Emulating a target trial of intensive nurse home visiting in the policy-relevant population using linked administrative data.

BACKGROUND: Populations willing to participate in randomized trials may not correspond well to policy-relevant target populations. Evidence of effectiveness that is complementary to randomized trials may be obtained by combining the 'target trial' causal inference framework with whole-of-population linked administrative data. METHODS: We demonstrate this approach in an evaluation of the South Australian Family Home Visiting Program, a nurse home visiting programme targeting socially disadvantaged families. Using de-identified data from 2004-10 in the ethics-approved Better Evidence Better Outcomes Linked Data (BEBOLD) platform, we characterized the policy-relevant population and emulated a trial evaluating effects on child developmental vulnerability at 5 years (n = 4160) and academic achievement at 9 years (n = 6370). Linkage to seven health, welfare and education data sources allowed adjustment for 29 confounders using Targeted Maximum Likelihood Estimation (TMLE) with SuperLearner. Sensitivity analyses assessed robustness to analytical choices. RESULTS: We demonstrated how the target trial framework may be used with linked administrative data to generate evidence for an intervention as it is delivered in practice in the community in the policy-relevant target population, and considering effects on outcomes years down the track. The target trial lens also aided in understanding and limiting the increased measurement, confounding and selection bias risks arising with such data. Substantively, we did not find robust evidence of a meaningful beneficial intervention effect. CONCLUSIONS: This approach could be a valuable avenue for generating high-quality, policy-relevant evidence that is complementary to trials, particularly when the target populations are multiply disadvantaged and less likely to participate in trials.

The Hospital Burden Associated With Intergenerational Contact With the Welfare System in Australia.

Importance: Intergenerational welfare contact is a policy issue because of the personal and social costs of entrenched disadvantage; yet, few studies have quantified the burden associated with intergenerational welfare contact for health. Objective: To examine the proportion of individuals who experienced intergenerational welfare contact and other welfare contact types and to estimate their cause-specific hospital burden. Design, Setting, and Participants: This cohort study used a whole-of-population linked administrative dataset of individuals followed from birth to age 20 years using deidentified data from the Better Evidence Better Outcomes Linked Data platform (Australian Government Centrelink [welfare payments], birth registration, perinatal birth records, and inpatient hospitalizations). Participants included individuals born in South Australia from 1991 to 1995 and their parents. Analysis was undertaken from January 2020 to June 2022. Exposures: Using Australian Government Centrelink data, welfare contact was defined as 1 or more parents receiving a means-tested welfare payment (low-income, unemployment, disability, or caring) when children were aged 11 to 15 years, or youth receiving payment at ages 16 to 20 years. Intergenerational welfare contact was defined as welfare contact occurring in both parent and offspring generations. Offspring were classified as: no welfare contact, parent-only welfare contact, offspring-only welfare contact, or intergenerational welfare contact. Main Outcomes and Measures: Hospitalization rates and cumulative incidence were estimated by age, hospitalization cause, and welfare contact group. Results: A total of 94 358 offspring (48 589 [51.5%] male) and 143 814 parents were included in analyses. The study population included 32 969 offspring (34.9%) who experienced intergenerational welfare contact. These individuals were more socioeconomically disadvantaged at birth and had the highest hospitalization rate (133.5 hospitalizations per 1000 person-years) compared with individuals with no welfare contact (46.1 hospitalizations per 1000 person-years), individuals with parent-only welfare contact (75.0 hospitalizations per 1000 person-years), and individuals with offspring-only welfare contact (87.6 hospitalizations per 1000 person-years). Hospitalizations were frequently related to injury, mental health, and pregnancy. For example, the proportion of individuals with intergenerational welfare contact who had experienced at least 1 hospitalization at ages 16 to 20 years was highest for injury (9.0% [95% CI, 8.7%-9.3%]). Conclusions and Relevance: In this population-based cohort study, individuals who experienced intergenerational welfare contact represented one-third of the population aged 11 to 20 years. Compared with individuals with parent-only welfare contact, individuals with intergenerational welfare contact were more disadvantaged at birth and had 78% higher hospitalization rates from age 11 to 20 years, accounting for more than half of all hospitalizations. Frequent hospitalization causes were injuries, mental health, and pregnancy. This study provides the policy-relevant estimate for what it could mean to break cycles of disadvantage for reducing hospital burden.

Characteristics of paediatric frequent presenters at emergency departments: A whole-of-population study.

