TRAVEL COST MODELS, HETEROSKEDASTICITY, AND SAMPLING

Using theoretical derivations, it is shown that collecting data on individuals' visitation rates to a recreation site by each of these methods: (1) on-site sampling of visits; (2) sampling individuals surrounding the recreations site; and (3) sampling license holders, results in three unique heteroskedacity problems. A different weighted least squares approach is offered in each case when estimating the visits per capita-travel cost relationship in zonal travel cost models. Furthermore, to the extent that individuals within an origin zone face different prices, there is an inherent aggregation bias when estimating consumer surplus.Resource /Energy Economics and Policy,

Childhood IQ and risk of bipolar disorder in adulthood: prospective birth cohort study

Background: Intellectual ability may be an endophenotypic marker for bipolar disorder. Aims: Within a large birth cohort, we aimed to assess whether childhood IQ (including both verbal IQ (VIQ) and performance IQ (PIQ) subscales) was predictive of lifetime features of bipolar disorder assessed in young adulthood. Method: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a large UK birth cohort, to test for an association between measures of childhood IQ at age 8 years and lifetime manic features assessed at age 22–23 years using the Hypomania Checklist-32 (HCL-32; n=1881 individuals). An ordinary least squares linear regression model was used, with normal childhood IQ (range 90–109) as the referent group. We adjusted analyses for confounding factors, including gender, ethnicity, handedness, maternal social class at recruitment, maternal age, maternal history of depression and maternal education. Results: There was a positive association between IQ at age 8 years and lifetime manic features at age 22–23 years (Pearson's correlation coefficient 0.159 (95% CI 0.120–0.198), P>0.001). Individuals in the lowest decile of manic features had a mean full-scale IQ (FSIQ) which was almost 10 points lower than those in the highest decile of manic features: mean FSIQ 100.71 (95% CI 98.74–102.6) v. 110.14 (95% CI 107.79–112.50), P>0.001. The association between IQ and manic features was present for FSIQ, VIQ and for PIQ but was strongest for VIQ. Conclusions: A higher childhood IQ score, and high VIQ in particular, may represent a marker of risk for the later development of bipolar disorder. This finding has implications for understanding of how liability to bipolar disorder may have been selected through generations. It will also inform future genetic studies at the interface of intelligence, creativity and bipolar disorder and is relevant to the developmental trajectory of bipolar disorder. It may also improve approaches to earlier detection and treatment of bipolar disorder in adolescents and young adults

Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study

Objective: To investigate the association between active commuting and incident cardiovascular disease (CVD), cancer, and all cause mortality. Design: Prospective population based study. Setting: UK Biobank. Participants: 263 450 participants (106 674 (52%) women; mean age 52.6), recruited from 22 sites across the UK. The exposure variable was the mode of transport used (walking, cycling, mixed mode v non-active (car or public transport)) to commute to and from work on a typical day. Main outcome measures: Incident (fatal and non-fatal) CVD and cancer, and deaths from CVD, cancer, or any causes. Results: 2430 participants died (496 were related to CVD and 1126 to cancer) over a median of 5.0 years (interquartile range 4.3-5.5) follow-up. There were 3748 cancer and 1110 CVD events. In maximally adjusted models, commuting by cycle and by mixed mode including cycling were associated with lower risk of all cause mortality (cycling hazard ratio 0.59, 95% confidence interval 0.42 to 0.83, P=0.002; mixed mode cycling 0.76, 0.58 to 1.00, P<0.05), cancer incidence (cycling 0.55, 0.44 to 0.69, P<0.001; mixed mode cycling 0.64, 0.45 to 0.91, P=0.01), and cancer mortality (cycling 0.60, 0.40 to 0.90, P=0.01; mixed mode cycling 0.68, 0.57 to 0.81, P<0.001). Commuting by cycling and walking were associated with a lower risk of CVD incidence (cycling 0.54, 0.33 to 0.88, P=0.01; walking 0.73, 0.54 to 0.99, P=0.04) and CVD mortality (cycling 0.48, 0.25 to 0.92, P=0.03; walking 0.64, 0.45 to 0.91, P=0.01). No statistically significant associations were observed for walking commuting and all cause mortality or cancer outcomes. Mixed mode commuting including walking was not noticeably associated with any of the measured outcomes. Conclusions: Cycle commuting was associated with a lower risk of CVD, cancer, and all cause mortality. Walking commuting was associated with a lower risk of CVD independent of major measured confounding factors. Initiatives to encourage and support active commuting could reduce risk of death and the burden of important chronic conditions

An overview of assessment methodology for obesity-related variables in infants at risk

