Comparing Observed and Unobserved Components of Childhood: Evidence From Finnish Register Data on Midlife Mortality From Siblings and Their Parents.

In this study, we argue that the long arm of childhood that determines adult mortality should be thought of as comprising an observed part and its unobserved counterpart, reflecting the observed socioeconomic position of individuals and their parents and unobserved factors shared within a family. Our estimates of the observed and unobserved parts of the long arm of childhood are based on family-level variance in a survival analytic regression model, using siblings nested within families as the units of analysis. The study uses a sample of Finnish siblings born between 1936 and 1950 obtained from Finnish census data. Individuals are followed from ages 35 to 72. To explain familial influence on mortality, we use demographic background factors, the socioeconomic position of the parents, and the individuals' own socioeconomic position at age 35 as predictors of all-cause and cause-specific mortality. The observed part-demographic and socioeconomic factors, including region; number of siblings; native language; parents' education and occupation; and individuals' income, occupation, tenancy status, and education-accounts for between 10 % and 25 % of the total familial influence on mortality. The larger part of the influence of the family on mortality is not explained by observed individual and parental socioeconomic position or demographic background and thus remains an unobserved component of the arm of childhood. This component highlights the need to investigate the influence of childhood circumstances on adult mortality in a comprehensive framework, including demographic, social, behavioral, and genetic information from the family of origin.

Assessing the impact of natural policy experiments on socioeconomic inequalities in health: how to apply commonly used quantitative analytical methods?

BACKGROUND: The scientific evidence-base for policies to tackle health inequalities is limited. Natural policy experiments (NPE) have drawn increasing attention as a means to evaluating the effects of policies on health. Several analytical methods can be used to evaluate the outcomes of NPEs in terms of average population health, but it is unclear whether they can also be used to assess the outcomes of NPEs in terms of health inequalities. The aim of this study therefore was to assess whether, and to demonstrate how, a number of commonly used analytical methods for the evaluation of NPEs can be applied to quantify the effect of policies on health inequalities. METHODS: We identified seven quantitative analytical methods for the evaluation of NPEs: regression adjustment, propensity score matching, difference-in-differences analysis, fixed effects analysis, instrumental variable analysis, regression discontinuity and interrupted time-series. We assessed whether these methods can be used to quantify the effect of policies on the magnitude of health inequalities either by conducting a stratified analysis or by including an interaction term, and illustrated both approaches in a fictitious numerical example. RESULTS: All seven methods can be used to quantify the equity impact of policies on absolute and relative inequalities in health by conducting an analysis stratified by socioeconomic position, and all but one (propensity score matching) can be used to quantify equity impacts by inclusion of an interaction term between socioeconomic position and policy exposure. CONCLUSION: Methods commonly used in economics and econometrics for the evaluation of NPEs can also be applied to assess the equity impact of policies, and our illustrations provide guidance on how to do this appropriately. The low external validity of results from instrumental variable analysis and regression discontinuity makes these methods less desirable for assessing policy effects on population-level health inequalities. Increased use of the methods in social epidemiology will help to build an evidence base to support policy making in the area of health inequalities.

The impact of increasing income inequalities on educational inequalities in mortality - An analysis of six European countries.

