Using the triangle of human ecology for understanding self-rated depression: A quantitative study based on the HUNT 3 cohort

Aims: To test the triangle of human ecology by examining associations between unipolar depression and different measures of human biological factors, health behavior, and the physical environment. Methods: Data originate from the third wave of the Nord-Trøndelag Health Study (2006-2008). The survey was based on a random sample of 50,000 Norwegians (response rate: 54 %). Logistic regression was preformed, using unipolar depression, measured with the HADS-score, as outcome variable and 38 explanatory variables. Results: Biological factors including older age and male gender were associated with higher odds of depression as were behavioral factors including drinking behavior and having a neurotic personality. Reduced odds were associated with units of alcohol consumed, extrovert personality and physical activity. Social networks were an environmental factor with reduced odds at both personal and neighborhood levels, as was warmer outdoor temperatures. Conclusion: Using the triangle of human ecology provides a holistic insight into how behavior, biology and the environment influence mental health

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Background Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods We used data from 1990 to 2019 on people aged 30–79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings The number of people aged 30–79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306–359) million women and 317 (292–344) million men in 1990 to 626 (584–668) million women and 652 (604–698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55–62) of women and 49% (46–52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43–51) of women and 38% (35–41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20–27) for women and 18% (16–21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings

Motor Vehicle Crashes Registered by Casualties, Place of Accident and Place of Residence : Urban and Rural Differences in Norway

Norway has among the lowest rates of deaths per 100 000 people in road transport. Nevertheless, serious motor vehicle crashes are among the greatest avoidable toll on public health. Striking differences exist between the urban and rural death rates. The author examines national trends in injury risk due to serious private motor vehicle crashes by both place of accident and place of residence. Place of accident emphasizes local environments and site conditions with place-based and situational behaviour. Place of residence reflects vehicle occupants’ mobility and travel patterns in different areas and suggests that geographically rooted risk behaviour influences accidents. The analyses are split by urban, peri-urban, and rural types of residential area, based on population size and density. Nationwide road traffic accident data for the period 2000–2010 for private 4-wheel vehicle occupants are employed for calculating rates and proportion of casualties within and outside different types of residential area. Trends in health risks are presented in time series for motorized casualties and for males in the age group 16–24 years, by type of residential area. The proportions of casualties within versus outside their types of residential area are demonstrated. Population-based health risk differences accentuate rural areas as risk environments. Safety improvements have benefited urban areas and populations. Rural occupants’ mobility patterns imply higher mileages and speed in rural low-control system areas

The use of multilevel models for the prediction of road accident outcomes

An important problem in road traffic accident research is the resolution of the magnitude by which individual accident characteristics affect the risk of fatality for each person involved. This article introduces the potential of a recently developed form of regression models, known as multilevel models, for quantifying the various influences on casualty outcomes. The application of multilevel models is illustrated by the analysis of the predictors of outcome amongst over 16,000 fatally and seriously injured casualties involved in accidents between 1985 and 1996 in Norway. Risk of fatality was found to be associated with casualty age and sex, as well as the type of vehicles involved, the characteristics of the impact, the attributes of the road section on which it took place, the time of day, and whether alcohol was suspected. After accounting for these factors, the multilevel analysis showed that 16% of unexplained variation in casualty outcomes was between accidents, whilst ~1% was associated with the area of Norway in which each incident occurred. The benefits of using multilevel models to analyse accident data are discussed along with the limitations of traditional regression modelling approaches