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OBJECTIVE: To assess the risk of severe childhood infections within families, we conducted a sibling analysis in a population-based cohort study with genealogical linkage. We investigated the sibling risk of hospitalization with common infections, a marker of severity. We hypothesized that having siblings hospitalized for infection would increase the proband's risk of admission with infection. STUDY DESIGN: We used population data on Western Australian live-born singletons and their siblings between 1980 and 2014. Measures of infection were infection-related hospitalizations from discharge diagnostic codes. Exposure was having a sibling who had an infection-related hospitalization. Outcomes were infection-related hospitalizations in the child/proband. Probands were followed until an infection-related hospitalization admission (up to the first three), death, 18th birthday, or end of 2014, whichever occurred first. Infection risks were estimated by adjusted Cox proportional hazard models for multiple events. RESULTS: Of 512,279 probands, 142,915 (27.9%) had infection-related hospitalizations; 133,322 (26.0%) had a sibling with a previous infection-related hospitalization (i.e. exposed). Median interval between sibling and proband infection-related hospitalizations was 1.4 years (inter-quartile range 0.5-3.7). Probands had a dose-dependent increase in risk if sibling/s had 1, 2, or 3+ infection-related hospitalizations (adjusted hazard ratio, aHR 1.41, 95% CI 1.39-1.43; aHR 1.65, 1.61-1.69; aHR 1.83, 1.77-1.90, respectively). Among siblings with the same clinical infection type, highest sibling risks were for genitourinary (aHR 2.06, 1.68-2.53), gastrointestinal (aHR 2.07, 1.94-2.19), and skin/soft tissue infections (aHR 2.34, 2.15-2.54). Overall risk of infection-related hospitalization was higher in children with more siblings and with older siblings. CONCLUSION: In this population-based study, we observed an increased risk of infection-related hospitalization in chi

Objective: To investigate the effect of interpregnancy interval (IPI) on preterm birth (PTB) according to whether the previous birth was preterm or term. Design: Cohort study. Setting: USA (California), Australia, Finland, Norway (1980–2017). Population: Women who gave birth to first and second (n = 3 213 855) singleton livebirths. Methods: Odds ratios (ORs) for PTB according to IPIs were modelled using logistic regression with prognostic score stratification for potential confounders. Within-site ORs were pooled by random effects meta-analysis. Outcome measure: PTB (gestational age <37 weeks). Results: Absolute risk of PTB for each IPI was 3–6% after a previous term birth and 17–22% after previous PTB. ORs for PTB differed between previous term and preterm births in all countries (P-for-interaction ≤ 0.001). For women with a previous term birth, pooled ORs were increased for IPI <6 months (OR 1.50, 95% CI 1.43–1.58); 6–11 months (OR 1.10, 95% CI 1.04–1.16); 24–59 months (OR 1.16, 95% CI 1.13–1.18); and ≥ 60 months (OR 1.72, 95%CI 1.60–1.86), compared with 18–23 months. For previous PTB, ORs were increased for <6 months (OR 1.30, 95% CI 1.18–1.42) and ≥60 months (OR 1.29, 95% CI 1.17–1.42), but were less than ORs among women with a previous term birth (P < 0.05). Conclusions: Associations between IPI and PTB are modified by whether or not the previous pregnancy was preterm. ORs for short and long IPIs were higher among women with a previous term birth than a previous PTB, which for short IPI is consistent with the maternal depletion hypothesis. Given the high risk of recurrence and assuming a causal association between IPI and PTB, IPI remains a potentially modifiable risk factor for women with previous PTB. Tweetable abstract: Short versus long interpregnancy intervals associated with higher ORs for preterm birth (PTB) after a previous PTB.

Asbestos exposure is associated with many adverse health conditions including malignant mesothelioma and lung cancer as well as production of autoantibodies. Autoantibodies may serve as biomarkers for asbestos exposure in patients with cancer, and autoimmune dysfunction has been linked to increased rates of various cancers. The aim of this study was to examine the hypothesis that autoantibodies are more frequent in asbestos-exposed individuals with either lung cancer or mesothelioma than those without these conditions. Asbestos-exposed individuals from Western Australia who had lung cancer (n = 24), malignant mesothelioma (n = 24), or no malignancy (n = 51) were tested for antinuclear autoantibodies (ANA) using indirect immunofluorescence and specific extractable nuclear autoantibodies (ENA) employing a multiplexed addressable laser bead immunoassay. Contrary to the hypothesis, data demonstrated that individuals without malignancy were more likely to be positive for ANA compared to those with cancer. However, autoantibodies to histone and Ro-60 were found to be associated with lung cancer. These results support a possible predictive value for specific autoantibodies in the early detection of lung cancer and/or in our understanding of the role of autoimmune processes in cancer. However, further studies are needed to identify specific target antigens for the antibodies.