BACKGROUND: Over the past decades, both health inequalities and income inequalities have been increasing in many European countries, but it is unknown whether and how these trends are related. We test the hypothesis that trends in health inequalities and trends in income inequalities are related, i.e. that countries with a stronger increase in income inequalities have also experienced a stronger increase in health inequalities. METHODS: We collected trend data on all-cause and cause-specific mortality, as well as on the household income of people aged 35-79, for Belgium, Denmark, England & Wales, France, Slovenia, and Switzerland. We calculated absolute and relative differences in mortality and income between low- and high-educated people for several time points in the 1990s and 2000s. We used fixed-effects panel regression models to see if changes in income inequality predicted changes in mortality inequality. RESULTS: The general trend in income inequality between high- and low-educated people in the six countries is increasing, while the mortality differences between educational groups show diverse trends, with absolute differences mostly decreasing and relative differences increasing in some countries but not in others. We found no association between trends in income inequalities and trends in inequalities in all-cause mortality, and trends in mortality inequalities did not improve when adjusted for rising income inequalities. This result held for absolute as well as for relative inequalities. A cause-specific analysis revealed some association between income inequality and mortality inequality for deaths from external causes, and to some extent also from cardiovascular diseases, but without statistical significance. CONCLUSIONS: We find no support for the hypothesis that increasing income inequality explains increasing health inequalities. Possible explanations are that other factors are more important mediators of the effect of education on health, or more simply that income is not an important determinant of mortality in this European context of high-income countries. This study contributes to the discussion on income inequality as entry point to tackle health inequalities. More research is needed to test the common and plausible assumption that increasing income inequality leads to more health inequality, and that one needs to act against the former to avoid the latter.

What causes health inequality? A systematic review on the relative importance of social causation and health selection.

BACKGROUND: The social gradient in health is one of the most reliable findings in public health research. The two competing hypotheses that try to explain this gradient are known as the social causation and the health selection hypothesis. There is currently no synthesis of the results of studies that test both hypotheses. METHODS: We provide a systematic review of the literature that has addressed both the health selection and social causation hypotheses between 1994 and 2013 using seven databases following PRISMA rules. RESULTS: The search strategy resulted in 2952 studies, of which, we included 34 in the review. The synthesis of these studies suggests that there is no general preference for either of the hypotheses (12 studies for social causation, 10 for health selection). However, both a narrative synthesis as well as meta-regression results show that studies using indicators for socio-economic status (SES) that are closely related to the labor market find equal support for health selection and social causation, whereas indicators of SES like education and income yield results that are in favor of the social causation hypothesis. High standards in statistical modeling were associated with more support for health selection. CONCLUSIONS: The review highlights the fact that the causal mechanisms behind health inequalities are dependent on whether or not the dimension being analyzed closely reflects labor market success. Additionally, further research should strive to improve the statistical modeling of causality, as this might influence the conclusions drawn regarding the relative importance of health selection and social causation.

Obesity and the potential reduction of social inequalities in mortality: evidence from 21 European populations.

BACKGROUND: Obesity contributes considerably to the problem of health inequalities in many countries, but quantitative estimates of this contribution and to what extent it is modifiable are scarce. We identify the potential for reducing educational inequalities in all-cause and obesity-related mortality in 21 European populations, by modifying educational differences in obesity and overweight. METHODS: Prevalence data and mortality data come from 21 European populations. Mortality rate ratios come from literature reviews. We use the population attributable fraction (PAF) to estimate the impact of scenario-based changes in the social distribution of obesity on educational inequalities in mortality. RESULTS: An elimination of differences in obesity between educational groups would decrease relative inequality in all-cause mortality between those with high and low education by up to 12% for men and 42% for women. About half of the relative inequality in mortality could be reduced for some causes of death in several countries, often in southern Europe. Absolute inequalities in all-cause mortality would be reduced by up to 69 (men) and 67 (women) deaths per 100,000 person-years. CONCLUSION: The potential reduction of health inequality by an elimination of social inequalities in obesity might be substantial. The reductions differ by country, cause of death and gender, suggesting that the priority given to obesity as an entry-point for tackling health inequalities should differ between countries and gender.

Social differences in avoidable mortality between small areas of 15 European cities: an ecological study.

