Periodic Reporting for period 1 - HEALTHINEQ (Social inequalities in population health: integrating evidence from longitudinal, family-based and genetically informed data)
Periodo di rendicontazione: 2021-10-01 al 2023-03-31
Social inequalities in health are observed in all societies. These inequalities have grown markedly over the past 30 years, with those in less advantaged social positions currently expected to live 5–10 years less than those in more advantaged positions. We integrate research scattered in multiple disciplines by establishing how family background – both social as experienced through living conditions and social resources in families, and genetic as inherited from biological parents – affects health and social disadvantage within and across generations.
This study has the following research aims:
• Aim 1: to assess long-term changes in health inequalities and establish the contribution of family factors and macro-level social and economic conditions to these changes.
• Aim 2: to examine multigenerational interdependencies of social position and health in up to four generations.
• Aim 3: to estimate the causal effects of social position on health by employing molecular genetic information and to assess whether genetic associations are mediated or modified by social position and family context.
• Aim 4: to evaluate the generalisability of explanations of social inequalities in health by means of comparative research.
Our research can fill major shortcomings in the current understanding of social inequalities in health and mortality: (1) The causes of change in health inequalities over time and in different social and economic conditions remain unclear; (2) An overly individualised approach to studying health fails to account for the fact that health inequalities are produced and maintained in families and across generations; (3) Few studies to date have combined increasingly powerful molecular genetic data with population representative social and health data to reduce unobserved confounding and elucidate the interplay between the social and genetic processes that underlie health inequalities.
This study has the following research aims:
• Aim 1: to assess long-term changes in health inequalities and establish the contribution of family factors and macro-level social and economic conditions to these changes.
• Aim 2: to examine multigenerational interdependencies of social position and health in up to four generations.
• Aim 3: to estimate the causal effects of social position on health by employing molecular genetic information and to assess whether genetic associations are mediated or modified by social position and family context.
• Aim 4: to evaluate the generalisability of explanations of social inequalities in health by means of comparative research.
Our research can fill major shortcomings in the current understanding of social inequalities in health and mortality: (1) The causes of change in health inequalities over time and in different social and economic conditions remain unclear; (2) An overly individualised approach to studying health fails to account for the fact that health inequalities are produced and maintained in families and across generations; (3) Few studies to date have combined increasingly powerful molecular genetic data with population representative social and health data to reduce unobserved confounding and elucidate the interplay between the social and genetic processes that underlie health inequalities.
We have published several peer-reviewed articles within each theme of research. Our findings to date include, for example, the following:
- The excess risk of dementia relating to disadvantaged childhood circumstances was only partly attributable to attained socioeconomic position and cardiovascular health in adulthood, suggesting that the accumulation of risk begins already in early life.
- The strong household clustering of both COVID-19 incidence and severity highlights the importance of household context in the prevention and mitigation of pandemic outcomes.
- A large cut in alcohol prices in 2004 in Finland was associated with a short-term increase in adverse birth outcomes among low-income mothers, and an overall increase in abortions.
- Although the 2004 alcohol price cut increased overall alcohol-related morbidity and mortality in the adult population, the genetic susceptibility to alcohol consumption did not become more manifest in predicting them.
- Marital status and genetic propensity of coronary heart disease (CHD) are independent predictors of CHD incidence.
- Inequalities in cancer mortality vary greatly across European countries and over time (1990–2015), predominantly due to differences in the magnitude of excess mortality among lower-educated groups.
We have also initiated PhD training in the International Max Planck Research School for Population, Health and Data Science; a three-year doctoral program that merges demography, epidemiology and data science with partners across Europe and the US. A further training initiative has been the collaboration with the University of Bristol MRC Integrative Epidemiology Unit, with training relating to new methods to improve understanding of how family background, behaviours and genes act together.
- The excess risk of dementia relating to disadvantaged childhood circumstances was only partly attributable to attained socioeconomic position and cardiovascular health in adulthood, suggesting that the accumulation of risk begins already in early life.
- The strong household clustering of both COVID-19 incidence and severity highlights the importance of household context in the prevention and mitigation of pandemic outcomes.
- A large cut in alcohol prices in 2004 in Finland was associated with a short-term increase in adverse birth outcomes among low-income mothers, and an overall increase in abortions.
- Although the 2004 alcohol price cut increased overall alcohol-related morbidity and mortality in the adult population, the genetic susceptibility to alcohol consumption did not become more manifest in predicting them.
- Marital status and genetic propensity of coronary heart disease (CHD) are independent predictors of CHD incidence.
- Inequalities in cancer mortality vary greatly across European countries and over time (1990–2015), predominantly due to differences in the magnitude of excess mortality among lower-educated groups.
We have also initiated PhD training in the International Max Planck Research School for Population, Health and Data Science; a three-year doctoral program that merges demography, epidemiology and data science with partners across Europe and the US. A further training initiative has been the collaboration with the University of Bristol MRC Integrative Epidemiology Unit, with training relating to new methods to improve understanding of how family background, behaviours and genes act together.
We have gone beyond standard observational research by integrating approaches and combining data sources that have recently improved in quality and extent but have so far been rarely used together. We do not rely on any single theory, but recognize that multiple theoretical frameworks including those from epidemiology, sociology, biological sciences, and economics are required for understanding the complex socio-biological processes that produce health inequalities.
In the absence of randomised trials, both family-based and genetically informed studies together permit stronger causal inference than standard observational studies. Furthermore, we have also taken advantage of natural experiments – policy changes or naturally occurring conditions such as major educational and other policy reforms, rapid economic changes and the COVID-19 pandemic – to identify pivotal social processes in the production of health inequalities. Our approach has relied on methodological triangulation: “the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias”.
At the core of the data infrastructure is the exceptionally rich Finnish data environment that provides us with resources that are unavailable and extensively costly or simply impossible to obtain elsewhere due to legal restrictions of data access. This data environment has allowed us to study health inequalities in the context of families and changing social conditions with access to over half a century of register data on mortality and healthcare use, as well as genetically informed health data. We have made significant progress in using these data for social inequalities research.
In the absence of randomised trials, both family-based and genetically informed studies together permit stronger causal inference than standard observational studies. Furthermore, we have also taken advantage of natural experiments – policy changes or naturally occurring conditions such as major educational and other policy reforms, rapid economic changes and the COVID-19 pandemic – to identify pivotal social processes in the production of health inequalities. Our approach has relied on methodological triangulation: “the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias”.
At the core of the data infrastructure is the exceptionally rich Finnish data environment that provides us with resources that are unavailable and extensively costly or simply impossible to obtain elsewhere due to legal restrictions of data access. This data environment has allowed us to study health inequalities in the context of families and changing social conditions with access to over half a century of register data on mortality and healthcare use, as well as genetically informed health data. We have made significant progress in using these data for social inequalities research.