Informal caregiving and metabolic markers in the UK Household Longitudinal Study.

OBJECTIVES: Informal caregiving is associated with poorer mental and physical health. Little research has yet focused on objectively measured health risk factors, such as metabolic markers. The aim of this study was to investigate whether informal caregiving was associated with markers of metabolism in a large, representative UK longitudinal study. We also investigated whether more intensive caregiving, as indicated by more caregiving hours, was associated with a less favourable metabolic profile. STUDY DESIGN/OUTCOME MEASURES: Using data on 9408 participants aged 16+ from the UK Household Longitudinal Study, we explored the relationship between caregiving and metabolic markers (blood pressure, total and high density lipoprotein cholesterol, glycated haemoglobin and triglycerides). We additionally investigated the importance of caregiving intensity (number of hours spent caregiving per week). Associations between caregiving/caregiving intensity and metabolic markers were tested using gender-stratified linear regression models adjusted for age, household income, education, social class, chronic illness, number of dependent children in the household, body mass index and partnership status. RESULTS: Men who were informal caregivers had higher total cholesterol levels than non-caregivers (3.25% higher, 95% CI: 0.07, 6.53). Women caregivers also had higher total cholesterol levels and women providing intensive care (over 20 h per week) had higher triglyceride levels (19.91% higher, 95% CI: 7.22, 34.10) and lower levels of high density lipoprotein cholesterol (8.46% lower, 95% CI: 14.51, 1.99); however, associations for women were attenuated in our final models. CONCLUSIONS: Informal caregiving is associated with less favourable lipid profiles. This may be one mechanism through which informal caregiving is associated with increased disease risk. The health of informal caregivers should be a priority for public health.

Crossing the road in time: Inequalities in older people's walking speeds.

Pedestrian crossings in the UK and US require people to walk at 1.2 m/s to cross the road in time; however a large proportion of older people do not walk this fast, potentially discouraging walking or putting older people at risk of injury. We use longitudinal data to investigate changes in walking speed, and ability to cross the road in time, at older ages. 31,015 walking speed measurements were taken from 10,249 men and women aged 60+ years in waves 1-7 of the English Longitudinal Study of Ageing (2002-2014). Growth curve analyses were used to model how walking speed changes with increasing age, and predicted probabilities of being able to cross the road in time were estimated. 10% of measured walking speeds were fast enough to cross the road in time. Walking speed declined with age (-5.7×10-3m/s/yr (95% CI -7.6×10-3, -3.9×10-3)), and the decline accelerated with increasing age (-0.3 ×10-3m/s/yr (-0.4 ×10-3, -0.3 ×10-3)). Female, less wealthy and less healthy older people had slower walking speeds. For instance, predicted probability of crossing the road in time at age 60 was 14.8% (10.1, 18.5) and 2.7% (1.5, 3.8) for the richest and poorest men and 8.4% (6.0, 1.1) and 1.5% (0.9, 2.2) for the richest and poorest women, and at age 80 they were 7.1% (3.6, 10.5) and 1.0% (0.3, 1.7) for the richest and poorest men and 3.7% (1.6, 5.9) and 0.5% (0.1, 0.9) for the richest and poorest women. Most older people do not walk fast enough to cross the road in time. Even the majority of the wealthiest and healthiest people aged 60 years and older do not walk fast enough to cross pedestrian crossings in the allocated time. Crossing times should be increased to allow for older peoples' slower walking speeds or other policies considered to improve walkability, and to help avoid injuries and social isolation.

Active travel to work and cardiovascular risk factors in the United Kingdom.

BACKGROUND: Increasing active travel (walking, cycling, public transport) is increasingly seen as integral to strategies to raise physical activity levels. PURPOSE: This study examined (1) sociodemographic correlates of active travel to work and (2) associations between active travel and cardiovascular risk factors in the United Kingdom (UK). METHODS: Data come from Understanding Society, a nationally representative survey of UK residents in 2009/2011, analyzed in 2012. Multinomial logistic regression assessed associations between sociodemographic factors and mode of transport to work. Linear and logistic regression was used to examine associations between mode of travel and overweight/obesity, and having hypertension or diabetes. RESULTS: A total of 69% of participants traveled to work using private transport, with public transport, walking, and cycling used by 16%, 12%, and 3%, respectively. Use of any active travel was more likely in participants living in London. Black participants were more likely to walk (AOR=1.41, 95% CI=1.08, 1.84) or take public transport (AOR=2.34, 95% CI=1.88, 2.90) to work than whites. Using public transport, walking, or cycling to work was associated with a lower likelihood of being overweight (AOR=0.80, 95% CI=0.54, 0.88 for walking). Walking or cycling was associated with a lower likelihood of having diabetes, and walking was associated with a lower likelihood of having hypertension than private transport (AOR=0.83, 95% CI=0.71, 0.97). CONCLUSIONS: There are wide variations in the mode of travel to work across regions and sociodemographic groups in the UK. The protective association between active travel and cardiovascular risk demonstrated in this nationally representative study adds to growing evidence that concerted policy focus in this area may benefit population health.

Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper.

Much has been written about the measurement of socio-economic position (SEP) in high-income countries (HIC). Less has been written for an epidemiology, health systems and public health audience about the measurement of SEP in low- and middle-income countries (LMIC). The social stratification processes in many LMIC-and therefore the appropriate measurement tools-differ considerably from those in HIC. Many measures of SEP have been utilized in epidemiological studies; the aspects of SEP captured by these measures and the pathways through which they may affect health are likely to be slightly different but overlapping. No single measure of SEP will be ideal for all studies and contexts; the strengths and limitations of a given indicator are likely to vary according to the specific research question. Understanding the general properties of different indicators, however, is essential for all those involved in the design or interpretation of epidemiological studies. In this article, we describe the measures of SEP used in LMIC. We concentrate on measures of individual or household-level SEP rather than area-based or ecological measures such as gross domestic product. We describe each indicator in terms of its theoretical basis, interpretation, measurement, strengths and limitations. We also provide brief comparisons between LMIC and HIC for each measure.

Difficulties in finding DNA mutations and associated phenotypic data in web resources using simple, uncomplicated search terms, and a suggested solution.

DNA mutation data currently reside in many online databases, which differ markedly in the terminology used to describe or define the mutation and also in completeness of content, potentially making it difficult both to locate a mutation of interest and to find sought-after data (eg phenotypic effect). To highlight the current deficiencies in the accessibility of web-based genetic variation information, we examined the ease with which various resources could be interrogated for five model mutations, using a set of simple search terms relating to the change in amino acid or nucleotide. Fifteen databases were investigated for the time and/or number of mouse clicks; clicks required to find the mutations; availability of phenotype data; the procedure for finding information; and site layout. Google and PubMed were also examined. The three locus-specific databases (LSDBs) generally yielded positive outcomes, but the 12 genome-wide databases gave poorer results, with most proving not to be searchable and only three yielding successful outcomes. Google and PubMed searches found some mutations and provided patchy information on phenotype. The results show that many web-based resources are not currently configured for fast and easy access to comprehensive mutation data, with only the isolated LSDBs providing optimal outcomes. Centralising this information within a common repository, coupled with a simple, all-inclusive interrogation process, would improve searching for all gene variation data.