Background: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data. Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000–2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations. Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available. Conclusion: This paper is the first to show that not only misclassification of non-rheumatic VHD but also of ARF as RHD yields substantial FPRs. Both sources of bias can be successfully addressed with the proposed model which provides an effective solution for reliable RHD case ascertainment from hospital data for epidemiological disease monitoring and policy evaluation.

BACKGROUND: The proportion of births via cesarean section (CS) varies worldwide and in many countries exceeds WHO-recommended rates. Long-term health outcomes for children born by CS are poorly understood, but limited data suggest that CS is associated with increased infection-related hospitalisation. We investigated the relationship between mode of birth and childhood infection-related hospitalisation in high-income countries with varying CS rates. METHODS AND FINDINGS: We conducted a multicountry population-based cohort study of all recorded singleton live births from January 1, 1996 to December 31, 2015 using record-linked birth and hospitalisation data from Denmark, Scotland, England, and Australia (New South Wales and Western Australia). Birth years within the date range varied by site, but data were available from at least 2001 to 2010 for each site. Mode of birth was categorised as vaginal or CS (emergency/elective). Infection-related hospitalisations (overall and by clinical type) occurring after the birth-related discharge date were identified in children until 5 years of age by primary/secondary International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes. Analysis used Cox regression models, adjusting for maternal factors, birth parameters, and socioeconomic status, with results pooled using meta-analysis. In total, 7,174,787 live recorded births were included. Of these, 1,681,966 (23%, range by jurisdiction 17%-29%) were by CS, of which 727,755 (43%, range 38%-57%) were elective. A total of 1,502,537 offspring (21%) had at least 1 infection-related hospitalisation. Compared to vaginally born children, risk of infection was greater among CS-born children (hazard ratio (HR) from random effects model, HR 1.10, 95% confidence interval (CI) 1.09-1.12, p < 0.001). The risk was higher following both elective (HR 1.13, 95% CI 1.12-1.13, p < 0.001) and emergency CS (HR 1.09, 95% CI 1.06-1.12, p < 0.001). Increased risks persisted to 5 years an

D Bond-Smith,1 R Seth,1 N de Klerk,1,2 L Nedkoff,1 M Anderson,3 J Hung,1 J Cannon,1,2 K Griffiths,4,5 JM Katzenellenbogen1,2 1School of Population and Global Health, The University of Western Australia, Perth, Australia; 2Telethon Kids Institute, Perth, Australia; 3Queensland Health, Brisbane, Australia; 4Centre for Big Data Research, The University of New South Wales, Sydney, Australia; 5Menzies School of Health Research, Charles Darwin University, Darwin, AustraliaCorrespondence: D Bond-Smith Email daniela.bond-smith@uwa.edu.auBackground: Previous research has raised substantial concerns regarding the validity of the International Statistical Classification of Diseases and Related Health Problems (ICD) codes (ICD-10 I05–I09) for rheumatic heart disease (RHD) due to likely misclassification of non-rheumatic valvular disease (non-rheumatic VHD) as RHD. There is currently no validated, quantitative approach for reliable case ascertainment of RHD in administrative hospital data.Methods: A comprehensive dataset of validated Australian RHD cases was compiled and linked to inpatient hospital records with an RHD ICD code (2000– 2018, n=7555). A prediction model was developed based on a generalized linear mixed model structure considering an extensive range of demographic and clinical variables. It was validated internally using randomly selected cross-validation samples and externally. Conditional optimal probability cutpoints were calculated, maximising discrimination separately for high-risk versus low-risk populations.Results: The proposed model reduced the false-positive rate (FPR) from acute rheumatic fever (ARF) cases misclassified as RHD from 0.59 to 0.27; similarly for non-rheumatic VHD from 0.77 to 0.22. Overall, the model achieved strong discriminant capacity (AUC: 0.93) and maintained a similar robust performance during external validation (AUC: 0.88). It can also be used when only basic demographic and diagnosis data are available.Conclusio