of supervision were more likely to pay dosing fees. The median 28-day dosing fee was AUD$110 (interquartile range AUD$80); median 28-day income was AUD$1520 (interquartile range AUD$700). For those who paid dosing fees, the fee comprised <10% of total monthly income for 70% of participants; however, 23% of participants paid fees comprising 10% to <20%, and 7% of participants paid fees comprising 20% or more of monthly income. Among those that had ever been in OAT, 72% experienced difficulties in paying treatment costs; 36% left treatment earlier than intended and 25% had been excluded due to payment difficulties. DISCUSSION AND CONCLUSIONS: Negative consequences of treatment costs to clients, particularly dosing fees, are evident. These costs impact treatment access and retention that may negatively impact clients' physical health, mental health and social wellbeing.

Economic analysis of out-of-pocket costs among people in opioid agonist treatment: A cross-sectional survey in three Australian jurisdictions.

BACKGROUND: Out-of-pocket costs for opioid agonist treatment (OAT) constitute a barrier to treatment entry and retention.This study examines OAT clients' total out-of-pocket costs (including dispensing fees, travel costs and OAT-related appointment costs) in different treatment settings (public clinics, community pharmacies, and private clinics). METHODS: Cross-sectional survey of 402 people with opioid drug use (OUD) in New South Wales (NSW), Victoria (VIC), Tasmania (TAS), Australia; 266 clients (66%) currently receiving methadone, buprenorphine or buprenorphine-naloxone treatment were asked about dispensing fees, travel costs and OAT-related appointment costs in the past 28 days. A two-part regression model was used to deal with non-normal distributions of costing data (right skew and excess zeros). RESULTS: Among clients currently receiving OAT, 87% paid out-of-pocket costs. Among those who paid out-of-pocket costs (N=194), travel costs accounted for more than half of total costs (52%), followed by dispensing fees (44%). The mean monthly total out-of-pocket costs were AU$135 (SD: AU$121) for public clinics, AU$161 (SD: AU$110) to AU$214 (SD: AU$166) for community pharmacies and AU$355 (SD: AU$159) for private clinics. Compared to participants in NSW private clinics, those at public clinics paid one third the total out-of-pocket costs (coefficient = 0.33; 95%CI = 0.23-0.48) and those at NSW, TAS, VIC pharmacies paid approximately half the costs (coefficient = 0.58; 95%CI = 0.42-0.79; coefficient = 0.51; 95%CI = 0.36-0.72; coefficient = 0.47; 95%CI = 0.34-0.66, respectively). People in OAT for more than a year paid half the total out-of-pocket costs, compared with those in OAT less than a year (coefficient = 0.49, 95%CI = 0.31-0.77). CONCLUSIONS: Participants in the current study spent one-eighth of their income on out-of-pocket costs associated with OAT representing a substantial financial burden. Total out-of-pocket costs disproportionately affects those who are newer in treatment and receiving fewer unsupervised doses. Considering and addressing total out-of-pocket costs, especially travel costs and dispensing fees, to clients is critical to prevent cost from being a barrier from receiving effective care.