AIM: To quantify the frequency of emergency department (ED) presentations and profile the socio-demographic, health and presentation characteristics of paediatric ED frequent presenters. METHODS: A population-based data linkage study of 55 921 children in the South Australian Early Childhood Data Project aged 0-12 years with 100 976 presentations to public hospital EDs in South Australia. For each child, the total number of recurrent ED presentations during a 364-day period post-index presentation was calculated. Frequent presenters were children who experienced ≥4 recurrent ED presentations. We determined the socio-demographic, health and presentation characteristics by number of recurrent presentations. RESULTS: Children with ≥4 recurrent presentations (4.4%) accounted for 15.4% of all paediatric ED presentations and 22.5% of subsequent admissions to hospital during the 12-month study period. Compared to children with no recurrent ED presentation, frequent presenters had higher proportions of socio-economic and health disadvantage at birth. One in two (49.3%) frequent presenters had at least one injury presentation and one (21.3%) in five had at least one presentation related to a chronic condition. CONCLUSIONS: Children with ≥4 presentations do not represent the majority of ED users. Nevertheless, they represent a disproportionate burden accounting for 15% of all paediatric ED presentations in a 12-month period. Frequent presenters were characterised by early socio-economic and health disadvantage, and childhood injury. Strategies targeting social disadvantage and childhood injury may reduce the burden of ED presentations.

Effect of maternal smoking during pregnancy on childhood type 1 diabetes: a whole-of-population study.

AIMS/HYPOTHESIS: Evidence of an association between maternal smoking during pregnancy (prenatal smoking) and childhood type 1 diabetes is mixed. Previous studies have been small and potentially biased due to unmeasured confounding. The objectives of this study were to estimate the association between prenatal smoking and childhood type 1 diabetes, assess residual confounding with a negative control design and an E-value analysis, and summarise published effect estimates from a meta-analysis. METHODS: This whole-of-population study (births from 1999 to 2013, participants aged ≤15 years) used de-identified linked administrative data from the South Australian Early Childhood Data Project. Type 1 diabetes was diagnosed in 557 children (ICD, tenth edition, Australian Modification [ICD-10-AM] codes: E10, E101-E109) during hospitalisation (2001-2014). Families not given financial assistance for school fees was a negative control outcome. Adjusted Cox proportional HRs were calculated. Analyses were conducted on complete-case (n = 264,542, type 1 diabetes = 442) and imputed (n = 286,058, type 1 diabetes = 557) data. A random-effects meta-analysis was used to summarise the effects of prenatal smoking on type 1 diabetes. RESULTS: Compared with non-smokers, children exposed to maternal smoking only in the first or second half of pregnancy had a 6% higher type 1 diabetes incidence (adjusted HR 1.06 [95% CI 0.73, 1.55]). Type 1 diabetes incidence was 24% lower (adjusted HR 0.76 [95% CI 0.58, 0.99]) among children exposed to consistent prenatal smoking, and 16% lower for exposure to any maternal smoking in pregnancy (adjusted HR 0.84 [95% CI 0.67, 1.08]), compared with the unexposed group. Meta-analytic estimates showed 28-29% lower risk of type 1 diabetes among children exposed to prenatal smoking compared with those not exposed. The negative control outcome analysis indicated residual confounding in the prenatal smoking and type 1 diabetes association. E-value analysis indicated that unmeasured confounding associated with prenatal smoking and childhood type 1 diabetes, with a HR of 1.67, could negate the observed effect. CONCLUSIONS/INTERPRETATION: Our best estimate from the study is that maternal smoking in pregnancy was associated with 16% lower childhood type 1 diabetes incidence, and some of this effect was due to residual confounding.

Potentially preventable hospitalisations in children: a comparison of definitions.

OBJECTIVE: To compare admission rate, cumulative incidence and social distribution of potentially preventable hospitalisations (PPHs) among children according to the current Australian adult definition, and the child definition developed in New Zealand. DESIGN, SETTING, PARTICIPANTS: Deidentified, linked public hospital, births registry and perinatal data of children aged 0-10 years born 2002-2012 in South Australia (n=1 91 742). MAIN OUTCOME MEASURES: PPH admission rates among 0-10 year olds and cumulative incidence by age 5 under the adult and child definitions. Cumulative incidence was assessed across indicators of social and health disadvantage. RESULTS: PPH admission rates among 0-10 year olds were 25.6 (95% CI 25.3 to 25.9) and 59.9 (95% CI 59.5 to 60.4) per 1000 person-years for the adult and child definitions, respectively. Greater absolute differences in admission rates between definitions were observed at younger ages (age <1 difference: 75.6 per 1000 person-years; age 10 difference: 1.4 per 1000 person-years). Cumulative incidence of PPHs among 0-5 year olds was higher under the child (25.0%, 95% CI 24.7 to 25.2) than the adult definition (12.8%, 95% CI 12.6 to 13.0). Higher PPH incidence was associated with social and health disadvantage. Approximately 80% of the difference in admission rate between definitions was due to five conditions. CONCLUSIONS: Respiratory conditions and gastroenteritis were key contributors to the higher PPH admission rate and cumulative incidence among children when calculated under the child definition compared to the adult definition. Irrespective of definition, higher PPH cumulative incidence was associated with social and health disadvantage at birth.