29 Defining vulnerability subgroups among pregnant women Variables The selection of variables for the LCA started with compiling a list of all possible risk and protective factors to vulnerability based on the framework of the National Academies of Sciences, Engineering, and Medicine (3), other scientific studies and definitions of vulnerability (4, 5, 8), and expertise of the research team. Based on this list, 42 variables were available and selected in our data sources. These were divided into nine themes: individual characteristics, socioeconomic characteristics, lifestyle factors, household characteristics, self-reported health, healthcare expenditures and utilization, psychosocial characteristics, life-events and living conditions. The timing of the PHM-2016 was decisive in the choice for 1 October 2016 as baseline to include information. If data were available only on yearly basis, we included data from 2016. To increase interpretability, variables were categorized into two or three categories with the first category representing the risk factor to vulnerability. Appendix 1 provides a detailed overview of the variables, including definitions, categories and sources. Outcomes We studied the association between latent class membership and perinatal and maternal health outcomes and care utilization to validate classes. Perinatal health outcomes comprised: preterm birth (<37 weeks), small for gestational age (SGA, <10th percentile corrected for gestational age and foetal sex), preterm birth and/or SGA, and admission to a neonatal intensive-care unit (NICU) after birth. Maternal health outcomes comprised: primary and secondary caesarean section, pre-eclampsia/hypertension and postpartum haemorrhage (≥1000 ml). Outcomes regarding healthcare utilization included: not having the first antenatal care appointment (i.e., booking visit) before the 10th week of pregnancy and not receiving postpartum care (at home) after birth. Appendix 1 provides more information. Statistical analyses Latent class analysis LCA is a data-driven analysis technique that aims to structure heterogeneity in a population by classifying individuals into unobserved – or latent – homogeneous classes (25). Structuring is based on included variables. Each class is denoted by conditional probabilities for each variable to take on a certain response value (e.g. 1 or 0), with the objective to categorize individuals into the smallest possible set of distinct and interpretable latent classes. Using R version 3.6.2 (package poLCA), we estimated latent class models using all 42 variables with no prior assumptions about the optimal number of classes (26). Missing data were imputed through Multiple Imputation using Chained Equations (MICE) (Appendix 2). We started with a one-class model and stepwise increased to a 15-class model. Parameters of the latent class models were estimated by maximum likelihood. We considered both statistical fit as well as parsimony and interpretability to select the optimal model (25). To compare the competing models’ relative fit, we used the Akaike Information Criterion (AIC) (27) and sample-size adjusted Bayesian Information Criterion (aBIC) (28). Lower values 2
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