Summary

The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models.


The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest.


One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation).


Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems.


This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210x.2012.00261.x

A general and simple method for obtaining R2 from generalized linear mixed-effects models

The primary purpose of this narrative review was to evaluate the current literature and to provide further insight into the role physical inactivity plays in the development of chronic disease and premature death. We confirm that there is irrefutable evidence of the effectiveness of regular physical activity in the primary and secondary prevention of several chronic diseases (e.g., cardiovascular disease, diabetes, cancer, hypertension, obesity, depression and osteoporosis) and premature death. We also reveal that the current Health Canada physical activity guidelines are sufficient to elicit health benefits, especially in previously sedentary people. There appears to be a linear relation between physical activity and health status, such that a further increase in physical activity and fitness will lead to additional improvements in health status.

/pdf/health-benefits-of-physical-activity-the-evidence-17tsp10zcy.pdf

Health benefits of physical activity: the evidence

Over 60 years ago, Selye1 recognized the paradox that the physiologic systems activated by stress can not only protect and restore but also damage the body. What links these seemingly contradictory roles? How does stress influence the pathogenesis of disease, and what accounts for the variation in vulnerability to stress-related diseases among people with similar life experiences? How can stress-induced damage be quantified? These and many other questions still challenge investigators. This article reviews the long-term effect of the physiologic response to stress, which I refer to as allostatic load.2 Allostasis — the ability to achieve stability through change3 — . . .

/pdf/protective-and-damaging-effects-of-stress-mediators-34e0m747fy.pdf

Protective and Damaging Effects of Stress Mediators

The metabolic syndrome

Results The prevalence of the metabolic syndrome ranged from 8.8% to 14.3%, depending on the definition. There were 109 deaths during the approximately 11.4year follow-up, of which 46 and 27 were due to CVD and CHD, respectively. Men with the metabolic syndrome as defined by the NCEP were 2.9 (95% confidence interval [CI], 1.2-7.2) to 4.2 (95% CI, 1.6-10.8) times more likely and, as defined by the WHO, 2.9 (95% CI, 1.2-6.8) to 3.3 (95% CI, 1.4-7.7) times more likely to die of CHD after adjustment for conventional cardiovascular risk factors. The metabolic syndrome as defined by the WHO was associated with 2.6 (95% CI, 1.4-5.1) to 3.0 (95% CI, 1.5-5.7) times higher CVD mortality and 1.9 (95% CI, 1.2-3.0) to 2.1 (95% CI, 1.3-3.3) times higher all-cause mortality. The NCEP definition less consistently predicted CVD and all-cause mortality. Factor analysis using 13 variables associated with metabolic or cardiovascular risk yielded a metabolic syndrome factor that explained 18% of total variance. Men with loadings on the metabolic factor in the highest quarter were 3.6 (95% CI, 1.7-7.9), 3.2 (95% CI, 1.7-5.8), and 2.3 (95% CI, 1.5-3.4) times more likely to die of CHD, CVD, and any cause, respectively. Conclusions Cardiovascular disease and all-cause mortality are increased in men with the metabolic syndrome, even in the absence of baseline CVD and diabetes. Early identification, treatment, and prevention of the metabolic syndrome present a major challenge for health care professionals facing an epidemic of overweight and sedentary lifestyle.

https://jamanetwork.com/journals/jama/articlepdf/1108360/JOC21220.pdf

The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men

This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.

https://jech.bmj.com/content/jech/60/1/7.full.pdf

Indicators of socioeconomic position (part 1)

The aim of this glossary is to encourage a dialogue that will advance the life course perspective.

https://jech.bmj.com/content/jech/57/10/778.full.pdf

Life course epidemiology

Studies on the health effects of income inequality have generated great interest. The evidence on this association between countries is mixed,1–4 but income inequality and health have been linked within the United States,5–11 Britain,12 and Brazil.13 Questions remain over how to interpret these findings and the mechanisms involved. We discuss three interpretations of the association between income inequality and health: the individual income interpretation, the psychosocial environment interpretation, and the neo-material interpretation.

#### Summary points

Income inequality has generally been associated with differences in health

A psychosocial interpretation of health inequalities, in terms of perceptions of relative disadvantage and the psychological consequences of inequality, raises several conceptual and empirical problems

Income inequality is accompanied by many differences in conditions of life at the individual and population levels, which may adversely influence health

Interpretation of links between income inequality and health must begin with the structural causes of inequalities, and not just focus on perceptions of that inequality

Reducing health inequalities and improving public health in the 21st century requires strategic investment in neo-material conditions via more equitable distribution of public and private resources

We reviewed the literature through traditional and electronic means and supplemented this with correlational analyses of gross domestic product and life expectancy and of income inequality and mortality trends based on data from the World Bank,14 the World Health Organization,15 and two British sources.16 17

According to the individual income interpretation, aggregate level associations between income inequality and health reflect only the individual level association between income and health. The curvilinear relation between income and health at the individual level 18 19 is a sufficient condition to produce health differences between populations with the same average income but different distributions of income.3 20 This interpretation assumes that determinants …

https://www.bmj.com/content/bmj/320/7243/1200.full.pdf

Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions

Objective : To examine the relation between health outcomes and the equality with which income is distributed in the United States. Design : The degree of income inequality, defined as the percentage of total household income received by the less well off 50% of households, and changes in income inequality were calculated for the 50 states in 1980 and 1990. These measures were then examined in relation to all cause mortality adjusted for age for each state, age specific deaths, changes in mortalities, and other health outcomes and potential pathways for 1980, 1990, and 1989-91. Main outcome measure : Age adjusted mortality from all causes. Results : There was a significant correlation (r=0.62, P Conclusions : Variations between states in the inequality of the distribution of income are significantly associated with variations between states in a large number of health outcomes and social indicators and with mortality trends. These differences parallel relative investments in human and social capital. Economic policies that influence income and wealth inequality may have an important impact on the health of countries. Key messages There was a significant correlation (r=0.62) between the proportion of total household income received by the less well off 50% of households and variation between states in death rates for the United States Income inequality was also significantly related to changes in mortality with smaller declines between 1980-90 in those states with greater income inequality Income inequality was associated with a large number of other health outcomes and with measures related to investments in human and social capital Economic policies that increase income inequality may also have a deleterious effect on population health

/pdf/inequality-in-income-and-mortality-in-the-united-states-psq6bkx58b.pdf

John Lynch

Papers

Indicators of socioeconomic position (part 1)

Life course epidemiology

Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions

Inequality in income and mortality in the United States: analysis of mortality and potential pathways.

Why do poor people behave poorly? Variation in adult health behaviours and psychosocial characteristics by stages of the socioeconomic lifecourse.