BACKGROUND: Health and inequalities in health among inhabitants of European cities are of major importance for European public health and there is great interest in how different health care systems in Europe perform in the reduction of health inequalities. However, evidence on the spatial distribution of cause-specific mortality across neighbourhoods of European cities is scarce. This study presents maps of avoidable mortality in European cities and analyses differences in avoidable mortality between neighbourhoods with different levels of deprivation. METHODS: We determined the level of mortality from 14 avoidable causes of death for each neighbourhood of 15 large cities in different European regions. To address the problems associated with Standardised Mortality Ratios for small areas we smooth them using the Bayesian model proposed by Besag, York and Mollié. Ecological regression analysis was used to assess the association between social deprivation and mortality. RESULTS: Mortality from avoidable causes of death is higher in deprived neighbourhoods and mortality rate ratios between areas with different levels of deprivation differ between gender and cities. In most cases rate ratios are lower among women. While Eastern and Southern European cities show higher levels of avoidable mortality, the association of mortality with social deprivation tends to be higher in Northern and lower in Southern Europe. CONCLUSIONS: There are marked differences in the level of avoidable mortality between neighbourhoods of European cities and the level of avoidable mortality is associated with social deprivation. There is no systematic difference in the magnitude of this association between European cities or regions. Spatial patterns of avoidable mortality across small city areas can point to possible local problems and specific strategies to reduce health inequality which is important for the development of urban areas and the well-being of their inhabitants.

Municipal interventions against inequalities in health: The view of their managers.

BACKGROUND: European city councils are increasingly developing interventions against health inequalities. There is little knowledge about how they are perceived. This study describes and analyses good practices and challenges for local interventions on inequalities in health through the narratives of European city managers. METHODS: A qualitative study was conducted. Each participating city (Amsterdam, Barcelona, Cluj-Napoca, Helsinki, Lisbon, London, Madrid, Rotterdam) selected interventions following these criteria: at least 6 months of implementation; an evaluation performed or foreseen; the reduction of health inequalities among their objectives, and only one of the interventions selected could be based on health care. Managers of these local interventions were interviewed following an outline. Eleven individual in-depth interviews describing nine local interventions were obtained. A thematic content analysis was performed. RESULTS: One or more local interventions against health inequalities were identified in each city. Most relied on quantitative data and were linked to national strategies. Few interventions addressed socio-economic determinants. Health care, employment and education were the main determinants addressed. With variable depth, evidence-base, participation and intersectorality were regular components of the interventions. Half of them targeted the city and half some deprived neighbourhoods. Few interventions had been evaluated. Scarcity of funding and sustainability of the projects were the main perceived barriers by the managers. CONCLUSIONS: City intervention managers were familiar with health inequalities and concepts as intersectorality, participation and evidence-based action, but others such as socioeconomic aims, gradient approach, evaluation and sustainability were not so widely applied. Managers' capacities and political leadership in governance for health should be reinforced.

Socioeconomic inequalities in mortality in 16 European cities.

AIMS: To explore inequalities in total mortality between small areas of 16 European cities for men and women, as well as to analyse the relationship between these geographical inequalities and their socioeconomic indicators. METHODS: A cross-sectional ecological design was used to analyse small areas in 16 European cities (26,229,104 inhabitants). Most cities had mortality data for a period between 2000 and 2008 and population size data for the same period. Socioeconomic indicators included an index of socioeconomic deprivation, unemployment, and educational level. We estimated standardised mortality ratios and controlled for their variability using Bayesian models. We estimated relative risk of mortality and excess number of deaths according to socioeconomic indicators. RESULTS: We observed a consistent pattern of inequality in mortality in almost all cities, with mortality increasing in parallel with socioeconomic deprivation. Socioeconomic inequalities in mortality were more pronounced for men than women, and relative inequalities were greater in Eastern and Northern European cities, and lower in some Western (men) and Southern (women) European cities. The pattern of excess number of deaths was slightly different, with greater inequality in some Western and Northern European cities and also in Budapest, and lower among women in Madrid and Barcelona. CONCLUSIONS: In this study, we report a consistent pattern of socioeconomic inequalities in mortality in 16 European cities. Future studies should further explore specific causes of death, in order to determine whether the general pattern observed is consistent for each cause of death.

Social and health policies or interventions to tackle health inequalities in European cities: a scoping review.

