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Social vulnerability indices (SVI) can predict communities’ vulnerability and resilience to public health threats such as drought, food insecurity or infectious diseases. Parity has yet to be investigated as an indicator of social vulnerability in young women. We adapted an SVI score, previously used by the US Centre for Disease Control (CDC), and calculated SVI for young urban South African women (n = 1584; median age 21.6, IQR 3.6 years). Social vulnerability was more frequently observed in women with children and increased as parity increased. Furthermore, young women classified as socially vulnerable were 2.84 times (95% CI 2.10–3.70; p < 0.001) more likely to report household food insecurity. We collected this information in 2018–2019, prior to the current global COVID-19 pandemic. With South Africa having declared a National State of Disaster in March 2020, early indicators suggest that this group of women have indeed been disproportionally affected, supporting the utility of such measures to inform disaster relief efforts.
This cross-sectional study of young women formed part of the World Health Organization (WHO) Healthy Life Trajectories Initiative (HeLTI), with intervention cohorts in Canada, China, India and South Africa [16–18]. The primary aim of HeLTI was to examine the impact of a complex continuum of care intervention beginning in the preconception period on maternal and child health to offset obesity risk in early childhood. We recruited women (June 2018 to July 2019) from randomly selected areas of Soweto using k-means clustering to define thirty communities with a 1km2 radius each in such a way as to minimise the sum of squares within each cluster [19]. The Human Research Ethics Committee (Medical) at the University of the Witwatersrand approved the study (M171137, M1811111). Fieldwork teams visited households to record type of residence (formal or informal), household density and the number of household assets. Eligible women from the household (ages 18.0–25.9 years; not pregnant, and no previous diagnosis of cancer, Type I Diabetes, or Epilepsy) attended the research unit in Soweto for an interviewer-administered survey and physical measurements. Survey domains included (1) socio-demographic (education, employment, relationships); (2) general health (medical and reproductive history, disease and medication use, HIV status, tobacco and alcohol use); (3) mental health (stressful life events, depression and anxiety); and (4) food insecurity. Survey questions used the WHO STEPS protocol, the United States (US) Centre for Disease Control (CDC) Global Adult Tobacco Survey [20], the WHO Alcohol Use Disorders (WHO-AUDIT) Test [21], the Adverse Childhood Experiences (ACE) Questionnaire [22], the PHQ-9 (score of 0–27; cut off ≥ 10 applied for probable depression) [23], and the General Anxiety Scale (GAD-7; score of 0–21; cut off ≥ 10 indicating moderate to severe symptoms) [23]. We recorded cell phone and email access using the questions: “ Do you currently have: (a) Your own number you can always be contacted on (cell phone); and (b) Your own email address that you are able to check regularly?”. We assessed food insecurity using an adapted Community Childhood Hunger Identification Project (CCHIP) index [24]. Physical measurements included blood pressure and anthropometry (height, weight and waist circumference (central adiposity)); all measured in triplicate and following WHO training on standardisation [25, 26] and International guidelines [27]. We collected and managed study data using REDCap electronic data capture tools [28]. Deidentified data sharing is available upon request, please contact Lisa Ware. We calculated SVI score for each participant using a similar set of composite measures to those previously used by the CDC [29] across four domains: (1) socioeconomic status (SES: income, poverty, employment and education); (2) household composition and disability (age, single parenting and disability); (3) housing and transportation (housing structure, crowding and vehicle access) and (4) minority status and language (race, ethnicity and English-language proficiency). We replaced income and poverty with household asset score from a list of 13 common assets (electricity, fridge, stove, vacuum cleaner, washing machine, satellite TV, DVD player, car, TV, landline telephone, cell phone, computer/laptop/tablet and internet access), shown to be central in economic assessment of the household and sensitive to change over time [30, 31]. We recorded unemployment for participants not currently working or studying and assessed education as the total number of years in education. We reviewed ages of household residents for vulnerable age groups (< 18 or ≥ 65 years). We did not use single parenting (≥ 1 child and single or not living with partner) so as not to bias results in this analysis. We recorded disability if participants reported claiming employment disability support. We recorded housing structures as formal (houses or apartments made of construction materials such as brick or concrete) or informal (shacks or containers). We determined crowding or household density by the number of household residents that stayed in the home most nights over the last 3 months, divided by the number of rooms available for sleeping. We assessed transport access by whether the household had a motorcar. We did not employ these indicators as Soweto is predominantly black African ethnicity (98.5%) with the majority of inhabitants speaking at least one of the five mainly used African languages in the area and very few (2.3%) speaking English as a first language [32]. We calculated the SVI score as a count of the number of individual variables meeting the criteria in Table Table11 to generate a score between zero and eight. This process identified individuals as socially vulnerable if their SVI score was in the highest 90th percentile. Social vulnerability domains and indicators used We compared median continuous variables using independent-samples Kruskal–Wallis test with Dunn-Bonferroni post-hoc pairwise comparisons and categorical data compared using Pearson Chi square test. We used Spearman partial correlation to test if there was an association between continuous or ordinal variables, or both, while controlling for age. We applied logistic regression to determine the effect of parity, age, and maternal age at first birth on SVI classification, and multinomial logistic regression to assess whether SVI predicted household food insecurity. For all analysis, we used IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp, Armonk, NY).
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