Inequalities in the distribution of COVID-19-related financial difficulties for Australian families with young children.

BACKGROUND: We examine (1) the frequency of financial difficulties in Australian families with young children (0-8 years) in the early and later phases of the pandemic; (2) the extent to which parents' pre-pandemic socio-economic disadvantage (SED) predicted financial difficulties; and (3) whether grandparent intergenerational SED further amplified this risk. METHOD: Data: Australian Temperament Project (ATP; established 1983, N = 2443) and ATP Generation 3 study (ATPG3; established 2012; N = 702), of which 74% (N = 553) completed a COVID-specific module in the early (May-September 2020) and/or later (October-December 2021) phases of the pandemic. OUTCOMES: Parent-reported loss of employment/reduced income, difficulty paying for essentials, and financial strain. EXPOSURES: Pre-pandemic parent and grandparent education and occupation. ANALYSIS: Logistic regressions, estimated via generalized estimating equations, were used to examine associations between the pre-pandemic SED of parents and grandparents and their interaction with financial difficulties, adjusting for potential confounders. RESULTS: At both pandemic time points, a third of parents reported adverse financial impacts (early: 34%, 95% confidence interval [CI] = 30-38; later: 32%, 95% CI = 28-36). Each standard deviation increase in the parents' pre-pandemic SED was associated with a 36% increase in the odds of reporting multiple financial difficulties (odds ratio [OR] = 1.36, 95% CI = 1.04-1.78). There was little evidence of an interaction between the SED of parents and grandparents. CONCLUSIONS: Financial impacts related to the COVID-19 pandemic were common and, irrespective of grandparent SED, disproportionately borne by parents with higher pre-pandemic SED. Given the well-established relationship between disadvantage and child health and development, sustained and well-targeted government supports will be critical to minimizing adverse impacts in years to come.

Can mental health competence reduce the higher risk of smoking initiation among teenagers with parents who smoke?

BACKGROUND: Parental smoking strongly influences adolescent smoking, yet few studies have examined factors that may protect against this. We investigated whether skills-based components of positive mental health ('mental health competence', MHC) modified the association between parental and teenager smoking, in the UK-representative Millennium Cohort Study (approximately 18 000 children, born 2000-02; analytic sample: n = 10 133). METHODS: Cohort members (CMs) reported at 14 years (y) whether they had ever smoked cigarettes. A dichotomized variable indicated whether one/both parents smoked when CMs were 11 y. A four-class latent MHC measure captured learning skills and prosocial behaviours at 11 y: High, High-Moderate, Moderate, Low. We examined effect measure modification (on the additive scale) by comparing risk differences (RDs) for CM smoking according to parental smoking, within each MHC class. We then estimated RDs for CM smoking according to combinations of parental smoking and MHC. Analyses accounted for confounding, sample design, attrition and item missingness. RESULTS: CMs were more likely to smoke cigarettes if their parent(s) smoked (27%) than CMs with no parent(s) who smoked (11%; RD: 16%). When stratified by MHC, RDs were stronger for low MHC (21%; 95% CI 11-31%) than other MHC classes (ranging: 7-11%). Compared to CMs with high MHC and non-smoker parents, those with low MHC and parent(s) who smoked had an RD of 28% (95% CI 20-36%). This was greater than the sum of RDs for those with low MHC and non-smoker parent(s) [7% (2-14%)] plus those with high MHC and whose parent(s) smoked [11% (7-15%)]. There was limited effect measure modification by moderate or High-Moderate MHC. CONCLUSION: Improving MHC to moderate levels may help reduce intergenerational transference of smoking.

Is Mental Health Competence in Childhood Associated With Health Risk Behaviors in Adolescence? Findings From the UK Millennium Cohort Study.

PURPOSE: Promoting positive mental health, particularly through enhancing competencies (such as prosocial behaviors and learning skills), may help prevent the development of health risk behaviors in adolescence and thus support future well-being. Few studies have examined how mental health competencies in childhood are associated with adolescent health risk behaviors, which could inform preventative approaches. METHODS: Using UK Millennium Cohort Study data (n = 10,142), we examined how mental health competence (MHC) measured at the end of elementary school (11 years) is associated with self-reported use of cigarettes, e-cigarettes, alcohol, illegal drugs, antisocial behavior, and sexual contact with another young person at age 14 years. A latent measure of MHC was used, capturing aspects of prosocial behavior and learning skills, categorized as high MHC, high-moderate MHC, moderate MHC, and low MHC. Logistic and multinomial regression estimated odds ratios and relative risk ratios for binary and categorical outcomes, respectively, before and after adjusting for confounders. Weights accounted for sample design and attrition and multiple imputation for item missingness. RESULTS: Those with low, moderate, or high-moderate MHC at age 11 years were more likely to have taken part in health risk behaviors at age 14 years compared with those with high MHC. The largest associations were seen for low MHC with binge drinking (relative risk ratio: 1.6 [95% confidence interval: 1.1-2.4]), having tried cigarettes (odds ratio: 2.2 [95% confidence interval: 1.6-3.1]) and tried illegal drugs (odds ratio: 2.0 [95% confidence interval: 1.3-3.1) after adjusting for confounders (which attenuated results but largely maintained significant findings). CONCLUSIONS: MHC in late childhood is associated with health risk behaviors in midadolescence. Interventions that increase children's MHC may support healthy development during adolescence, with the potential to improve health and well-being through to adulthood.