BACKGROUND: Health inequalities can be tackled with appropriate health and social policies, involving all community groups and governments, from local to global. The objective of this study was to carry out a scoping review on social and health policies or interventions to tackle health inequalities in European cities published in scientific journals. METHODS: Scoping review. The search was done in "PubMed" and the "Sociological Abstracts" database and was limited to articles published between 1995 and 2011. The inclusion criteria were: interventions had to take place in European cities and they had to state the reduction of health inequalities among their objectives. RESULTS: A total of 54 papers were included, of which 35.2% used an experimental design, and 74.1% were carried out in the United Kingdom. The whole city was the setting in 27.8% of them and 44.4% were based on promoting healthy behaviours. Adults and children were the most frequent target population and half of the interventions had a universal approach and the other half a selective one. Half of the interventions were evaluated and showed positive results. CONCLUSIONS: Although health behaviours are not the main determinants of health inequalities, the majority of the selected documents were based on evaluations of interventions focusing on them.

Socioeconomic differences in the use of ill-defined causes of death in 16 European countries.

BACKGROUND: Cause-of-death data linked to information on socioeconomic position form one of the most important sources of information about health inequalities in many countries. The proportion of deaths from ill-defined conditions is one of the indicators of the quality of cause-of-death data. We investigated educational differences in the use of ill-defined causes of death in official mortality statistics. METHODS: Using age-standardized mortality rates from 16 European countries, we calculated the proportion of all deaths in each educational group that were classified as due to "Symptoms, signs and ill-defined conditions". We tested if this proportion differed across educational groups using Chi-square tests. RESULTS: The proportion of ill-defined causes of death was lower than 6.5% among men and 4.5% among women in all European countries, without any clear geographical pattern. This proportion statistically significantly differed by educational groups in several countries with in most cases a higher proportion among less than secondary educated people compared with tertiary educated people. CONCLUSIONS: We found evidence for educational differences in the distribution of ill-defined causes of death. However, the differences between educational groups were small suggesting that socioeconomic inequalities in cause-specific mortality in Europe are not likely to be biased.

Use and acceptance of traditional, complementary and integrative medicine in Germany-an online representative cross-sectional study.

Background: Older representative surveys show that Traditional, Complementary and Integrative Medicine (TCIM) is used by about 60% of the German population. However, no data exists for the current nationwide situation. The main aim of this cross-sectional study is to investigate the current use and acceptance of TCIM in Germany. Methods: This study is based on a representative sample of the German population aged 18-75 years. Participants were asked about the use and acceptance of TCIM. The survey was conducted online using Computer Assisted Web Interview (CAWI) in 2022 by three renowned German market research institutes on behalf of and in close coordination with the working group. The data set was analyzed descriptively and inferentially. Results: In total, 4,065 participants (52% female, 48% male, 0.4% diverse) responded completely (response rate: 21.5%). Among participants, 70% stated that they had used TCIM at some point in their lives, with 32% doing so in the last 12 months and 18% currently. The most common reason given (17%) was musculoskeletal pain. For their own health, 39% stated that TCIM is important. Traditional European Medicine was rated as very/mainly effective by 27% of participants and as partly effective by 44% (conventional medicine: 69% very/mainly effective, 19% partly effective). As a complementary treatment strategy to conventional medicine, 35% considered TCIM to be optimal ("Complementary Medicine"), 33% in combination with conventional medicine ("Integrative Medicine") and 5% without conventional medicine ("Alternative Medicine"). The majority of the participants were in favor of more research on TCIM and stated that the costs of TCIM services should be covered by health insurance companies (71% and 69%, respectively). Conclusion: These results from a representative online-population suggest that the use of TCIM in Germany remains at a high level. The nationwide relevance of TCIM should be given greater consideration in German health care policy making. TCIM should be systematically investigated using appropriate study designs and methods including high quality randomized clinical trials to investigate their effectiveness, efficacy, therapeutic safety and costs in the future.

