A History of Population Health

In A history of Population Health Johan P. Mackenbach offers a comparative study of trends in 40 specific diseases in Europe and their explanation, focusing on the causes of the spectacular improvements in people’s health since the early 18th century. ; Readership: Health care professionals with an interest in medical history, students and scientists in public health, economics, sociology, and related disciplines, and anyone interested in the history of health and disease

A History of Population Health

In A history of Population Health Johan P. Mackenbach offers a comparative study of trends in 40 specific diseases in Europe and their explanation, focusing on the causes of the spectacular improvements in people’s health since the early 18th century. ; Readership: Health care professionals with an interest in medical history, students and scientists in public health, economics, sociology, and related disciplines, and anyone interested in the history of health and disease

Can inequalities in political participation explain health inequalities?

Inequalities in health are pervasive and durable, but they are not uniform. To date, however, the drivers of these between-country patters in health inequalities remain largely unknown. In this analysis, we draw on data from 17 European countries to explore whether inequalities in political participation, that is, inequalities in voting by educational attainment, are correlated with health inequalities. Over and above a range of relevant confounders, such as GDP, income inequality, health spending, social protection spending, poverty rates, and smoking, greater inequalities in political participation remain correlated with higher health inequalities. If ‘politicians and officials are under no compulsion to pay much heed to classes and groups of citizens that do not vote’ then political inequalities could indirectly affect health through its impact on policy choices that determine who has access to the resources necessary for a healthy life. Inequalities in political participation, then, may well be one of the ‘causes of the causes’ of ill-health

How comparable are different measures of self-rated health? Evidence from five European countries

Self-rated health (SRH) is a common health measurement in international research. Yet different versions of this item are often applied. This study compares the US (United States) version (from excellent to poor) and the EU (European) version (from very good to very bad) of SRH, and examines differences in their associations with demographic and objective health variables. Data were drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE), comprising information from 11,622 respondents aged 50 years and over in five countries. Respondents were presented with both the EU and US versions. Information was collected on basic demographics and health variables including chronic diseases, symptoms, functional limitations and depression. Firstly, the distribution of each version of the SRH item was assessed, and both relative and literal concordance was examined. Subsequently, multivariate regression analysis was used to assess differences in the associations of both items with demographic and health indicators. The US version has a more symmetric distribution and smaller variance than the EU version. Although the EU version discriminates better at the negative end, the US version shows better discrimination at the negative end of the scale. 69% of respondents provided literally concordant answers, while only about one third provided relatively concordant answers. Overall, however, less than 10% of respondents were discordant in either sense. Furthermore, the two versions were strongly correlated (polychoric correlatio

The Biodiversity Impact of Health Care:Quantifying the Extinction-Risk Footprint of Health Care in The Netherlands and Other European Countries

The health care sector exists to support and promote human wellbeing; however, its operations contribute to environmental degradation undermining nature’s capacity to support the same wellbeing. Biodiversity loss, in particular, creates threats to wellbeing through a reduction in ecosystem service provisioning and increases in disease. This study aims to estimate the extinction-risk footprint associated with the health care sector, focusing on Europe. We created an environmentally-extended multi-region input–output model using data on the extinction risk of species available from the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species. Using input–output analysis, we then quantified the extinction-risk footprint of the Dutch health care sector and, for comparison, that of the 30 European nations which use similar sector classifications in their National Accounts reporting. We found that the Netherlands has the highest health care extinction-risk footprint on a per-capita basis and that health care contributes 4.4% of the Dutch consumption extinction-risk footprint compared with an average of 2.6% across the comparator set. Food and beverage supply chains make a disproportionate contribution to health care’s extinction-risk footprint, while supply chains implicated in the sector’s carbon footprint make a limited contribution. These results suggest that reducing the environmental impact of the health care sector may require a differentiated approach when multiple environmental indicators are considered.</p

The Biodiversity Impact of Health Care:Quantifying the Extinction-Risk Footprint of Health Care in The Netherlands and Other European Countries

The health care sector exists to support and promote human wellbeing; however, its operations contribute to environmental degradation undermining nature’s capacity to support the same wellbeing. Biodiversity loss, in particular, creates threats to wellbeing through a reduction in ecosystem service provisioning and increases in disease. This study aims to estimate the extinction-risk footprint associated with the health care sector, focusing on Europe. We created an environmentally-extended multi-region input–output model using data on the extinction risk of species available from the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species. Using input–output analysis, we then quantified the extinction-risk footprint of the Dutch health care sector and, for comparison, that of the 30 European nations which use similar sector classifications in their National Accounts reporting. We found that the Netherlands has the highest health care extinction-risk footprint on a per-capita basis and that health care contributes 4.4% of the Dutch consumption extinction-risk footprint compared with an average of 2.6% across the comparator set. Food and beverage supply chains make a disproportionate contribution to health care’s extinction-risk footprint, while supply chains implicated in the sector’s carbon footprint make a limited contribution. These results suggest that reducing the environmental impact of the health care sector may require a differentiated approach when multiple environmental indicators are considered.</p