HCT frailty scale for younger and older adults undergoing allogeneic hematopoietic cell transplantation.

The HCT Frailty Scale is an easy prognostic tool composed of (a) Clinical Frailty Scale; (b) Instrumental Activities of Daily Living; (c) Timed-up-and-Go test; (d) Grip Strength; (e) Self-Health Rated Questionnaire; (f) Falls tests; (g) Albumin and C-reactive protein levels. This scale was designed to classify allogeneic hematopoietic cell transplant (alloHCT) candidates into fit, pre-frail and frail groups, irrespective of age. This study evaluates the ability of this frailty classification to predict overall survival (OS) and non-relapse mortality (NRM) in adult patients of all ages, in a prospective sample of 298 patients transplanted between 2018 and 2020. At first consultation, 103 (34.6%) patients were fit, 148 (49.7%) pre-frail, and 47 (15.8%) were frail. The 2-year OS and NRM of the three groups were 82.9%, 67.4%, and 48.3% (P < 0.001), and 5.4%, 19.2%, and 37.7% (P < 0.001). For patients younger than 60 years (n = 174), the 2-year OS and NRM of fit, pre-frail, and frail groups were 88.4%, 69.3% and 53.1% (P = 0.002), and 5.8%, 22.8%, and 34.8% (P = 0.005), respectively; and in patients older than 60 (n = 124), OS and NRM were 75.5%, 63.8% and 41.4% (P = 0.006), and 4.9%, 16.4%, and 42.1% (P = 0.001). In conclusion, frailty predicted worse transplant outcomes in both younger and older adults.

Impact of hematopoietic cell transplant frailty scale on transplant outcome in adults.

This prospective study designs an HCT Frailty Scale to classify alloHCT candidates into groups of frail, pre-frail, and fit, and to be implemented in the first consultation at no additional cost. The present scale is composed of the following eight variables: Clinical Frailty Scale, Instrumental Activities of Daily Living, Timed Up and Go Test, Grip Strength, Self-Health Rated, Falls, Albumin, and C-Reactive Protein. The Frailty score of a patient is the weighted sum of scores for each item, with weights assigned according to the hazard ratios of a multivariable Cox proportional hazards model estimated and validated with data on OS as the dependent variable, and the scores of the eight variables as explanatory ones, from 298 adults split into training (n = 200) and validation (n = 98) sets. For clinical use, the scale scores were transformed into three categories: scale score ≤1: fit; 15.5 frail. The estimated probabilities of 1-year OS in each group of frailty, were, respectively: 83.7%, 48.5%, and 16.5% (p < 0.001). In the validation cohort, the respective values were 90.3%, 69.5%, and 46.2% (p < 0.001). Pending further external validations, the HCT Frailty Scale is a low cost-highly informative prognostic signal of outcomes at the pre-transplant stage.

Pilot prospective study of Frailty and Functionality in routine clinical assessment in allogeneic hematopoietic cell transplantation.

A Frailty and Functionality evaluation for alloHCT was implemented using existing resources. We describe the implementation of this evaluation across all ages and at first consultation, and correlate results with posttransplant outcomes in 168 patients. The evaluation consists of: Clinical Frailty Scale (CFS), Instrumental Activities of Daily Living (IADL), grip strength (GS), timed up and go test (TUGT), self-rated health question (SRH), Single question of Falls, albumin and C-Reactive Protein (CRP) levels. Median time to perform the evaluation was 5-6 min. Median age was 58 years (range: 19-77) and median follow-up was 5.3 months. TUGT > 10 s (HR 2.92; p = 0.003), raised CRP (HR 4.40; p < 0.001), and hypoalbuminemia (HR 2.10; p = 0.043) were significant risk factors for worse overal survival (OS). CFS ≥ 3 (HR 3.11; p = 0.009), TUGT > 10 s (HR 3.47; p = 0.003), GS (HR 2.56; p = 0.029), SRH ( 10 s and raised CRP were significant predictors for worse OS and NRM. SRH (

Can the built environment reduce health inequalities? A study of neighbourhood socioeconomic disadvantage and walking for transport.