Smoking and the potential for reduction of inequalities in mortality in Europe.

Socioeconomic inequalities in health and mortality remain a widely recognized problem. Countries with smaller inequalities in smoking have smaller inequalities in mortality, and smoking plays an important part in the explanation of inequalities in some countries. We identify the potential for reducing inequalities in all-cause and smoking-related mortality in 19 European populations, by applying different scenarios of smoking exposure. Smoking prevalence information and mortality data come from 19 European populations. Prevalence rates are mostly taken from National Health Surveys conducted around the year 2000. Mortality rates are based on country-specific longitudinal or cross-sectional datasets. Relative risks come from the Cancer Prevention Study II. Besides all-cause mortality we analyze several smoking-related cancers and chronic obstructive pulmonary disease/asthma. We use a newly-developed tool to quantify the changes in population health potentially resulting from modifying the population distribution of exposure to smoking. This tool is based on the epidemiological measure of the population attributable fraction, and estimates the impact of scenario-based distributions of smoking on educational inequalities in mortality. The potential reduction of relative inequality in all-cause mortality between those with high and low education amounts up to 26 % for men and 32 % for women. More than half of the relative inequality may be reduced for some causes of death, often in countries of Northern Europe and in Britain. Patterns of potential reduction in inequality differ by country or region and sex, suggesting that the priority given to smoking as an entry-point for tackling health inequalities should differ between countries.

Innovations in medical care and mortality trends from four circulatory diseases between 1970 and 2005.

BACKGROUND: Governments have identified innovation in pharmaceuticals and medical technology as a priority for health policy. Although the contribution of medical care to health has been studied extensively in clinical settings, much less is known about its contribution to population health. We examine how innovations in the management of four circulatory disorders have influenced trends in cause-specific mortality at the population level. METHODS: Based on literature reviews, we selected six medical innovations with proven effectiveness against hypertension, ischaemic heart disease, heart failure and cerebrovascular disease. We combined data on the timing of these innovations and cause-specific mortality trends (1970-2005) from seven European countries. We sought to identify associations between the introduction of innovations and favourable changes in mortality, using Joinpoint-models based on linear spline regression. RESULTS: For both ischaemic heart disease and cerebrovascular disease, the timing of medical innovations was associated with improved mortality in four out of five countries and five out of seven countries, respectively, depending on the innovation. This suggests that innovation has impacted positively on mortality at the population level. For hypertension and heart failure, such associations could not be identified. CONCLUSION: Although improvements in cause-specific mortality coincide with the introduction of some innovations, this is not invariably true. This is likely to reflect the incremental effects of many interventions, the time taken for them to be adopted fully and the presence of contemporaneous changes in disease incidence. Research on the impact of medical innovations on population health is limited by unreliable data on their introduction.

Socioeconomic inequalities in mortality from conditions amenable to medical interventions: do they reflect inequalities in access or quality of health care?

BACKGROUND: Previous studies have reported large socioeconomic inequalities in mortality from conditions amenable to medical intervention, but it is unclear whether these can be attributed to inequalities in access or quality of health care, or to confounding influences such as inequalities in background risk of diseases. We therefore studied whether inequalities in mortality from conditions amenable to medical intervention vary between countries in patterns which differ from those observed for other (non-amenable) causes of death. More specifically, we hypothesized that, as compared to non-amenable causes, inequalities in mortality from amenable causes are more strongly associated with inequalities in health care use and less strongly with inequalities in common risk factors for disease such as smoking. METHODS: Cause-specific mortality data for people aged 30-74 years were obtained for 14 countries, and were analysed by calculating age-standardized mortality rates and relative risks comparing a lower with a higher educational group. Survey data on health care use and behavioural risk factors for people aged 30-74 years were obtained for 12 countries, and were analysed by calculating age-and sex-adjusted odds ratios comparing a low with a higher educational group. Patterns of association were explored by calculating correlation coefficients. RESULTS: In most countries and for most amenable causes of death substantial inequalities in mortality were observed, but inequalities in mortality from amenable causes did not vary between countries in patterns that are different from those seen for inequalities in non-amenable mortality. As compared to non-amenable causes, inequalities in mortality from amenable causes are not more strongly associated with inequalities in health care use. Inequalities in mortality from amenable causes are also not less strongly associated with common risk factors such as smoking. CONCLUSIONS: We did not find evidence that inequalities in mortality from amenable conditions are related to inequalities in access or quality of health care. Further research is needed to find the causes of socio-economic inequalities in mortality from amenable conditions, and caution should be exercised in interpreting these inequalities as indicating health care deficiencies.

