Skills
- Large administrative data wrangling and analysis in the Central Bureau for Statistics, Netherlands (CBS) Microdata Environment
- Administrative Health Data Wrangling and Analysis
- Environmental pollution and pesticide data analysis
- Visualisation of large data (n ~ 1.8 million)
- Mediation analysis with multiple mediators
Abstract
Background. Nearly 16% percent of children around the world live in
poverty and those born into it are especially susceptible to its effects. Although
poverty is consistently linked to adverse health outcomes, it is a heterogenous
phenomenon and depending on the context its associated risk factors can differ
both in nature and in severity. Interventions during the first years of life can
be both effective and cost efficient. Given the complexity of context-specific
poverty risk factors and their interactions with each other, the current project
takes a data-driven approach to identifying intervention targets that can aid
policymakers. The goal of the study is to investigate the association between
family poverty and maternal and infant health in The Netherlands, as well as
potential mediating intervention targets. Method. Data used for this study
is part of a large population-based dataset consisting of records from official
healthcare providers and the Central Bureau for Statistics in The Netherlands.
It covers a time period of 10 years (2010-2020) with 1.8 million pregnancies.
We take a two-step approach to the analysis to address the goals of the research
question – 1) a descriptive analysis of relevant poverty-related factors in re-
gards to child health; 2) causal pre-registered confirmatory mediation analyses
of theory-informed mediators of the effects of poverty on child birth outcomes.
After a conceptual pre-registration of the hypotheses we split 10% of the data for
feasibility assessment of the planned analyses. Following the 10% holdout sam-
ple, an analytic pre-registration of the methods will be submitted, after which
analyses will be conducted on the remaining 90% of the cases. Additional ro-
bustness tests will also be conducted, as well as analyses replicating the results
using different variables to account for income, such as education, which can
often be the only proxy for socioeconomic status that researchers have access
to.
poverty and those born into it are especially susceptible to its effects. Although
poverty is consistently linked to adverse health outcomes, it is a heterogenous
phenomenon and depending on the context its associated risk factors can differ
both in nature and in severity. Interventions during the first years of life can
be both effective and cost efficient. Given the complexity of context-specific
poverty risk factors and their interactions with each other, the current project
takes a data-driven approach to identifying intervention targets that can aid
policymakers. The goal of the study is to investigate the association between
family poverty and maternal and infant health in The Netherlands, as well as
potential mediating intervention targets. Method. Data used for this study
is part of a large population-based dataset consisting of records from official
healthcare providers and the Central Bureau for Statistics in The Netherlands.
It covers a time period of 10 years (2010-2020) with 1.8 million pregnancies.
We take a two-step approach to the analysis to address the goals of the research
question – 1) a descriptive analysis of relevant poverty-related factors in re-
gards to child health; 2) causal pre-registered confirmatory mediation analyses
of theory-informed mediators of the effects of poverty on child birth outcomes.
After a conceptual pre-registration of the hypotheses we split 10% of the data for
feasibility assessment of the planned analyses. Following the 10% holdout sam-
ple, an analytic pre-registration of the methods will be submitted, after which
analyses will be conducted on the remaining 90% of the cases. Additional ro-
bustness tests will also be conducted, as well as analyses replicating the results
using different variables to account for income, such as education, which can
often be the only proxy for socioeconomic status that researchers have access
to.
