Socioeconomic position and adverse childhood experiences as risk factors for health-related behaviour change and employment adversity during the COVID-19 pandemic: insights from a prospective cohort study in the UK | BMC Public Health


study population

The study used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), an ongoing birth cohort study that enrolled 14,541 pregnant women in Avon, UK, with expected delivery dates between April 1, 1991 and December 31, 1992 [32,33,34].Mothers, children and partners of the mother were followed up with the help of clinics, questionnaires and the link to routine data. Additional eligible cases were recruited for the study when the oldest participants were approximately 7 years old. Including these, the progeny cohort consists of 14,901 participants who were alive at 1 year of age. The website gives details of available data via a fully searchable data dictionary: http://www.bristol.ac.uk/alspac/researchers/our-data/.

This study is based on 2710 descendant participants who responded between May 26 and July 5, 2020 to a questionnaire that was deployed early during the COVID-19 pandemic [31]. The questionnaire was developed and delivered using REDCap (Research Electronic Data CAPture tools), a secure web application for online data collection hosted at the University of Bristol [35].

aces

ACE measures were derived from questions about multiple forms of ACEs reported via questionnaires by participants and their mothers at multiple time points from birth to 23 years of age. Full details are described elsewhere [36]. Briefly, dichotomous exposure indicators ranging from 0 to 16 years were constructed for the ten ACEs included in the World Health Organization’s international ACE questionnaire [37]. The ten ACEs we considered were:

  1. 1.

    have ever been sexually abused or forced to engage in sexual activity or to touch someone in a sexual manner (sexual abuse)

  2. 2.

    Adult in family has ever been physically cruel to or hurt child (physical abuse)

  3. 3.

    Parents have ever been emotionally cruel to the child or often said hurtful/offensive things to the child (emotional abuse)

  4. 4.

    Child always felt left out, misunderstood, or never important to the family, parents never questioned or never listened when child talked about their free time (emotional neglect)

  5. 5.

    Parent was a daily cannabis or hard drug user, or had a drinking problem (parent substance abuse)

  6. 6.

    a parent has ever been diagnosed with schizophrenia or hospitalized for a psychiatric problem, or the parent had an eating disorder (bulimia or anorexia) during the child’s first 18 years of life, was taking medication for depression or anxiety, attempted suicide, or had a previously identified suicide Higher scoring thresholds for depression (Edinburgh Postnatal Depression Scale (EPDS) >12-13) (mental illness of parents or suicide)

  7. 7.

    Parents have ever been affected by partner’s physically cruel behavior or have ever engaged in violence towards one another, including hitting, choking, choking, hitting, and shoving (violence between parents)

  8. 8th.

    Parents separated or divorced (separation of parents)

  9. 9.

    Child was victim of bullying (bullying) weekly

  10. 10

    Parent was convicted of a criminal offense (parental conviction)

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Based on the sum of the dichotomous ACE constructs, each participant received an ACE score (0-10 ACEs), which was categorized as 0, 1, 2-3, or 4+ ACEs for comparability with previous studies.

Sep

Socioeconomic position was indicated by occupational social class at age 23 (~4 years before the onset of the pandemic) using the 3-Class Socioeconomic Classification of National Statistics (NS-SEC). [38]:

  1. 1.

    Higher managerial, administrative and specialist occupations (NS-SEC Group 1)

  2. 2.

    Middle occupations (NS-SEC Group 2)

  3. 3.

    Routine and manual occupations (NS-SEC Group 3)

  4. 4.

    Never worked and long-term unemployed (LTU)

This was derived from participants’ self-reported occupation, business/industry, and job responsibilities from a postal questionnaire administered at a median age of 23 years. Responses were coded into eight NS-SEC classes, which were then grouped into the four classes mentioned above (Supplementary Table 1). Participants were instructed to skip certain questions if they were “not doing any work,” so participants who only skipped those questions were assigned to fourth grade. Participants who reported being full-time students were excluded from the SEP analysis (Supplementary Fig. 1).

