Neighbourhood-related socioeconomic perinatal health inequalities: An illustration of the mediational g-formula and considerations for the big data context.

BACKGROUND: Advances in computing power have enabled the collection, linkage and processing of big data. Big data in conjunction with robust causal inference methods can be used to answer research questions regarding the mechanisms underlying an exposure-outcome relationship. The g-formula is a flexible approach to perform causal mediation analysis that is suited for the big data context. Although this approach has many advantages, it is underused in perinatal epidemiology and didactic explanation for its implementation is still limited. OBJECTIVE: The aim of this was to provide a didactic application of the mediational g-formula by means of perinatal health inequalities research. METHODS: The analytical procedure of the mediational g-formula is illustrated by investigating whether the relationship between neighbourhood socioeconomic status (SES) and small for gestational age (SGA) is mediated by neighbourhood social environment. Data on singleton births that occurred in the Netherlands between 2010 and 2017 (n = 1,217,626) were obtained from the Netherlands Perinatal Registry and linked to sociodemographic national registry data and neighbourhood-level data. The g-formula settings corresponded to a hypothetical improvement in neighbourhood SES from disadvantaged to non-disadvantaged. RESULTS: At the population level, a hypothetical improvement in neighbourhood SES resulted in a 6.3% (95% confidence interval [CI] 5.2, 7.5) relative reduction in the proportion of SGA, that is the total effect. The total effect was decomposed into the natural direct effect (5.6%, 95% CI 5.1, 6.1) and the natural indirect effect (0.7%, 95% CI 0.6, 0.9). In terms of the magnitude of mediation, it was observed the natural indirect effect accounted for 11.4% (95% CI 9.2, 13.6) of the total effect of neighbourhood SES on SGA. CONCLUSIONS: The mediational g-formula is a flexible approach to perform causal mediation analysis that is suited for big data contexts in perinatal health research. Its application can contribute to providing valuable insights for the development of policy and public health interventions.

Creating a sense of urgency and provoking action - An example on the use of heat maps to address perinatal health inequalities.

Health outcomes of mothers and their (unborn) children in the perinatal period, i.e., during pregnancy and shortly after birth, can vary by geographical location. This is often due to differences in exposure to medical and social risk factors. Policies aimed at reducing inequalities in perinatal health can provide significant long-term health benefits, especially for (unborn) children. However, a lack of insight into regional perinatal health inequalities means that perinatal health is not always a priority in policy formulation. Novel methods should be used to draw attention to these inequalities, spark interdisciplinary debate and encourage collaborative initiatives. In this commentary, we propose that the development of heat maps that visualize perinatal health outcomes, and risk factors for those outcomes, could be a valuable tool in doing this. Heat maps are a data visualization technique that uses color variations to emphasize value differences between areas. Visualizing health inequalities could potentially create a sense of urgency among (local) stakeholders to initiate polices aimed at improving perinatal health. We illustrate the targeted use of heat maps with an example from the city of Rotterdam, the Netherlands. Large perinatal health inequalities between neighborhoods were visualized in heat maps by a team from the Erasmus Medical Center to bring these inequalities to the attention of the municipality of Rotterdam. Local collaborative initiatives were set up to reduce perinatal health inequalities. These local initiatives formed the foundation for later national policies, including proposals to online implement heat maps regarding perinatal health topics, that are still ongoing today.

Social determinants of vulnerability in the population of reproductive age: a systematic review.

BACKGROUND: The health of an (unborn) child is largely determined by the health and social determinants of its parents. The extent to which social determinants of parents or prospective parents affect their own health depends partly on their coping or resilience abilities. Inadequate abilities allow negative effects of unfavourable social determinants to prevail, rendering them vulnerable to adverse health outcomes. Addressing these determinants in the reproductive-aged population is therefore a key approach in improving the health of the future generation. This systematic review aims to synthesise evidence on social determinants of vulnerability, i.e., inadequate coping or low resilience, in the general population of reproductive age. METHODS: The databases EMBASE, Medline, PsycINFO, CINAHL, Google Scholar, Web of Science, and Cochrane Library, were systematically searched from database inception to December 2th 2021. Observational studies examining social determinants and demographics in relation to vulnerability among the general population of reproductive age (men and women aged 18-40 years), conducted in a high-income country in Europe or North America, Australia or New Zealand were eligible for inclusion. Relevant data was extracted from each included article and findings were presented in a narrative and tabulated manner. RESULTS: We identified 40,028 unique articles, of which 78 were full text reviewed. Twenty-five studies were included, of which 21 had a cross-sectional study design (84%). Coping was the most frequently assessed outcome measure (n = 17, 68%). Thirty social determinants were identified. Overall, a younger age, lower socioeconomic attainment, lack of connection with the social environment, and adverse life events were associated with inadequate coping or low resilience. CONCLUSIONS: This review shows that certain social determinants are associated with vulnerability in reproductive-aged individuals. Knowing which factors make people more or less vulnerable carries health-related implications. More high-quality research is needed to obtain substantial evidence on the strength of the effect of these social conditions in this stage of life.

Non-medical determinants of perinatal health: protocol for a systematic review with meta-analysis.

INTRODUCTION: Research focusing on the associations between non-medical determinants and unfavourable perinatal health outcomes is increasing. Despite increasing knowledge on this theme, it still remains unclear to what extent social, environmental and lifestyle factors contribute to these unfavourable outcomes. Therefore, we aim to provide a systematic review, preferably with meta-analysis, in order to provide insight into the associations between non-medical determinants and perinatal mortality, preterm birth and being small for gestational age (SGA). METHODS AND ANALYSIS: Observational studies performed in European countries studying the associations between non-medical determinants and unfavourable perinatal health outcomes will be included. Primary outcomes of interest are perinatal mortality, preterm birth and SGA. To retrieve potential eligible articles, a systematic literature search was performed in the following online databases on 5 October 2018: MEDLINE, Embase, Web of Science, Cochrane and Google Scholar. Additionally, a reference list check and citation search will be performed. Data of the included articles will be extracted using a standardised and piloted data extraction form. Risk of bias will be assessed using the Newcastle-Ottawa Scale. The study selection and data extraction process will be performed by two reviewers independently. Disagreements will be resolved through discussion with a third reviewer. The pooled effects will be calculated separately for each association found between one of the outcome measures and the non-medical determinants using a random effects model. Heterogeneity of the studies will be assessed using the I2 statistic. ETHICS AND DISSEMINATION: No ethical approval is necessary for a systematic review with meta-analysis. The findings will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42018056105.