Aims: The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). Methods: This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC. Results: The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively. Conclusion: When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates. Cite this article: Bone Joint J 2022;104-B(9):1060–1066.

Objectives Multimorbidity is common in the older population, but the impact of combinations of chronic conditions on disability and quality of life (QoL) is not well known. This analysis explores the effect of specific combinations of chronic diseases on disability, QoL and self-rated health (SRH). Design We used data from two population representative cross-sectional studies, the Northern Ireland Health and Social Wellbeing Survey (NIHSWS) 2005 and the Survey of Lifestyle, Attitudes and Nutrition (SLAN) 2007 (conducted in the Republic of Ireland). Setting Randomly selected community-living participants were interviewed at home. Participants A total of 6159 participants aged 50 years and older were included in the analysis. Outcome measures Chronic conditions were classified as cardiovascular disease, chronic pain, diabetes or respiratory disease. Interaction terms estimated by logistic regression were used to examine the effects of multiple chronic conditions on disability, SRH and QoL. Results Each chronic condition group was correlated with each of the others after adjusting for sociodemographic factors. Those from Northern Ireland were more likely to report a limitation in daily activities (45%) compared to those from the Republic of Ireland (21%). Each condition had an independent effect on disability, SRH and QoL, and those with multiple chronic conditions reported the worst outcomes. However, there were no statistically significant positive interactions between chronic condition groups with respect to any outcome. Conclusions Chronic conditions affect individuals largely independent of each other with respect to their effect on disability, SRH and QoL. However, a significant proportion of the population aged 50 years and over across the island of Ireland lives with multimorbidity, and this group is at the highest risk of disability, poor SRH and poor QoL.

In recent decades, there has been a shift to later childbearing in high-income countries. There is limited large-scale evidence of the relationship between maternal age and child outcomes beyond the perinatal period. The objective of this study is to quantify a child’s risk of developmental vulnerability at age five, according to their mother’s age at childbirth. Linkage of population-level perinatal, hospital, and birth registration datasets to data from the Australian Early Development Census (AEDC) and school enrolments in Australia’s most populous state, New South Wales (NSW), enabled us to follow a cohort of 99,530 children from birth to their first year of school in 2009 or 2012. The study outcome was teacher-reported child development on five domains measured by the AEDC, including physical health and well-being, emotional maturity, social competence, language and cognitive skills, and communication skills and general knowledge. Developmental vulnerability was defined as domain scores below the 2009 AEDC 10th percentile cut point. The mean maternal age at childbirth was 29.6 years (standard deviation [SD], 5.7), with 4,382 children (4.4%) born to mothers aged <20 years and 20,026 children (20.1%) born to mothers aged ≥35 years. The proportion vulnerable on ≥1 domains was 21% overall and followed a reverse J-shaped distribution according to maternal age: it was highest in children born to mothers aged ≤15 years, at 40% (95% CI, 32–49), and was lowest in children born to mothers aged between 30 years and ≤35 years, at 17%–18%. For maternal ages 36 years to ≥45 years, the proportion vulnerable on ≥1 domains increased to 17%–24%. Adjustment for sociodemographic characteristics significantly attenuated vulnerability risk in children born to younger mothers, while adjustment for potentially modifiable factors, such as antenatal visits, had little additional impact across all ages. Although the multi-agency linkage yielded a broad range of sociodemographic, perinatal, health, and developmental variables at the child’s birth and school entry, the study was necessarily limited to variables available in the source data, which were mostly recorded for administrative purposes. Increasing maternal age was associated with a lesser risk of developmental vulnerability for children born to mothers aged 15 years to about 30 years. In contrast, increasing maternal age beyond 35 years was generally associated with increasing vulnerability, broadly equivalent to the risk for children born to mothers in their early twenties, which is highly relevant in the international context of later childbearing. That socioeconomic disadvantage explained approximately half of the increased risk of developmental vulnerability associated with younger motherhood suggests there may be scope to improve population-level child development through policies and programs that support disadvantaged mothers and children.

Background: Preterm birth and developmental vulnerability are more common in Australian Aboriginal compared with non‐Aboriginal children. We quantified how gestational age relates to developmental vulnerability in both populations.Methods: Perinatal datasets were linked to the Australian Early Development Census (AEDC), which collects data on five domains, including physical, social, emotional, language/cognitive, and general knowledge/communication development. We quantified the risk of developmental vulnerability on ≥1 domains at age 5, according to gestational age and Aboriginality, for 97 989 children born in New South Wales, Australia, who started school in 2009 or 2012.Results: Seven thousand and seventy‐nine children (7%) were Aboriginal. Compared with non‐Aboriginal children, Aboriginal children were more likely to be preterm (5% vs. 9%), and developmentally vulnerable on ≥1 domains (20% vs. 36%). Overall, the proportion of developmentally vulnerable children decreased with increasing gestational age, from 44% at ≤27 weeks to 20% at 40 weeks. Aboriginal children had higher risks than non‐Aboriginal children across the gestational age range, peaking among early term children (risk difference [RD] 19.0, 95% confidence interval [CI] 16.3, 21.7; relative risk [RR] 1.91, 95% CI 1.77, 2.06). The relation of gestational age to developmental outcomes was the same in Aboriginal and non‐Aboriginal children, and adjustment for socio‐economic disadvantage attenuated the risk differences and risk ratios across the gestational age range.Conclusions: Although the relation of gestational age to developmental vulnerability was similar in Aboriginal and non‐Aboriginal children, Aboriginal children had a higher risk of developmental vulnerability at all gestational ages, which was largely accounted for by socio‐economic disadvantage.

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym’s synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.