21.K. Workshop: Closing the data gap on child health – approaches, characteristics and methodological challenges

As stated in the 2010 and 2020 Marmot review, inequalities, especially during childhood, have lifelong impacts. Social hardships during childhood reduce the chances for children to develop and flourish and show to have a negative impact on livelong health outcomes. To monitor progress in reducing inequalities in child health and to inform policy makers and other stakeholders to develop healthy public policies, data on social determinants, health outcomes, health behaviour, the conditions in which children grow up, and the utilisation of health care are needed. But these data have been scarce for routine population health monitoring. Routine data sources like mortality statistics or hospital discharge data have shown to be not very well suited to monitor child health since they are only outcome focussed and cover only extreme events. In this workshop we aim to showcase different national approaches that aim to overcome this data gap. We want to discuss their set-up including the strength and weaknesses in terms of organisation, management, data quality, information richness, and dissemination and we want to learn about the experiences workshop participants made in their countries. First, we will learn about two approaches that combine questionnaire and examination elements. England has long-standing experiences in monitoring child health and includes children in their general population-based health examination survey, so that children's health can be seen in the context of other household members, in particular parent's socio-economic status, health status, and health-related behaviours. The German approach focusses specifically on child health and includes a module for longitudinal monitoring. Second, we will learn about experiences from Finland who make use of routinely collected data from health check-ups performed at child health clinics and school health care. While the first two approaches use representative population samples, the Finish approach make use of a whole population sample including those who utilise public health services. The third part of this workshop will focus on a specific methodological discussion regarding the identification of children with chronic diseases and the comparison of several measurement approaches. We will devote twenty minutes for the presentation of the three different data collection approaches in order to gain a good overview on their set-up and their characteristics and 15 minutes for the methodological presentation regarding the measurement of chronic diseases in children. 15 minutes are reserved for further discussion with the workshop participants and the exchange of experiences in child health monitoring worldwide. Key messages Register- and survey-based approaches for child health monitoring exist that provide data for routine population health monitoring and inform policy-making. Methodological issues, e.g. in terms of standards or measurement approaches exist and need further research and discussion.

Skills building seminar: Population health monitoring goes upstream to improve the uptake of the wider health determinants

Population health monitoring and reporting provides regular and up to date information on health outcomes, health behaviour, and parameters related to health care. If relevant and possible, information is stratified by sex, age and socio-economic indicators. Finally, the outputs of routine population health monitoring and reporting aim to inform policy makers and other stakeholders to develop healthy public policies and implement health promoting actions. However, does the processed data and information really help us to develop a strong and effective narrative on how to improve population health? Alternatively, this information might perpetuate a focus on health care and individualistic solutions for health and inhibits us to frame a narrative focussed on the social, political, and commercial determinants of health. If we (1) aim to follow a Health in All Policies approach, (2) agree that changes in population health can best be reached if health determinants are tackled upstream, and (3) acknowledge that health is mainly determined outside the health sector, we probably miss parts of the picture with our current routine population health monitoring and reporting activities. In this workshop, we aim to explore data and information sources that have the potential to expand our general monitoring and reporting focus. The first presentation will focus on the living environment and provides an overview on geospatial information like land use, road and rail networks, amenities, and air pollution, available online at EU level. The second presentation puts the focus on health promoting processes within Finnish municipalities. The tool, that follows a Health in All Policies approach, has emerged to a resource that collects and presents this information routinely. The third presentation will discuss methods for involving the community in making decisions about measuring what is important for their own health and wellbeing. It will also discuss taking their ideas forward and identifying validated tools that can be used to measure what the community want. The last presentation highlights the relevance of policy analysis, for example in the area of food and nutrition, and underlines how this analytical approach can be used to communicate actions needed upstream. Experts from public health authorities and universities are invited to discuss during this skills building seminar how the focus on the wider health determinants and upstream prevention can be strengthened in routine population health monitoring and reporting. The presentations are followed by an interactive part of 30 minutes to discuss the applicability of the presented approaches and further possibilities to broaden the scope of population health monitoring and reporting. Key messages The data and information usually used for population health monitoring and reporting lacks information necessary to promote a Health in All Policies perspective and to push upstream prevention. To frame narratives that underline the importance of the social, political and commercial determinants of health, we need to expand our data and information sources and include policy analysis.

Validating record-linkage based methodology for addressing health survey non-participation

Background As a consequence of declining levels of participation in health surveys, the results purported to be population-representative may be biased. Traditional adjustments for non-participation, such as weighting, can fail to correct for such biases. We aim to validate our developed methodology, which simulates non-participants, and compare results from the inferred sample to the ’gold standard’ sample of participants and true non-participants, and participants alone. Methods Participants and non-participants of the Finnish Health 2000 survey, and a contemporaneous population sample are available, with alcohol-related hospitalisations and deaths (“harms”, individually record-linked for all Health 2000 invitees). Synthetic observations on non-participants were simulated through comparison of participants and population sample. Alcohol consumption of true and inferred non-participants were multiply imputed based on harms and education as well as age and sex, assuming data are Missing At Random (MAR). Results are compared via the relative differences (RD) between the inferred sample and 1) gold standard sample, and 2) participants alone. Results Average weekly estimates for men are 129g in the inferred sample, and 130g in the gold standard (RD -1.2%, 95%CI -2.0, -0.4%), and 35g for women in both samples (RD -0.8%; -1.9, 0.3%). Estimates for men with secondary levels of education had the greatest RD (-1.9%; -3.3, -0.5%). Comparisons between the participants and the inferred sample revealed few differences. Conclusions All RD between the inferred and gold standard samples lie within our ±5% acceptability limits, in support of the use of our methodology for adjusting for non-participation in health surveys. However, under MAR, there are no significant differences between the results generated from the inferred sample and the participants alone. Further work exploring Missing Not At Random scenarios is required to ensure utility for reliable population health monitoring. Key messages Survey weights alone cannot adjust for non-representativeness, but we have shown that data linkage can be used to match the characteristics and outcomes of the selected sample. Non-participation in health surveys may be adjusted for using our methodology, with further exploration into alternative missing data scenarios required.

Geospatial data sources to produce new evidence for routine population health monitoring

Background Many public agencies collect geospatial data in relation to Environment and Territorial Planning. This data is harmonized, standardized, made available online, and it is often collected for a group of countries. These characteristics make this data particularly useful for population health studies, yet in public health there is a certain lack of knowledge with regard to this type of data. The large datasets, the data format and the ways to access this data all hamper their use. The aim of this work is to present an overview of geospatial databases that produce routinely available geospatial data able to support population health monitoring and interventions. The process of how to transform this data in evidence at the local scale will also be highlighted. Methods First, we conducted a survey via the websites of international public institutes with relevant geospatial data gathered for Europe. Second, we identified how we could transform the data into evidence relevant to both, population health studies and decision makers, namely at the local level. Results At the EU level, the European Territorial Observatory Network (ESPON), EUROSTAT, the Joint Research Centre and the Environment European Agency (EEA) are some examples of public agencies both producing and offering access to geospatial data. They are responsible for collecting data regarding, e.g., land use, road and rail networks, amenities and pollution. Although geospatial data has been compiled all over Europe, it can be used to produce evidence at the local level. For instance, it is possible to extract information on the greenspace area at a local administrative level from the Corine Land Cover project, managed by the EEA, and then, measure the share of area per inhabitant. Conclusions Geospatial data have much more to offer than the obvious location factor. It brings new evidence at the local level, supporting studies and empowering decision-makers at all levels.