Residents of socioeconomically disadvantaged neighbourhoods are more likely to walk for transport than their counterparts in advantaged neighbourhoods; however, the reasons for higher rates of transport walking in poorer neighbourhoods remain unclear. We investigated this issue using data from the HABITAT study of physical activity among 11,037 mid-aged residents of 200 neighbourhoods in Brisbane, Australia. Using a five-step mediation analysis and multilevel regression, we found that higher levels of walking for transport in disadvantaged neighbourhoods was associated with living in a built environment more conducive to walking (i.e. greater street connectivity and land use mix) and residents of these neighbourhoods having more limited access to a motor vehicle. The health benefits that accrue to residents of disadvantaged neighbourhoods as a result of their higher levels of walking for transport might help offset the negative effects of less healthy behaviours (e.g. smoking, poor diet), thus serving to contain or reduce neighbourhood inequalities in chronic disease.

Test-retest reliability of perceptions of the neighborhood environment for physical activity by socioeconomic status.

BACKGROUND: Further development of high quality measures of neighborhood perceptions will require extensions and refinements to our existing approaches to reliability assessment. This study examined the test-retest reliability of perceptions of the neighborhood environment by socioeconomic status (SES). METHODS: Test and retest surveys were conducted using a mail survey method with persons aged 40 to 65 years (n = 222, 78.2% response rate). SES was measured using the respondent's education level and the socioeconomic characteristics of their neighborhood of residence. Reliability was assessed using intraclass correlations (ICC) estimated with random coefficient models. RESULTS: Overall, the 27 items had moderate-to-substantial reliability (ICC = 0.41-0.74). Few statistically significant differences were found in ICC between the education groups or neighborhoods, although the ICCs were significantly larger among the low SES for items that measured perceptions of neighborhood greenery, interesting things to see, litter, traffic volume and speed, crime, and rowdy youth on the streets. CONCLUSION: For the majority of the items, poor reliability and subsequent exposure misclassification is no more or less likely among low educated respondents and residents of disadvantaged neighborhoods. Estimates of the association between neighborhood perceptions and physical activity therefore are likely to be similarly precise irrespective of the respondent's socioeconomic background.

Increased traffic exposure and negative birth outcomes: a prospective cohort in Australia.

BACKGROUND: Pregnant women exposed to traffic pollution have an increased risk of negative birth outcomes. We aimed to investigate the size of this risk using a prospective cohort of 970 mothers and newborns in Logan, Queensland. METHODS: We examined two measures of traffic: distance to nearest road and number of roads around the home. To examine the effect of distance we used the number of roads around the home in radii from 50 to 500 metres. We examined three road types: freeways, highways and main roads. RESULTS: There were no associations with distance to road. A greater number of freeways and main roads around the home were associated with a shorter gestation time. There were no negative impacts on birth weight, birth length or head circumference after adjusting for gestation. The negative effects on gestation were largely due to main roads within 400 metres of the home. For every 10 extra main roads within 400 metres of the home, gestation time was reduced by 1.1% (95% CI: -1.7, -0.5; p-value = 0.001). CONCLUSIONS: Our results add weight to the association between exposure to traffic and reduced gestation time. This effect may be due to the chemical toxins in traffic pollutants, or because of disturbed sleep due to traffic noise.

The association between objectively measured neighborhood features and walking in middle-aged adults.

PURPOSE: Explore the role of the neighborhood environment in supporting walking. DESIGN: Cross-sectional study of 10,286 residents of 200 neighborhoods. Participants were selected using a stratified two-stage cluster design. Data were collected by mail survey (68.5% response rate). SETTING: Brisbane City Local Government Area, Australia, 2007. SUBJECTS: Brisbane residents aged 40 to 65 years. MEASURES: Environmental: street connectivity, residential density, hilliness, tree coverage, bikeways, and streetlights within a 1-km circular buffer from each resident's home; and network distance to nearest river or coast, public transport, shop, and park. Walking: minutes walked in the previous week: < 30 minutes, ≥ 30 to < 90 minutes, ≥ 90 to < 150 minutes, ≥ 150 to < 300 minutes, and ≥ 300 minutes. ANALYSIS: The association between each neighborhood characteristic and walking was examined using multilevel multinomial logistic regression, and the model parameters were estimated using Markov chain Monte Carlo simulation. RESULTS: After adjustment for individual factors, the likelihood of walking for more than 300 minutes (relative to < 30 minutes) was highest in areas with the most connectivity (odds ratio [OR] 5 1.93; 99% confidence intervals [CI], 1.32-2.80), greatest residential density (OR 5 1.47; 99% CI, 1.02-2.12), least tree coverage (OR 5 1.69; 99% CI, 1.13-2.51), most bikeways (OR 5 1.60; 99% CI, 1.16-2.21), and most streetlights (OR 5 1.50; 99% CI, 1.07-2.11). The likelihood of walking for more than 300 minutes was also higher among those who lived closest to a river or the coast (OR 5 2.06; 99% CI, 1.41-3.02). CONCLUSION: The likelihood of meeting (and exceeding) physical activity recommendations on the basis of walking was higher in neighborhoods with greater street connectivity and residential density, more streetlights and bikeways, closer proximity to waterways, and less tree coverage. Interventions targeting these neighborhood characteristics may lead to improved environmental quality as well as lower rates of overweight and obesity and associated chromic disease.