Cause-specific mortality time series analysis: a general method to detect and correct for abrupt data production changes.

BACKGROUND: Monitoring the time course of mortality by cause is a key public health issue. However, several mortality data production changes may affect cause-specific time trends, thus altering the interpretation. This paper proposes a statistical method that detects abrupt changes ("jumps") and estimates correction factors that may be used for further analysis. METHODS: The method was applied to a subset of the AMIEHS (Avoidable Mortality in the European Union, toward better Indicators for the Effectiveness of Health Systems) project mortality database and considered for six European countries and 13 selected causes of deaths. For each country and cause of death, an automated jump detection method called Polydect was applied to the log mortality rate time series. The plausibility of a data production change associated with each detected jump was evaluated through literature search or feedback obtained from the national data producers.For each plausible jump position, the statistical significance of the between-age and between-gender jump amplitude heterogeneity was evaluated by means of a generalized additive regression model, and correction factors were deduced from the results. RESULTS: Forty-nine jumps were detected by the Polydect method from 1970 to 2005. Most of the detected jumps were found to be plausible. The age- and gender-specific amplitudes of the jumps were estimated when they were statistically heterogeneous, and they showed greater by-age heterogeneity than by-gender heterogeneity. CONCLUSION: The method presented in this paper was successfully applied to a large set of causes of death and countries. The method appears to be an alternative to bridge coding methods when the latter are not systematically implemented because they are time- and resource-consuming.

Dimensions of Social Stratification and Their Relation to Mortality: A Comparison Across Gender and Life Course Periods in Finland.

Differences in mortality between groups with different socioeconomic positions (SEP) are well-established, but the relative contribution of different SEP measures is unclear. This study compares the correlation between three SEP dimensions and mortality, and investigates differences between gender and age groups (35-59 vs. 60-84). We use an 11% random sample with an 80% oversample of deaths from the Finnish population with information on education, occupational class, individual income, and mortality (n = 496,658; 274,316 deaths between 1995 and 2007). We estimate bivariate and multivariate Cox proportional hazard models and population attributable fractions. The total effects of education are substantially mediated by occupation and income, and the effects of occupation is mediated by income. All dimensions have their own net effect on mortality, but income shows the steepest mortality gradient (HR 1.78, lowest vs. highest quintile). Income is more important for men and occupational class more important among elderly women. Mortality inequalities are generally smaller in older ages, but the relative importance of income increases. In health inequality studies, the use of only one SEP indicator functions well as a broad marker of SEP. However, only analyses of multiple dimensions allow insights into social mechanisms and how they differ between population subgroups.

The reciprocal relationship between material factors and health in the life course: evidence from SHARE and ELSA.