Results

The term lockdown refers to the stay-at-home order issued by the UK Government on Monday 23 March 2020. In an online questionnaire deployed between May 26 and July 5, 2020, participants (mean age 27.8 years) were asked to indicate their perceived changes in multiple health-related behaviors compared to pre-lockdown. The question asked whether each of the following activities had decreased a lot, decreased a little, stayed the same, increased a little, or increased a lot since lockdown: number of homemade meals eaten, number of meals eaten in a day, number of snacks eaten in a day , amount of physical activity/exercise, amount of sleep, alcohol and smoking/vaping. We grouped the responses into three levels: “decreased,” “stayed the same,” and “increased.”

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Respondents could select “not applicable” if they had not performed the activity prior to the lockout and did not do so at the time of completing the questionnaire. For the first five variables (number of homemade meals, number of meals, number of snacks, amount of physical activity, amount of sleep), the number of participants selecting “not applicable” was negligible (n= 6, 4, 8, 2, and 4, respectively) so these responses were coded as missing. For alcohol and smoking, N/A was coded as the fourth category, representing non-drinkers and non-smokers/vapers.

In the same questionnaire, the participants reported on their employment situation before the lockdown. We categorized the responses into employed and non-employed before the lockdown and combined this with the employment status when completing the questionnaire to derive a change in employment status during the lockdown. This had five categories: employed without change, employed on short-time work, employed and on vacation or paid or unpaid leave, no longer working and not working before the lockdown.

Participants also reported on their financial situation compared to before the COVID-19 pandemic. Possible answers were: “much worse off”, “slightly worse off”, “about the same”, “slightly better off” or “much better off”; We grouped the first two into worse off and the last two into better off. Participants reported whether they or their partner had filed new benefit claims or taken out rent or mortgage or other debt deferrals since the pandemic.

Missing data

ACE measures were derived from >500 questions answered between birth and age 23, and no participant had data on all of these questions. We therefore used multivariate multiple imputation to estimate missing values ​​that were assumed to be absent by chance. This avoids the exclusion of participants and at the same time reduces selection bias. Participants were excluded from analyzes only if they answered < 10% of the ACE questions (Supplementary Fig. 1). For participants who responded to ≥ 50% of the questions about an ACE, those questions were used to create a binary indicator of the presence/absence of that ACE. For participants who responded to < 50% of the questions about an ACE, the presence/absence of the ACE was set to absent and imputed. The ACE score was derived after imputing the presence/absence of individual ACEs. In view of known gender differences in ACE prevalence, missing data for males and females were imputed separately and the data sets were recombined prior to analysis. The imputation model included all outcomes, exposures, and covariates included in the analysis, as well as 24 auxiliary variables likely to predict absence or ACE exposure (details in Supplementary File 2). Some participants were missing SEP data, so we included 8-class NS-SEC in the imputation model [38], from which the 4-group version described above was derived. Because only participants who completed the COVID-19 questionnaire were included in the analysis, most of the analysis sample had complete outcome data. For those who did not respond to certain sections or questions, these were imputed in the same model. Using the Mice package. [39] In R version 4.0.2 we created 50 imputed datasets for males and females with 10 iterations per dataset. Imputed values ​​were combined using Rubin's rules [40] and trace plots used to check convergence of estimates.

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Statistical Analysis

All analyzes were performed in R version 4.0.2. To examine how participants in our analysis differed from the overall cohort, we compared the distribution of maternal education between included and excluded participants due to lack of data. We used multinomial logistic regression to examine associations between ACE score, individual ACEs, and SEP with health behaviors and employment outcomes during lockdown. We assessed unadjusted associations and associations adjusted for covariates. These included the ethnicity of the participants, age in years at completion of the questionnaire, and home ownership. We also adjusted for the mother’s marital status, parity and age at birth of the participant, and the educational qualifications of the mother and her partner. We tested for an interaction between each exposure and gender on the outcome.



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