Neighborhood disadvantage and physical activity: baseline results from the HABITAT multilevel longitudinal study.

PURPOSE: To examine the association between neighborhood disadvantage and physical activity (PA). METHODS: We use data from the HABITAT multilevel longitudinal study of PA among middle-aged (40-65 years) men and women (N = 11,037, 68.5% response rate) living in 200 neighborhoods in Brisbane, Australia. PA was measured using three questions from the Active Australia Survey (general walking, moderate, and vigorous activity), one indicator of total activity, and two questions about walking and cycling for transport. The PA measures were operationalized by using multiple categories based on time and estimated energy expenditure that were interpretable with reference to the latest PA recommendations. The association between neighborhood disadvantage and PA was examined with the use of multilevel multinomial logistic regression and Markov chain Monte Carlo simulation. The contribution of neighborhood disadvantage to between-neighborhood variation in PA was assessed using the 80% interval odds ratio. RESULTS: After adjustment for sex, age, living arrangement, education, occupation, and household income, reported participation in all measures and levels of PA varied significantly across Brisbane's neighborhoods, and neighborhood disadvantage accounted for some of this variation. Residents of advantaged neighborhoods reported significantly higher levels of total activity, general walking, moderate, and vigorous activity; however, they were less likely to walk for transport. There was no statistically significant association between neighborhood disadvantage and cycling for transport. In terms of total PA, residents of advantaged neighborhoods were more likely to exceed PA recommendations. CONCLUSIONS: Neighborhoods may exert a contextual effect on the likelihood of residents participating in PA. The greater propensity of residents in advantaged neighborhoods to do high levels of total PA may contribute to lower rates of cardiovascular disease and obesity in these areas.

HABITAT: A longitudinal multilevel study of physical activity change in mid-aged adults.

BACKGROUND: Little is known about the patterns and influences of physical activity change in mid-aged adults. This study describes the design, sampling, data collection, and analytical plan of HABITAT, an innovative study of (i) physical activity change over five years (2007-2011) in adults aged 40-65 years at baseline, and (ii) the relative contribution of psychological variables, social support, neighborhood perceptions, area-level factors, and sociodemographic characteristics to physical activity change. METHODS/DESIGN: HABITAT is a longitudinal multi-level study. 1625 Census Collection Districts (CCDs) in Brisbane, Australia were ranked by their index of relative socioeconomic disadvantage score, categorized into deciles, and 20 CCDs from each decile were selected to provide 200 local areas for study inclusion. From each of the 200 CCDs, dwellings with individuals aged between 40-65 years (in 2007) were identified using electoral roll data, and approximately 85 people per CCD were selected to participate (N = 17,000). A comprehensive Geographic Information System (GIS) database has been compiled with area-level information on public transport networks, footpaths, topography, traffic volume, street lights, tree coverage, parks, public services, and recreational facilities Participants are mailed a questionnaire every two years (2007, 2009, 2011), with items assessing physical activity (general walking, moderate activity, vigorous activity, walking for transport, cycling for transport, recreational activities), sitting time, perceptions of neighborhood characteristics (traffic, pleasant surroundings, streets, footpaths, crime and safety, distance to recreational and business facilities), social support, social cohesion, activity-related cognitions (attitudes, efficacy, barriers, motivation), health, and sociodemographic characteristics. Analyses will use binary and multinomial logit regression models, as well as generalized linear latent growth models. DISCUSSION: HABITAT will provide unique information to improve our understanding of the determinants of physical activity, and to help identify "people" and "place" priority targets for public policy and health promotion aimed at increasing physical activity participation among mid-aged men and women.