59 Predicting population-level vulnerability among pregnant women Study population The study population consisted of 4172 unique women with a pregnancy and childbirth in 2017 or 2018 who participated in the PHM-2016 prior to pregnancy. Details on selecting the study population are described in Chapter 2 (12). To illustrate the prevalence and spatial variation of multidimensional vulnerability at national level, all unique registered pregnancies in Perined from 2017 to 2021 were considered (n = 807.904) (17). Missing data were imputed through Multiple Imputation using Chained Equations (MICE), leading to five imputed datasets (27). Independent variables Analogous to our previous study, we included 42 variables in the predictive models (12). Details on these variables have been described in Appendix 1 of our previous study (Chapter 2) (12). The first category in each variable denotes the risk factor to vulnerability. Of those 42 variables, 31 variables concerned routinely collected data available for all pregnant women in DIAPER (n = 807.904). Those included individual characteristics (age, ethnicity, parity, asylum seeker status), socioeconomic characteristics (educational level, household income, socioeconomic position by occupational status, debts and payment arrears, permanent employment contract, and full-time employment contract), household characteristics (type of household, marital status, dissolution of marriage, household size, and youth support utilization), healthcare expenditures and utilization (total healthcare expenditures, General Practitioner’s (GP) expenditures, hospital expenditures, medication use, and addiction related care utilization), psychosocial characteristics (mental healthcare utilization, mild intellectual disability), life events (crime suspect, crime victim, having been detained, frequent moving, loss of a family member), living conditions (home ownership, motorized vehicle ownership, proximity to GP office, liveability neighbourhood). The other 11 variables were derived from the PHM-2016 and consequently only available for 4172 individuals. These variables included lifestyle factors (smoking, alcohol use, physical activity, Body Mass Index (BMI)), self-reported health (perceived health status, long-term illness, restricted by health), psychosocial characteristics (risk of depression or anxiety disorders, loneliness, feelings of control over life) and socioeconomic characteristics (insufficient financial resources). Outcome: multidimensional vulnerability The outcome measure is multidimensional vulnerability, as derived from our previous study (Chapter 2) (12). Women classified into the ‘multidimensional vulnerability’-class share a combination of multiple risk factors to vulnerability in several domains and lack protective factors. It is not a straightforward equation and risk factors vary across individuals. Most present risk factors include not having an income or receiving benefits, rental housing, high GP healthcare expenditures, long-term illness, negative self-perceived health, and elevated risks of feeling lonely, depressed or anxious. 3
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