'I don't really associate climate change with actual people's health': a qualitative study in England of perceptions of climate change and its impacts on health.

OBJECTIVES: The health impacts of climate change are increasing, but qualitative evidence on people's perceptions is limited. This qualitative study investigated people's perceptions of climate change and its impacts on health. STUDY DESIGN: This was an online study using semistructured interviews. METHODS: A total of 41 semistructured interviews were conducted in 2021 with members of the public aged ≥15 years living in England, recruited via community-based groups. Data were analysed using reflexive thematic analysis. RESULTS: Participants were concerned about climate change, which was often perceived as extreme weather events happening elsewhere. Changes in the UK's seasons and weather patterns were noted, but participants were uncertain whether these changes resulted from climate change. Participants often struggled to identify health impacts of climate change; where health impacts were described, they tended to be linked to extreme weather events outside the United Kingdom and their associated threats to life. The mental health impacts of such events were also noted. CONCLUSIONS: The study found that most participants did not perceive climate change to be affecting people's health in England. This raises questions about whether framing climate change as a health issue, an approach advocated for countries less exposed to the direct effects of climate change, will increase its salience for the British public.

REGENT: a risk assessment and classification algorithm for genetic and environmental factors.

The identification of environmental and genetic factors that contribute to disease risk requires appropriate statistical methods and software that can integrate different sources of risk, provide statistical assessment of combined risk factors, and facilitate interpretation of this risk. We have developed an R package, REGENT, to calculate risks conferred by genetic factors and multilevel environmental factors. This is performed at a population level, with the option to also analyse individual-level data. REGENT incorporates variability in risk factors to calculate confidence intervals for risk estimates and to classify the population into different categories of risk based on significant differences from the baseline average member of the population. REGENT is an R package available from CRAN: http://cran.r-project.org/web/packages/REGENT. It will be of value to genetic researchers exploring the utility of the variants detected for their disorder, and to clinical researchers interested in genetic risk studies.

Risk categorization for complex disorders according to genotype relative risk and precision in parameter estimates.

PURPOSE: To develop a method of genetic risk categorization based on the risk conferred by genetic variants and the precision with which risks are known. METHODS: We develop a method for risk assignment based on an "average" member of the population and their genotype, deriving empirical confidence intervals encompassing all relevant sources of variation in disease risk. An individual with risk confidence interval that does not overlap with that of the "average" individual is categorized as having higher or lower disease risk. The method is applied to data sets in Crohn's disease and type 2 diabetes. RESULTS: The proportion of the population assigned to the average risk category depends on genotype relative risk, allele frequency and sample size of the study used to estimate these parameters. For low genotype relative risks or minor allele frequency, little resolution into different risk categories may be possible. CONCLUSION: The utility of a genetic risk variant for risk categorization depends on both the magnitude of the genotype relative risk and the accuracy with which this, and other elements of risk calculation, are known. Genetic risk calculations should include an assessment of the accuracy of the risk estimation.

The effect of high level physical activity (8.5 METs or greater) and estrogen replacement therapy upon bone mass in postmenopausal females, aged 50-68 years.

The purpose of this study was to assess the effect of physical activity (PA) and estrogen therapy (ET) upon bone mass at the 1/3 and 4 mm distal radial sites in 50 postmenopausal females. The Ss (means age = 57.4 +/- 5.4 yrs) completed: 1) activity and gynecological surveys, 2) Balke treadmill tests (85% of age determined HR), and 3) single photon absorptiometry measurements of the radius. The activity surveys and treadmill tests were used to categorize Ss into high (8.5 METs or greater, n = 27) and low (6.0 METs or less, n = 23) physical activity groups (HPA/LPA), and the gynecological surveys were used to distinguish Ss who were on estrogen therapy (n = 17) and those who had never been on estrogen therapy (n = 33). Data revealed the HPA group had significantly higher BMC (g/cm) and BMC/BW (g/cm2) at the 1/3 distal radial site than the LPA group (.834 g/cm to .721 g/cm, p less than .01; and .698 g/cm2 to .653 g/cm2, p less than .06, respectively) but were not significantly different at the 4 mm distal site. The ET group had a significantly higher bone mass than the never on ET group for BMC/BW at the 4 mm site (.907 g/cm to .809 g/cm p less than .027). It was concluded that high level physical activity (8.5 METs or greater) or estrogen therapy was helpful in reducing the risk of bone loss in postmenopausal women.