Have health inequalities changed during childhood in the New Labour generation? Findings from the UK Millennium Cohort Study.

OBJECTIVES: To examine how population-level socioeconomic health inequalities developed during childhood, for children born at the turn of the 21st century and who grew up with major initiatives to tackle health inequalities (under the New Labour Government). SETTING: The UK. PARTICIPANTS: Singleton children in the Millennium Cohort Study at ages 3 (n=15 381), 5 (n=15 041), 7 (n=13 681) and 11 (n=13 112) years. PRIMARY OUTCOMES: Relative (prevalence ratios (PR)) and absolute health inequalities (prevalence differences (PD)) were estimated in longitudinal models by socioeconomic circumstances (SEC; using highest maternal academic attainment, ranging from 'no academic qualifications' to 'degree' (baseline)). Three health outcomes were examined: overweight (including obesity), limiting long-standing illness (LLSI), and socio-emotional difficulties (SED). RESULTS: Relative and absolute inequalities in overweight, across the social gradient, emerged by age 5 and increased with age. By age 11, children with mothers who had no academic qualifications were considerably more likely to be overweight as compared with those with degree-educated mothers (PR=1.6 (95% CI 1.4 to 1.8), PD=12.9% (9.1% to 16.8%)). For LLSI, inequalities emerged by age 7 and remained at 11, but only for children whose mothers had no academic qualifications (PR=1.7 (1.3 to 2.3), PD=4.8% (2% to 7.5%)). Inequalities in SED (observed across the social gradient and at all ages) declined between 3 and 11, although remained large at 11 (eg, PR=2.4 (1.9 to 2.9), PD=13.4% (10.2% to 16.7%) comparing children whose mothers had no academic qualifications with those of degree-educated mothers). CONCLUSIONS: Although health inequalities have been well documented in cross-sectional and trend data in the UK, it is less clear how they develop during childhood. We found that relative and absolute health inequalities persisted, and in some cases widened, for a cohort of children born at the turn of the century. Further research examining and comparing the pathways through which SECs influence health may further our understanding of how inequalities could be prevented in future generations of children.

The effect of survey method on survey participation: analysis of data from the Health Survey for England 2006 and the Boost Survey for London.

BACKGROUND: There is a need for local level health data for local government and health bodies, for health surveillance and planning and monitoring of policies and interventions. The Health Survey for England (HSE) is a nationally-representative survey of the English population living in private households, but sub-national analyses can be performed only at a regional level because of sample size. A boost of the HSE was commissioned to address the need for local level data in London but a different mode of data collection was used to maximise participant numbers for a given cost. This study examines the effects on survey and item response of the different survey modes. METHODS: Household and individual level data are collected in HSE primarily through interviews plus individual measures through a nurse visit. For the London Boost, brief household level data were collected through interviews and individual level data through a longer self-completion questionnaire left by the interviewer and collected later. Sampling and recruitment methods were identical, and both surveys were conducted by the same organisation. There was no nurse visit in the London Boost. Data were analysed to assess the effects of differential response rates, item non-response, and characteristics of respondents. RESULTS: Household response rates were higher in the 'Boost' (61%) than 'Core' (HSE participants in London) sample (58%), but the individual response rate was considerably higher in the Core (85%) than Boost (65%). There were few differences in participant characteristics between the Core and Boost samples, with the exception of ethnicity and educational qualifications. Item non-response was similar for both samples, except for educational level. Differences in ethnicity were corrected with non-response weights, but differences in educational qualifications persisted after non-response weights were applied. When item non-response was added to those reporting no qualification, participants' educational levels were similar in the two samples. CONCLUSION: Although household response rates were similar, individual response rates were lower using the London Boost method. This may be due to features of London that are particularly associated with lower response rates for the self-completion element of the Boost method, such as the multi-lingual population. Nevertheless, statistical adjustments can overcome most of the demographic differences for analysis. Care must be taken when designing self-completion questionnaires to minimise item non-response.

The effect of mode and context on survey results: analysis of data from the Health Survey for England 2006 and the Boost Survey for London.

BACKGROUND: Health-related data at local level could be provided by supplementing national health surveys with local boosts. Self-completion surveys are less costly than interviews, enabling larger samples to be achieved for a given cost. However, even when the same questions are asked with the same wording, responses to survey questions may vary by mode of data collection. These measurement differences need to be investigated further. METHODS: The Health Survey for England in London ('Core') and a London Boost survey ('Boost') used identical sampling strategies but different modes of data collection. Some data were collected by face-to-face interview in the Core and by self-completion in the Boost; other data were collected by self-completion questionnaire in both, but the context differed. Results were compared by mode of data collection using two approaches. The first examined differences in results that remained after adjusting the samples for differences in response. The second compared results after using propensity score matching to reduce any differences in sample composition. RESULTS: There were no significant differences between the two samples for prevalence of some variables including long-term illness, limiting long-term illness, current rates of smoking, whether participants drank alcohol, and how often they usually drank. However, there were a number of differences, some quite large, between some key measures including: general health, GHQ12 score, portions of fruit and vegetables consumed, levels of physical activity, and, to a lesser extent, smoking consumption, the number of alcohol units reported consumed on the heaviest day of drinking in the last week and perceived social support (among women only). CONCLUSION: Survey mode and context can both affect the responses given. The effect is largest for complex question modules but was also seen for identical self-completion questions. Some data collected by interview and self-completion can be safely combined.