Background: The first 2 years of a child’s life are a particularly critical time period for obesity prevention. Aim: An increasing amount of research across the world is aimed at understanding factors that impact early childhood obesity and developing interventions that target these factors effectively. With this growing interest, new and interdisciplinary research teams are developing to meet this research need. Due to rapid growth velocity during this phase of the lifespan, typical assessments used in older populations may not be valid or applicable in infants, and investigators need to be aware of the pros and cons of specific methodological strategies. Methods: This paper provides an overview of methodology available to assess obesity-related factors in the areas of anthropometry and body composition, nutrient intake, and energy expenditure in infants aged 0–2 years. Results: Gold standard measures for body composition, such as dual-energy X-ray absorptiometry (DXA) or other imaging techniques, are costly, require highly trained personnel, and are limited for research application. Nutrient intake methodology primarily includes surveys and questionnaires completed via parent proxy report. In terms of energy expenditure, methods of calorimetry are expensive and may not differentiate between different activities. Questionnaires or physical activity sensors offer another way of energy expenditure assessment. However, questionnaires have a certain recall bias, while the sensors require further validation. Conclusions: Overall, in addition to understanding the pros and cons of each assessment tool, researchers should take into consideration the experience of the interdisciplinary team of investigators, as well as the cost and availability of measures at their institution

The impact of confounding on the associations of different adiposity measures with the incidence of cardiovascular disease: a cohort study of 296 535 adults of white European descent

Aims: The data regarding the associations of body mass index (BMI) with cardiovascular (CVD) risk, especially for those at the low categories of BMI, are conflicting. The aim of our study was to examine the associations of body composition (assessed by five different measures) with incident CVD outcomes in healthy individuals. Methods and results: A total of 296 535 participants (57.8% women) of white European descent without CVD at baseline from the UK biobank were included. Exposures were five different measures of adiposity. Fatal and non-fatal CVD events were the primary outcome. Low BMI (≤18.5 kg m−2) was associated with higher incidence of CVD and the lowest CVD risk was exhibited at BMI of 22–23 kg m−2 beyond, which the risk of CVD increased. This J-shaped association attenuated substantially in subgroup analyses, when we excluded participants with comorbidities. In contrast, the associations for the remaining adiposity measures were more linear; 1 SD increase in waist circumference was associated with a hazard ratio of 1.16 [95% confidence interval (CI) 1.13–1.19] for women and 1.10 (95% CI 1.08–1.13) for men with similar magnitude of associations for 1 SD increase in waist-to-hip ratio, waist-to-height ratio, and percentage body fat mass. Conclusion: Increasing adiposity has a detrimental association with CVD health in middle-aged men and women. The association of BMI with CVD appears more susceptible to confounding due to pre-existing comorbidities when compared with other adiposity measures. Any public misconception of a potential ‘protective’ effect of fat on CVD risk should be challenged

Dietary fat and total energy intake modifies the association of genetic profile risk score on obesity: evidence from 48 170 UK Biobank participants

Background: Obesity is a multifactorial condition influenced by both genetics and lifestyle. The aim of this study was to investigate whether the association between a validated genetic profile risk score for obesity (GPRS-obesity) and body mass index (BMI) or waist circumference (WC) was modified by macronutrient intake in a large general population study. Methods: This study included cross-sectional data from 48 170 white European adults, aged 37–73 years, participating on the UK Biobank. Interactions between GPRS-obesity, and macronutrient intake (including total energy, protein, fat, carbohydrate and dietary fibre intake) and its effects on BMI and WC were investigated. Results: The 93-SNPs genetic profile risk score was associated with a higher BMI (β:0.57 kg.m−2 per standard deviation (s.d.) increase in GPRS, [95%CI:0.53–0.60]; P=1.9 × 10−183) independent of major confounding factors. There was a significant interaction between GPRS and total fat intake (P[interaction]=0.007). Among high fat intake individuals, BMI was higher by 0.60 [0.52, 0.67] kg.m−2 per s.d. increase in GPRS-obesity; the change in BMI with GPRS was lower among low fat intake individuals (β:0.50 [0.44, 0.57] kg.m-2). Significant interactions with similar patterns were observed for saturated fat intake (High β:0.66 [0.59, 0.73] versus Low β:0.49 [0.42, 0.55] kg.m-2, P-interaction=2 × 10-4), and total energy intake (High β:0.58 [0.51, 0.64] versus Low β:0.49 [0.42, 0.56] kg.m−2, P-interaction=0.019), but not for protein intake, carbohydrate intake and fiber intake (P-interaction >0.05). The findings were broadly similar using WC as the outcome. Conclusions: These data suggest that the benefits of reducing the intake of fats and total energy intake, may be more important in individuals with high genetic risk for obesity