The widely established health differences between people with greater economic resources and those with fewer resources can be attributed to both social causation (material factors affecting health) and health selection (health affecting material wealth). Each of these pathways may have different intensities at different ages, because the sensitivity of health to a lack of material wealth and the degree to which health can influence economic resources may change. We study the relative importance, in terms of explanatory power, of social causation and health selection, comparing the transitions from childhood to adulthood and from adulthood to old age. We use retrospective survey data from ten European countries from the Survey of Health, Ageing and Retirement in Europe (SHARELIFE, n = 18,734) and the English Longitudinal Study of Ageing (ELSA, n = 6117), and structural equations models in a cross-lagged panel design. Material wealth and health depend on their prior status, wealth more so than health. In the transition from childhood to adulthood, social causation and health selection are equally important: the standardized coefficients for men in SHARE are 0.07 and 0.06, respectively, i.e. one standard deviation increase in material wealth in childhood is associated with a 0.07 standard deviation increase in adult health. In the transition from adulthood to old age, social causation is more important than health selection (0.52 vs. 0.01), across gender and data sets. Both pathways contribute to the creation of health inequalities-however, their relative importance changes with age, which is important for understanding how health inequalities develop and how policies can address them.

Retrospective life course data from European countries on how early life experiences determine health in old age and possible mid-life mediators.

The data presented in this article is related to the research paper entitled "The long arm of childhood circumstances on health in old age: Evidence from SHARELIFE" (E. Pakpahan, R. Hoffmann, H. Kröger, 2016) [1]. It presents the distribution of socioeconomic status (SES) and health from childhood until old age in thirteen European countries. In order to capture the characteristics of longitudinal data, which resembles life course data, we divide the data into three schematic periods: childhood (up to 15 years old), adulthood (30 to 60 years old), and old age (61 to 90 years old). This data set contains respondents' life histories, ranging from childhood conditions (such as housing and health) to detailed questions on education, adult SES (working history, income, and wealth) and old age health. The data can be used not only to understand on how early life experiences determine health in old age, but also to recognise the importance of possible mid-life mediators.

The Association of Levels of and Decline in Grip Strength in Old Age with Trajectories of Life Course Occupational Position.

BACKGROUND: The study of the influence of life course occupational position (OP) on health in old age demands analysis of time patterns in both OP and health. We study associations between life course time patterns of OP and decline in grip strength in old age. METHODS: We analyze 5 waves from the Survey of Health Ageing and Retirement in Europe (n = 5108, ages 65-90). We use a pattern-mixture latent growth model to predict the level and decline in grip strength in old age by trajectory of life course OP. We extend and generalize the structured regression approach to establish the explanatory power of different life course models for both the level and decline of grip strength. RESULTS: Grip strength declined linearly by 0.70 kg (95% CI -0.74;-0.66) for men and 0.42 kg (95% CI -0.45;-0.39) for women per year. The level of men's grip strength can best be explained by a critical period during midlife, with those exposed to low OP during this period having 1.67 kg (95% CI -2.33;-1.00) less grip strength. These differences remain constant over age. For women, no association between OP and levels of or decline in grip strength was found. CONCLUSIONS: Men's OP in midlife seems to be a critical period for the level of grip strength in old age. Inequalities remain constant over age. The integration of the structured regression approach and latent growth modelling offers new possibilities for life course epidemiology.

Picking up the pieces--applying the DISEASE FILTER to health data.

This contribution presents systematic biases in the process of generating health data by using a step-by-step explanation of the DISEASE FILTER, a heuristic instrument that we designed in order to better understand and evaluate health data. The systematic bias in health data generally varies by data type (register versus survey data) and the operationalization of health outcomes. Self-reported subjective health and disease assessments, for instance, underlie a different selectivity than do data based on medical examinations or health care statistics. Although this is obvious, systematic approaches used to better understand the process of generating health data have been missing until now. We begin with the definitions and classifications of diseases that change (e.g. over time), describe the selective nature of access to and use of medical health care (e.g. depending on health insurance and gender), present biases in diagnoses (e.g. by gender and professional status), report these biases in relation to the decision for or against various treatment (e.g. by age and income), and finally outline the determinants of the treatments (ambulant versus stationary, e.g. via mobility and age). We then show how to apply the DISEASE FILTER to health data and discuss the benefits and shortcomings of our heuristic model. Finally, we give some suggestions on how to deal with biases in health data and how to avoid them.